Date: 2019-12-25 22:22:42 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 16187 rows and 121 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)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 16187 121
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, 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:kmeans | 2 | 1.000 | 0.969 | 0.988 | ** | |
| SD:skmeans | 3 | 1.000 | 0.969 | 0.987 | ** | 2 |
| SD:pam | 2 | 1.000 | 0.984 | 0.993 | ** | |
| CV:NMF | 2 | 1.000 | 0.983 | 0.992 | ** | |
| MAD:kmeans | 2 | 1.000 | 0.977 | 0.991 | ** | |
| ATC:kmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| ATC:pam | 4 | 1.000 | 0.962 | 0.987 | ** | 2 |
| ATC:hclust | 2 | 0.980 | 0.972 | 0.986 | ** | |
| CV:mclust | 2 | 0.947 | 0.951 | 0.979 | * | |
| ATC:mclust | 4 | 0.938 | 0.913 | 0.964 | * | |
| ATC:skmeans | 6 | 0.933 | 0.847 | 0.937 | * | 2,3,5 |
| CV:kmeans | 4 | 0.920 | 0.945 | 0.960 | * | |
| MAD:skmeans | 4 | 0.916 | 0.880 | 0.941 | * | 2,3 |
| MAD:pam | 6 | 0.914 | 0.894 | 0.938 | * | 2 |
| SD:NMF | 2 | 0.882 | 0.929 | 0.968 | ||
| MAD:NMF | 2 | 0.868 | 0.941 | 0.973 | ||
| CV:pam | 4 | 0.850 | 0.865 | 0.940 | ||
| CV:skmeans | 3 | 0.827 | 0.886 | 0.951 | ||
| SD:mclust | 4 | 0.786 | 0.852 | 0.913 | ||
| SD:hclust | 3 | 0.776 | 0.859 | 0.910 | ||
| ATC:NMF | 2 | 0.669 | 0.826 | 0.928 | ||
| CV:hclust | 3 | 0.494 | 0.697 | 0.845 | ||
| MAD:hclust | 3 | 0.478 | 0.727 | 0.828 | ||
| MAD:mclust | 2 | 0.289 | 0.720 | 0.836 |
**: 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 0.882 0.929 0.968 0.460 0.538 0.538
#> CV:NMF 2 1.000 0.983 0.992 0.487 0.514 0.514
#> MAD:NMF 2 0.868 0.941 0.973 0.471 0.529 0.529
#> ATC:NMF 2 0.669 0.826 0.928 0.470 0.533 0.533
#> SD:skmeans 2 1.000 0.959 0.984 0.456 0.548 0.548
#> CV:skmeans 2 0.660 0.866 0.938 0.504 0.496 0.496
#> MAD:skmeans 2 0.983 0.941 0.978 0.470 0.533 0.533
#> ATC:skmeans 2 1.000 0.994 0.997 0.464 0.538 0.538
#> SD:mclust 2 0.636 0.847 0.907 0.479 0.498 0.498
#> CV:mclust 2 0.947 0.951 0.979 0.320 0.690 0.690
#> MAD:mclust 2 0.289 0.720 0.836 0.427 0.496 0.496
#> ATC:mclust 2 0.417 0.679 0.814 0.280 0.890 0.890
#> SD:kmeans 2 1.000 0.969 0.988 0.418 0.579 0.579
#> CV:kmeans 2 0.595 0.816 0.911 0.493 0.496 0.496
#> MAD:kmeans 2 1.000 0.977 0.991 0.420 0.579 0.579
#> ATC:kmeans 2 1.000 1.000 1.000 0.441 0.560 0.560
#> SD:pam 2 1.000 0.984 0.993 0.436 0.566 0.566
#> CV:pam 2 0.428 0.788 0.892 0.475 0.498 0.498
#> MAD:pam 2 1.000 0.984 0.993 0.439 0.560 0.560
#> ATC:pam 2 1.000 0.991 0.997 0.445 0.554 0.554
#> SD:hclust 2 0.836 0.929 0.963 0.322 0.700 0.700
#> CV:hclust 2 0.466 0.749 0.827 0.380 0.711 0.711
#> MAD:hclust 2 0.899 0.918 0.966 0.360 0.660 0.660
#> ATC:hclust 2 0.980 0.972 0.986 0.440 0.554 0.554
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.495 0.679 0.805 0.368 0.735 0.547
#> CV:NMF 3 0.488 0.552 0.722 0.343 0.757 0.553
#> MAD:NMF 3 0.476 0.581 0.809 0.324 0.742 0.564
#> ATC:NMF 3 0.710 0.803 0.911 0.398 0.717 0.510
#> SD:skmeans 3 1.000 0.969 0.987 0.429 0.785 0.613
#> CV:skmeans 3 0.827 0.886 0.951 0.295 0.725 0.509
#> MAD:skmeans 3 0.989 0.965 0.985 0.381 0.799 0.630
#> ATC:skmeans 3 1.000 0.984 0.994 0.285 0.870 0.758
#> SD:mclust 3 0.370 0.664 0.792 0.113 0.940 0.880
#> CV:mclust 3 0.741 0.836 0.918 0.976 0.657 0.507
#> MAD:mclust 3 0.216 0.312 0.653 0.267 0.638 0.481
#> ATC:mclust 3 0.795 0.906 0.944 0.952 0.592 0.542
#> SD:kmeans 3 0.557 0.712 0.859 0.474 0.736 0.572
#> CV:kmeans 3 0.510 0.676 0.782 0.308 0.680 0.454
#> MAD:kmeans 3 0.584 0.735 0.858 0.543 0.742 0.562
#> ATC:kmeans 3 0.672 0.896 0.920 0.459 0.706 0.508
#> SD:pam 3 0.588 0.831 0.850 0.479 0.731 0.538
#> CV:pam 3 0.579 0.685 0.785 0.382 0.708 0.484
#> MAD:pam 3 0.813 0.764 0.912 0.520 0.759 0.574
#> ATC:pam 3 0.889 0.915 0.964 0.416 0.803 0.649
#> SD:hclust 3 0.776 0.859 0.910 0.591 0.817 0.739
#> CV:hclust 3 0.494 0.697 0.845 0.491 0.722 0.609
#> MAD:hclust 3 0.478 0.727 0.828 0.572 0.747 0.625
#> ATC:hclust 3 0.766 0.898 0.891 0.220 0.947 0.904
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.583 0.638 0.779 0.1575 0.846 0.602
#> CV:NMF 4 0.527 0.551 0.753 0.1336 0.735 0.380
#> MAD:NMF 4 0.575 0.595 0.752 0.1774 0.796 0.521
#> ATC:NMF 4 0.604 0.685 0.834 0.1239 0.811 0.518
#> SD:skmeans 4 0.814 0.730 0.861 0.1356 0.883 0.674
#> CV:skmeans 4 0.841 0.884 0.945 0.1244 0.905 0.736
#> MAD:skmeans 4 0.916 0.880 0.941 0.1321 0.898 0.720
#> ATC:skmeans 4 0.850 0.784 0.889 0.1511 0.912 0.788
#> SD:mclust 4 0.786 0.852 0.913 0.2994 0.752 0.495
#> CV:mclust 4 0.653 0.566 0.778 0.1434 0.901 0.728
#> MAD:mclust 4 0.332 0.582 0.717 0.2395 0.681 0.456
#> ATC:mclust 4 0.938 0.913 0.964 0.3085 0.689 0.421
#> SD:kmeans 4 0.609 0.716 0.836 0.1703 0.811 0.564
#> CV:kmeans 4 0.920 0.945 0.960 0.1417 0.889 0.696
#> MAD:kmeans 4 0.608 0.639 0.787 0.1328 0.819 0.527
#> ATC:kmeans 4 0.749 0.663 0.777 0.1410 0.868 0.635
#> SD:pam 4 0.743 0.816 0.915 0.0822 0.968 0.903
#> CV:pam 4 0.850 0.865 0.940 0.1170 0.834 0.575
#> MAD:pam 4 0.735 0.725 0.850 0.0699 0.850 0.615
#> ATC:pam 4 1.000 0.962 0.987 0.0876 0.929 0.813
#> SD:hclust 4 0.510 0.631 0.751 0.2181 0.933 0.872
#> CV:hclust 4 0.492 0.623 0.776 0.1609 0.865 0.696
#> MAD:hclust 4 0.573 0.625 0.806 0.2040 0.901 0.779
#> ATC:hclust 4 0.699 0.833 0.881 0.2945 0.799 0.599
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.786 0.820 0.892 0.0690 0.931 0.745
#> CV:NMF 5 0.661 0.614 0.788 0.0690 0.817 0.438
#> MAD:NMF 5 0.794 0.807 0.894 0.0657 0.877 0.585
#> ATC:NMF 5 0.605 0.628 0.780 0.0773 0.853 0.511
#> SD:skmeans 5 0.816 0.809 0.863 0.0501 0.959 0.843
#> CV:skmeans 5 0.794 0.751 0.881 0.0496 0.954 0.836
#> MAD:skmeans 5 0.894 0.876 0.915 0.0492 0.952 0.828
#> ATC:skmeans 5 0.928 0.939 0.970 0.0509 0.917 0.763
#> SD:mclust 5 0.633 0.671 0.781 0.0829 0.937 0.799
#> CV:mclust 5 0.619 0.578 0.717 0.0498 0.829 0.503
#> MAD:mclust 5 0.494 0.549 0.733 0.0828 0.897 0.711
#> ATC:mclust 5 0.875 0.900 0.912 0.0447 0.952 0.838
#> SD:kmeans 5 0.645 0.643 0.772 0.0845 0.869 0.580
#> CV:kmeans 5 0.788 0.708 0.845 0.0625 0.997 0.987
#> MAD:kmeans 5 0.684 0.728 0.815 0.0747 0.928 0.724
#> ATC:kmeans 5 0.688 0.768 0.813 0.0729 0.900 0.637
#> SD:pam 5 0.775 0.794 0.900 0.1181 0.812 0.465
#> CV:pam 5 0.795 0.816 0.892 0.0566 0.915 0.705
#> MAD:pam 5 0.760 0.713 0.879 0.0818 0.848 0.546
#> ATC:pam 5 0.883 0.866 0.942 0.1392 0.833 0.527
#> SD:hclust 5 0.550 0.593 0.749 0.1369 0.811 0.600
#> CV:hclust 5 0.574 0.662 0.747 0.0987 0.852 0.590
#> MAD:hclust 5 0.594 0.575 0.743 0.0647 0.965 0.906
#> ATC:hclust 5 0.832 0.803 0.911 0.1413 0.903 0.677
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.727 0.646 0.812 0.0529 0.872 0.514
#> CV:NMF 6 0.779 0.741 0.865 0.0463 0.877 0.506
#> MAD:NMF 6 0.765 0.664 0.830 0.0505 0.874 0.517
#> ATC:NMF 6 0.598 0.476 0.676 0.0386 0.899 0.569
#> SD:skmeans 6 0.821 0.781 0.863 0.0368 0.972 0.880
#> CV:skmeans 6 0.795 0.666 0.802 0.0436 0.916 0.671
#> MAD:skmeans 6 0.856 0.796 0.885 0.0391 0.940 0.755
#> ATC:skmeans 6 0.933 0.847 0.937 0.0297 0.986 0.949
#> SD:mclust 6 0.623 0.613 0.711 0.0333 0.903 0.683
#> CV:mclust 6 0.650 0.556 0.733 0.0431 0.880 0.562
#> MAD:mclust 6 0.537 0.442 0.659 0.0469 0.898 0.670
#> ATC:mclust 6 0.838 0.821 0.865 0.0430 0.985 0.944
#> SD:kmeans 6 0.692 0.564 0.728 0.0490 0.941 0.738
#> CV:kmeans 6 0.768 0.637 0.745 0.0437 0.889 0.595
#> MAD:kmeans 6 0.749 0.666 0.755 0.0429 0.927 0.670
#> ATC:kmeans 6 0.758 0.667 0.782 0.0450 0.967 0.840
#> SD:pam 6 0.808 0.843 0.911 0.0474 0.921 0.670
#> CV:pam 6 0.836 0.812 0.902 0.0355 0.963 0.837
#> MAD:pam 6 0.914 0.894 0.938 0.0578 0.929 0.698
#> ATC:pam 6 0.873 0.826 0.891 0.0415 0.944 0.757
#> SD:hclust 6 0.570 0.572 0.721 0.0485 0.970 0.895
#> CV:hclust 6 0.693 0.710 0.781 0.0546 0.947 0.800
#> MAD:hclust 6 0.625 0.595 0.715 0.0557 0.852 0.584
#> ATC:hclust 6 0.806 0.777 0.852 0.0372 0.947 0.761
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)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.836 0.929 0.963 0.3223 0.700 0.700
#> 3 3 0.776 0.859 0.910 0.5911 0.817 0.739
#> 4 4 0.510 0.631 0.751 0.2181 0.933 0.872
#> 5 5 0.550 0.593 0.749 0.1369 0.811 0.600
#> 6 6 0.570 0.572 0.721 0.0485 0.970 0.895
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
#> DRR006374 2 0.2423 0.966 0.040 0.960
#> DRR006375 1 0.0000 0.960 1.000 0.000
#> DRR006376 1 0.0000 0.960 1.000 0.000
#> DRR006377 1 0.0000 0.960 1.000 0.000
#> DRR006378 1 0.8207 0.692 0.744 0.256
#> DRR006379 1 0.0000 0.960 1.000 0.000
#> DRR006380 1 0.8386 0.674 0.732 0.268
#> DRR006381 1 0.0000 0.960 1.000 0.000
#> DRR006382 1 0.8555 0.653 0.720 0.280
#> DRR006383 1 0.8555 0.653 0.720 0.280
#> DRR006384 2 0.2778 0.965 0.048 0.952
#> DRR006385 1 0.0000 0.960 1.000 0.000
#> DRR006386 2 0.0000 0.956 0.000 1.000
#> DRR006387 1 0.0000 0.960 1.000 0.000
#> DRR006388 1 0.0000 0.960 1.000 0.000
#> DRR006389 1 0.0000 0.960 1.000 0.000
#> DRR006390 2 0.4022 0.950 0.080 0.920
#> DRR006391 2 0.4022 0.950 0.080 0.920
#> DRR006392 1 0.0000 0.960 1.000 0.000
#> DRR006393 1 0.0000 0.960 1.000 0.000
#> DRR006394 1 0.6247 0.819 0.844 0.156
#> DRR006395 1 0.0000 0.960 1.000 0.000
#> DRR006396 1 0.0000 0.960 1.000 0.000
#> DRR006397 1 0.0000 0.960 1.000 0.000
#> DRR006398 1 0.0000 0.960 1.000 0.000
#> DRR006399 1 0.0000 0.960 1.000 0.000
#> DRR006400 1 0.0000 0.960 1.000 0.000
#> DRR006401 2 0.2423 0.966 0.040 0.960
#> DRR006402 2 0.2423 0.966 0.040 0.960
#> DRR006403 1 0.0000 0.960 1.000 0.000
#> DRR006404 1 0.0000 0.960 1.000 0.000
#> DRR006405 1 0.0000 0.960 1.000 0.000
#> DRR006406 1 0.0000 0.960 1.000 0.000
#> DRR006407 1 0.0000 0.960 1.000 0.000
#> DRR006408 1 0.6712 0.799 0.824 0.176
#> DRR006409 1 0.0000 0.960 1.000 0.000
#> DRR006410 1 0.0000 0.960 1.000 0.000
#> DRR006411 1 0.0000 0.960 1.000 0.000
#> DRR006412 2 0.4022 0.950 0.080 0.920
#> DRR006413 1 0.0000 0.960 1.000 0.000
#> DRR006414 1 0.0376 0.958 0.996 0.004
#> DRR006415 1 0.0376 0.958 0.996 0.004
#> DRR006416 1 0.0000 0.960 1.000 0.000
#> DRR006417 1 0.0000 0.960 1.000 0.000
#> DRR006418 1 0.0000 0.960 1.000 0.000
#> DRR006419 1 0.0000 0.960 1.000 0.000
#> DRR006420 1 0.0000 0.960 1.000 0.000
#> DRR006421 1 0.0000 0.960 1.000 0.000
#> DRR006422 1 0.8327 0.679 0.736 0.264
#> DRR006423 2 0.4022 0.950 0.080 0.920
#> DRR006424 1 0.0000 0.960 1.000 0.000
#> DRR006425 1 0.8327 0.679 0.736 0.264
#> DRR006426 1 0.0000 0.960 1.000 0.000
#> DRR006427 2 0.0000 0.956 0.000 1.000
#> DRR006428 1 0.0000 0.960 1.000 0.000
#> DRR006429 1 0.6438 0.812 0.836 0.164
#> DRR006430 1 0.0000 0.960 1.000 0.000
#> DRR006431 1 0.0000 0.960 1.000 0.000
#> DRR006432 1 0.0000 0.960 1.000 0.000
#> DRR006433 1 0.0000 0.960 1.000 0.000
#> DRR006434 2 0.3431 0.958 0.064 0.936
#> DRR006435 2 0.0000 0.956 0.000 1.000
#> DRR006436 2 0.0000 0.956 0.000 1.000
#> DRR006437 1 0.0000 0.960 1.000 0.000
#> DRR006438 1 0.0000 0.960 1.000 0.000
#> DRR006439 1 0.0000 0.960 1.000 0.000
#> DRR006440 1 0.7883 0.721 0.764 0.236
#> DRR006441 1 0.7219 0.769 0.800 0.200
#> DRR006442 1 0.0000 0.960 1.000 0.000
#> DRR006443 1 0.7883 0.721 0.764 0.236
#> DRR006444 2 0.0000 0.956 0.000 1.000
#> DRR006445 1 0.0000 0.960 1.000 0.000
#> DRR006446 2 0.4022 0.950 0.080 0.920
#> DRR006447 1 0.0000 0.960 1.000 0.000
#> DRR006448 1 0.0000 0.960 1.000 0.000
#> DRR006449 1 0.0000 0.960 1.000 0.000
#> DRR006450 1 0.0000 0.960 1.000 0.000
#> DRR006451 1 0.0000 0.960 1.000 0.000
#> DRR006452 1 0.0000 0.960 1.000 0.000
#> DRR006453 1 0.0000 0.960 1.000 0.000
#> DRR006454 1 0.0000 0.960 1.000 0.000
#> DRR006455 2 0.0000 0.956 0.000 1.000
#> DRR006456 1 0.0376 0.958 0.996 0.004
#> DRR006457 1 0.0000 0.960 1.000 0.000
#> DRR006458 1 0.0000 0.960 1.000 0.000
#> DRR006459 1 0.0000 0.960 1.000 0.000
#> DRR006460 2 0.2423 0.966 0.040 0.960
#> DRR006461 2 0.3431 0.958 0.064 0.936
#> DRR006462 1 0.0000 0.960 1.000 0.000
#> DRR006463 1 0.7883 0.721 0.764 0.236
#> DRR006464 1 0.5294 0.856 0.880 0.120
#> DRR006465 1 0.0000 0.960 1.000 0.000
#> DRR006466 1 0.0000 0.960 1.000 0.000
#> DRR006467 1 0.0000 0.960 1.000 0.000
#> DRR006468 2 0.0000 0.956 0.000 1.000
#> DRR006469 1 0.7219 0.769 0.800 0.200
#> DRR006470 1 0.0000 0.960 1.000 0.000
#> DRR006471 1 0.0000 0.960 1.000 0.000
#> DRR006472 1 0.0000 0.960 1.000 0.000
#> DRR006473 2 0.4022 0.950 0.080 0.920
#> DRR006474 2 0.2603 0.966 0.044 0.956
#> DRR006475 1 0.0000 0.960 1.000 0.000
#> DRR006476 1 0.3431 0.910 0.936 0.064
#> DRR006477 1 0.0938 0.952 0.988 0.012
#> DRR006478 1 0.0000 0.960 1.000 0.000
#> DRR006479 1 0.0000 0.960 1.000 0.000
#> DRR006480 1 0.0000 0.960 1.000 0.000
#> DRR006481 1 0.0000 0.960 1.000 0.000
#> DRR006482 1 0.0000 0.960 1.000 0.000
#> DRR006483 1 0.0000 0.960 1.000 0.000
#> DRR006484 1 0.0000 0.960 1.000 0.000
#> DRR006485 1 0.7883 0.721 0.764 0.236
#> DRR006486 1 0.0000 0.960 1.000 0.000
#> DRR006487 1 0.0376 0.958 0.996 0.004
#> DRR006488 2 0.0000 0.956 0.000 1.000
#> DRR006489 1 0.0000 0.960 1.000 0.000
#> DRR006490 1 0.0000 0.960 1.000 0.000
#> DRR006491 1 0.0000 0.960 1.000 0.000
#> DRR006492 1 0.0000 0.960 1.000 0.000
#> DRR006493 1 0.0376 0.958 0.996 0.004
#> DRR006494 1 0.0000 0.960 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.1860 0.9542 0.000 0.948 0.052
#> DRR006375 1 0.0592 0.9256 0.988 0.000 0.012
#> DRR006376 1 0.1289 0.9242 0.968 0.000 0.032
#> DRR006377 1 0.4002 0.8260 0.840 0.000 0.160
#> DRR006378 3 0.4527 0.8558 0.052 0.088 0.860
#> DRR006379 1 0.3116 0.8793 0.892 0.000 0.108
#> DRR006380 3 0.5416 0.8413 0.080 0.100 0.820
#> DRR006381 1 0.0237 0.9252 0.996 0.000 0.004
#> DRR006382 3 0.3193 0.8071 0.004 0.100 0.896
#> DRR006383 3 0.3193 0.8071 0.004 0.100 0.896
#> DRR006384 2 0.2448 0.9491 0.000 0.924 0.076
#> DRR006385 1 0.1411 0.9236 0.964 0.000 0.036
#> DRR006386 2 0.0000 0.9433 0.000 1.000 0.000
#> DRR006387 1 0.1031 0.9249 0.976 0.000 0.024
#> DRR006388 1 0.1643 0.9226 0.956 0.000 0.044
#> DRR006389 1 0.1643 0.9226 0.956 0.000 0.044
#> DRR006390 2 0.3116 0.9343 0.000 0.892 0.108
#> DRR006391 2 0.3116 0.9343 0.000 0.892 0.108
#> DRR006392 1 0.0237 0.9252 0.996 0.000 0.004
#> DRR006393 1 0.1031 0.9249 0.976 0.000 0.024
#> DRR006394 3 0.4733 0.7512 0.196 0.004 0.800
#> DRR006395 1 0.1529 0.9228 0.960 0.000 0.040
#> DRR006396 1 0.1163 0.9238 0.972 0.000 0.028
#> DRR006397 1 0.1643 0.9226 0.956 0.000 0.044
#> DRR006398 1 0.1643 0.9226 0.956 0.000 0.044
#> DRR006399 1 0.3116 0.8793 0.892 0.000 0.108
#> DRR006400 1 0.3116 0.8793 0.892 0.000 0.108
#> DRR006401 2 0.1860 0.9542 0.000 0.948 0.052
#> DRR006402 2 0.1860 0.9542 0.000 0.948 0.052
#> DRR006403 1 0.3116 0.8793 0.892 0.000 0.108
#> DRR006404 1 0.3038 0.8820 0.896 0.000 0.104
#> DRR006405 1 0.1753 0.9185 0.952 0.000 0.048
#> DRR006406 1 0.1753 0.9185 0.952 0.000 0.048
#> DRR006407 1 0.4002 0.8260 0.840 0.000 0.160
#> DRR006408 3 0.3454 0.8203 0.104 0.008 0.888
#> DRR006409 1 0.0237 0.9252 0.996 0.000 0.004
#> DRR006410 1 0.1031 0.9249 0.976 0.000 0.024
#> DRR006411 1 0.1289 0.9239 0.968 0.000 0.032
#> DRR006412 2 0.3116 0.9343 0.000 0.892 0.108
#> DRR006413 1 0.0424 0.9258 0.992 0.000 0.008
#> DRR006414 1 0.6302 0.0161 0.520 0.000 0.480
#> DRR006415 1 0.6302 0.0161 0.520 0.000 0.480
#> DRR006416 1 0.1753 0.9185 0.952 0.000 0.048
#> DRR006417 1 0.0892 0.9225 0.980 0.000 0.020
#> DRR006418 1 0.0592 0.9261 0.988 0.000 0.012
#> DRR006419 1 0.0237 0.9245 0.996 0.000 0.004
#> DRR006420 1 0.0237 0.9245 0.996 0.000 0.004
#> DRR006421 1 0.1753 0.9166 0.952 0.000 0.048
#> DRR006422 3 0.4505 0.8534 0.048 0.092 0.860
#> DRR006423 2 0.3116 0.9343 0.000 0.892 0.108
#> DRR006424 1 0.0892 0.9260 0.980 0.000 0.020
#> DRR006425 3 0.4505 0.8534 0.048 0.092 0.860
#> DRR006426 1 0.0892 0.9225 0.980 0.000 0.020
#> DRR006427 2 0.0000 0.9433 0.000 1.000 0.000
#> DRR006428 1 0.0424 0.9244 0.992 0.000 0.008
#> DRR006429 3 0.5826 0.7293 0.204 0.032 0.764
#> DRR006430 1 0.0237 0.9252 0.996 0.000 0.004
#> DRR006431 1 0.0237 0.9252 0.996 0.000 0.004
#> DRR006432 1 0.0892 0.9225 0.980 0.000 0.020
#> DRR006433 1 0.1753 0.9166 0.952 0.000 0.048
#> DRR006434 2 0.2448 0.9449 0.000 0.924 0.076
#> DRR006435 2 0.0000 0.9433 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.9433 0.000 1.000 0.000
#> DRR006437 1 0.1411 0.9236 0.964 0.000 0.036
#> DRR006438 1 0.0237 0.9245 0.996 0.000 0.004
#> DRR006439 1 0.1753 0.9166 0.952 0.000 0.048
#> DRR006440 3 0.3764 0.8488 0.040 0.068 0.892
#> DRR006441 3 0.4007 0.8523 0.084 0.036 0.880
#> DRR006442 1 0.2537 0.8736 0.920 0.000 0.080
#> DRR006443 3 0.3764 0.8488 0.040 0.068 0.892
#> DRR006444 2 0.0000 0.9433 0.000 1.000 0.000
#> DRR006445 1 0.0892 0.9256 0.980 0.000 0.020
#> DRR006446 2 0.3116 0.9343 0.000 0.892 0.108
#> DRR006447 1 0.0592 0.9261 0.988 0.000 0.012
#> DRR006448 1 0.3192 0.8766 0.888 0.000 0.112
#> DRR006449 1 0.0892 0.9256 0.980 0.000 0.020
#> DRR006450 1 0.0892 0.9256 0.980 0.000 0.020
#> DRR006451 1 0.3116 0.8793 0.892 0.000 0.108
#> DRR006452 1 0.0747 0.9259 0.984 0.000 0.016
#> DRR006453 1 0.1860 0.9170 0.948 0.000 0.052
#> DRR006454 1 0.1643 0.9226 0.956 0.000 0.044
#> DRR006455 2 0.0000 0.9433 0.000 1.000 0.000
#> DRR006456 1 0.6302 0.0161 0.520 0.000 0.480
#> DRR006457 1 0.1643 0.9183 0.956 0.000 0.044
#> DRR006458 1 0.0237 0.9252 0.996 0.000 0.004
#> DRR006459 1 0.0237 0.9252 0.996 0.000 0.004
#> DRR006460 2 0.1860 0.9542 0.000 0.948 0.052
#> DRR006461 2 0.2448 0.9449 0.000 0.924 0.076
#> DRR006462 1 0.1163 0.9238 0.972 0.000 0.028
#> DRR006463 3 0.3764 0.8488 0.040 0.068 0.892
#> DRR006464 3 0.5678 0.5817 0.316 0.000 0.684
#> DRR006465 1 0.0747 0.9262 0.984 0.000 0.016
#> DRR006466 1 0.2711 0.8870 0.912 0.000 0.088
#> DRR006467 1 0.0237 0.9252 0.996 0.000 0.004
#> DRR006468 2 0.0000 0.9433 0.000 1.000 0.000
#> DRR006469 3 0.4007 0.8523 0.084 0.036 0.880
#> DRR006470 1 0.0892 0.9225 0.980 0.000 0.020
#> DRR006471 1 0.0237 0.9245 0.996 0.000 0.004
#> DRR006472 1 0.3816 0.8285 0.852 0.000 0.148
#> DRR006473 2 0.3116 0.9343 0.000 0.892 0.108
#> DRR006474 2 0.2066 0.9532 0.000 0.940 0.060
#> DRR006475 1 0.0237 0.9245 0.996 0.000 0.004
#> DRR006476 1 0.7386 0.0675 0.508 0.032 0.460
#> DRR006477 1 0.5706 0.5629 0.680 0.000 0.320
#> DRR006478 1 0.1031 0.9249 0.976 0.000 0.024
#> DRR006479 1 0.0237 0.9245 0.996 0.000 0.004
#> DRR006480 1 0.0237 0.9252 0.996 0.000 0.004
#> DRR006481 1 0.1163 0.9194 0.972 0.000 0.028
#> DRR006482 1 0.1411 0.9236 0.964 0.000 0.036
#> DRR006483 1 0.0237 0.9245 0.996 0.000 0.004
#> DRR006484 1 0.0237 0.9245 0.996 0.000 0.004
#> DRR006485 3 0.3764 0.8488 0.040 0.068 0.892
#> DRR006486 1 0.0237 0.9245 0.996 0.000 0.004
#> DRR006487 1 0.6302 0.0161 0.520 0.000 0.480
#> DRR006488 2 0.0000 0.9433 0.000 1.000 0.000
#> DRR006489 1 0.0237 0.9252 0.996 0.000 0.004
#> DRR006490 1 0.0424 0.9244 0.992 0.000 0.008
#> DRR006491 1 0.2537 0.8736 0.920 0.000 0.080
#> DRR006492 1 0.1529 0.9228 0.960 0.000 0.040
#> DRR006493 1 0.6302 0.0161 0.520 0.000 0.480
#> DRR006494 1 0.0237 0.9252 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.1302 0.9325 0.000 0.956 0.044 0.000
#> DRR006375 1 0.1211 0.8039 0.960 0.000 0.000 0.040
#> DRR006376 1 0.3172 0.7961 0.840 0.000 0.000 0.160
#> DRR006377 1 0.6107 0.7086 0.648 0.000 0.088 0.264
#> DRR006378 3 0.7076 -0.7611 0.012 0.088 0.496 0.404
#> DRR006379 1 0.3801 0.6898 0.780 0.000 0.000 0.220
#> DRR006380 4 0.7010 0.7269 0.004 0.100 0.444 0.452
#> DRR006381 1 0.0469 0.8031 0.988 0.000 0.000 0.012
#> DRR006382 3 0.2563 0.0105 0.000 0.072 0.908 0.020
#> DRR006383 3 0.2563 0.0105 0.000 0.072 0.908 0.020
#> DRR006384 2 0.2124 0.9248 0.000 0.924 0.068 0.008
#> DRR006385 1 0.4164 0.7683 0.736 0.000 0.000 0.264
#> DRR006386 2 0.1576 0.9116 0.000 0.948 0.004 0.048
#> DRR006387 1 0.1637 0.7932 0.940 0.000 0.000 0.060
#> DRR006388 1 0.4222 0.7648 0.728 0.000 0.000 0.272
#> DRR006389 1 0.4222 0.7648 0.728 0.000 0.000 0.272
#> DRR006390 2 0.2843 0.9110 0.000 0.892 0.088 0.020
#> DRR006391 2 0.2843 0.9110 0.000 0.892 0.088 0.020
#> DRR006392 1 0.0469 0.8029 0.988 0.000 0.000 0.012
#> DRR006393 1 0.1867 0.8019 0.928 0.000 0.000 0.072
#> DRR006394 3 0.7385 -0.4405 0.140 0.004 0.428 0.428
#> DRR006395 1 0.3142 0.7945 0.860 0.000 0.008 0.132
#> DRR006396 1 0.2011 0.7928 0.920 0.000 0.000 0.080
#> DRR006397 1 0.4222 0.7648 0.728 0.000 0.000 0.272
#> DRR006398 1 0.4222 0.7648 0.728 0.000 0.000 0.272
#> DRR006399 1 0.3801 0.6898 0.780 0.000 0.000 0.220
#> DRR006400 1 0.3801 0.6898 0.780 0.000 0.000 0.220
#> DRR006401 2 0.1302 0.9325 0.000 0.956 0.044 0.000
#> DRR006402 2 0.1302 0.9325 0.000 0.956 0.044 0.000
#> DRR006403 1 0.3801 0.6898 0.780 0.000 0.000 0.220
#> DRR006404 1 0.4008 0.7315 0.756 0.000 0.000 0.244
#> DRR006405 1 0.3528 0.7965 0.808 0.000 0.000 0.192
#> DRR006406 1 0.3528 0.7965 0.808 0.000 0.000 0.192
#> DRR006407 1 0.6107 0.7086 0.648 0.000 0.088 0.264
#> DRR006408 4 0.6429 0.7360 0.012 0.048 0.380 0.560
#> DRR006409 1 0.0336 0.8034 0.992 0.000 0.000 0.008
#> DRR006410 1 0.1637 0.7932 0.940 0.000 0.000 0.060
#> DRR006411 1 0.4964 0.6829 0.616 0.000 0.004 0.380
#> DRR006412 2 0.2843 0.9110 0.000 0.892 0.088 0.020
#> DRR006413 1 0.0592 0.8034 0.984 0.000 0.000 0.016
#> DRR006414 3 0.7148 0.3165 0.140 0.000 0.496 0.364
#> DRR006415 3 0.7148 0.3165 0.140 0.000 0.496 0.364
#> DRR006416 1 0.3688 0.7925 0.792 0.000 0.000 0.208
#> DRR006417 1 0.4905 0.6758 0.632 0.000 0.004 0.364
#> DRR006418 1 0.3610 0.7782 0.800 0.000 0.000 0.200
#> DRR006419 1 0.4564 0.7056 0.672 0.000 0.000 0.328
#> DRR006420 1 0.4564 0.7056 0.672 0.000 0.000 0.328
#> DRR006421 1 0.5630 0.6755 0.608 0.000 0.032 0.360
#> DRR006422 3 0.7012 -0.7566 0.008 0.092 0.496 0.404
#> DRR006423 2 0.2843 0.9110 0.000 0.892 0.088 0.020
#> DRR006424 1 0.0817 0.8010 0.976 0.000 0.000 0.024
#> DRR006425 3 0.7012 -0.7566 0.008 0.092 0.496 0.404
#> DRR006426 1 0.4905 0.6758 0.632 0.000 0.004 0.364
#> DRR006427 2 0.1576 0.9116 0.000 0.948 0.004 0.048
#> DRR006428 1 0.5069 0.6979 0.664 0.000 0.016 0.320
#> DRR006429 3 0.8145 -0.4450 0.128 0.044 0.440 0.388
#> DRR006430 1 0.0469 0.8029 0.988 0.000 0.000 0.012
#> DRR006431 1 0.0336 0.8034 0.992 0.000 0.000 0.008
#> DRR006432 1 0.4905 0.6758 0.632 0.000 0.004 0.364
#> DRR006433 1 0.5630 0.6755 0.608 0.000 0.032 0.360
#> DRR006434 2 0.1792 0.9266 0.000 0.932 0.068 0.000
#> DRR006435 2 0.1576 0.9116 0.000 0.948 0.004 0.048
#> DRR006436 2 0.1576 0.9116 0.000 0.948 0.004 0.048
#> DRR006437 1 0.3764 0.7861 0.784 0.000 0.000 0.216
#> DRR006438 1 0.4564 0.7056 0.672 0.000 0.000 0.328
#> DRR006439 1 0.5630 0.6755 0.608 0.000 0.032 0.360
#> DRR006440 3 0.0712 0.1094 0.004 0.004 0.984 0.008
#> DRR006441 3 0.7142 -0.7396 0.040 0.048 0.472 0.440
#> DRR006442 1 0.6570 0.5991 0.580 0.000 0.100 0.320
#> DRR006443 3 0.0712 0.1094 0.004 0.004 0.984 0.008
#> DRR006444 2 0.1576 0.9116 0.000 0.948 0.004 0.048
#> DRR006445 1 0.1302 0.8047 0.956 0.000 0.000 0.044
#> DRR006446 2 0.2843 0.9110 0.000 0.892 0.088 0.020
#> DRR006447 1 0.3610 0.7782 0.800 0.000 0.000 0.200
#> DRR006448 1 0.3837 0.6858 0.776 0.000 0.000 0.224
#> DRR006449 1 0.1211 0.7983 0.960 0.000 0.000 0.040
#> DRR006450 1 0.1211 0.7983 0.960 0.000 0.000 0.040
#> DRR006451 1 0.3801 0.6898 0.780 0.000 0.000 0.220
#> DRR006452 1 0.0817 0.8011 0.976 0.000 0.000 0.024
#> DRR006453 1 0.2530 0.8034 0.888 0.000 0.000 0.112
#> DRR006454 1 0.4222 0.7648 0.728 0.000 0.000 0.272
#> DRR006455 2 0.1576 0.9116 0.000 0.948 0.004 0.048
#> DRR006456 3 0.7148 0.3165 0.140 0.000 0.496 0.364
#> DRR006457 1 0.5543 0.6794 0.612 0.000 0.028 0.360
#> DRR006458 1 0.0336 0.8034 0.992 0.000 0.000 0.008
#> DRR006459 1 0.0336 0.8034 0.992 0.000 0.000 0.008
#> DRR006460 2 0.1302 0.9325 0.000 0.956 0.044 0.000
#> DRR006461 2 0.1792 0.9266 0.000 0.932 0.068 0.000
#> DRR006462 1 0.2011 0.7928 0.920 0.000 0.000 0.080
#> DRR006463 3 0.0712 0.1094 0.004 0.004 0.984 0.008
#> DRR006464 3 0.7731 -0.1964 0.228 0.000 0.396 0.376
#> DRR006465 1 0.1211 0.8078 0.960 0.000 0.000 0.040
#> DRR006466 1 0.6058 0.6916 0.632 0.000 0.072 0.296
#> DRR006467 1 0.0469 0.8029 0.988 0.000 0.000 0.012
#> DRR006468 2 0.1576 0.9116 0.000 0.948 0.004 0.048
#> DRR006469 3 0.7142 -0.7396 0.040 0.048 0.472 0.440
#> DRR006470 1 0.4905 0.6758 0.632 0.000 0.004 0.364
#> DRR006471 1 0.2011 0.8034 0.920 0.000 0.000 0.080
#> DRR006472 1 0.6386 0.6874 0.640 0.000 0.124 0.236
#> DRR006473 2 0.2843 0.9110 0.000 0.892 0.088 0.020
#> DRR006474 2 0.1661 0.9310 0.000 0.944 0.052 0.004
#> DRR006475 1 0.2011 0.8034 0.920 0.000 0.000 0.080
#> DRR006476 1 0.8879 -0.0220 0.396 0.052 0.284 0.268
#> DRR006477 1 0.7958 0.4424 0.560 0.044 0.204 0.192
#> DRR006478 1 0.1716 0.8040 0.936 0.000 0.000 0.064
#> DRR006479 1 0.4564 0.7056 0.672 0.000 0.000 0.328
#> DRR006480 1 0.0336 0.8034 0.992 0.000 0.000 0.008
#> DRR006481 1 0.5400 0.6566 0.608 0.000 0.020 0.372
#> DRR006482 1 0.4164 0.7683 0.736 0.000 0.000 0.264
#> DRR006483 1 0.2011 0.8034 0.920 0.000 0.000 0.080
#> DRR006484 1 0.4955 0.6888 0.648 0.000 0.008 0.344
#> DRR006485 3 0.0712 0.1094 0.004 0.004 0.984 0.008
#> DRR006486 1 0.2081 0.8027 0.916 0.000 0.000 0.084
#> DRR006487 3 0.7148 0.3165 0.140 0.000 0.496 0.364
#> DRR006488 2 0.1576 0.9116 0.000 0.948 0.004 0.048
#> DRR006489 1 0.0469 0.8029 0.988 0.000 0.000 0.012
#> DRR006490 1 0.5069 0.6979 0.664 0.000 0.016 0.320
#> DRR006491 1 0.6570 0.5991 0.580 0.000 0.100 0.320
#> DRR006492 1 0.3142 0.7945 0.860 0.000 0.008 0.132
#> DRR006493 3 0.7148 0.3165 0.140 0.000 0.496 0.364
#> DRR006494 1 0.0336 0.8034 0.992 0.000 0.000 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.0290 0.9163 0.000 0.992 0.008 0.000 0.000
#> DRR006375 1 0.2233 0.6984 0.892 0.000 0.000 0.104 0.004
#> DRR006376 1 0.3934 0.5323 0.716 0.000 0.000 0.276 0.008
#> DRR006377 4 0.6088 0.5600 0.304 0.000 0.012 0.572 0.112
#> DRR006378 5 0.6510 0.7546 0.016 0.112 0.296 0.012 0.564
#> DRR006379 1 0.4404 0.5556 0.760 0.000 0.000 0.152 0.088
#> DRR006380 5 0.3413 0.6027 0.000 0.124 0.044 0.000 0.832
#> DRR006381 1 0.1121 0.7063 0.956 0.000 0.000 0.044 0.000
#> DRR006382 3 0.3246 0.3464 0.000 0.096 0.860 0.020 0.024
#> DRR006383 3 0.3246 0.3464 0.000 0.096 0.860 0.020 0.024
#> DRR006384 2 0.1469 0.9066 0.000 0.948 0.036 0.000 0.016
#> DRR006385 4 0.4278 0.4257 0.452 0.000 0.000 0.548 0.000
#> DRR006386 2 0.3011 0.8873 0.000 0.876 0.036 0.012 0.076
#> DRR006387 1 0.1121 0.6905 0.956 0.000 0.000 0.044 0.000
#> DRR006388 4 0.4283 0.4665 0.456 0.000 0.000 0.544 0.000
#> DRR006389 4 0.4283 0.4665 0.456 0.000 0.000 0.544 0.000
#> DRR006390 2 0.2074 0.8936 0.000 0.920 0.036 0.000 0.044
#> DRR006391 2 0.2074 0.8936 0.000 0.920 0.036 0.000 0.044
#> DRR006392 1 0.0880 0.7060 0.968 0.000 0.000 0.032 0.000
#> DRR006393 1 0.2127 0.6707 0.892 0.000 0.000 0.108 0.000
#> DRR006394 5 0.7062 0.6445 0.120 0.004 0.268 0.064 0.544
#> DRR006395 1 0.4313 0.4525 0.716 0.000 0.008 0.260 0.016
#> DRR006396 1 0.2230 0.6770 0.884 0.000 0.000 0.116 0.000
#> DRR006397 4 0.4283 0.4665 0.456 0.000 0.000 0.544 0.000
#> DRR006398 4 0.4283 0.4665 0.456 0.000 0.000 0.544 0.000
#> DRR006399 1 0.4404 0.5556 0.760 0.000 0.000 0.152 0.088
#> DRR006400 1 0.4404 0.5556 0.760 0.000 0.000 0.152 0.088
#> DRR006401 2 0.0290 0.9163 0.000 0.992 0.008 0.000 0.000
#> DRR006402 2 0.0290 0.9163 0.000 0.992 0.008 0.000 0.000
#> DRR006403 1 0.4404 0.5556 0.760 0.000 0.000 0.152 0.088
#> DRR006404 1 0.5067 0.4539 0.648 0.000 0.000 0.288 0.064
#> DRR006405 1 0.4473 0.4056 0.656 0.000 0.000 0.324 0.020
#> DRR006406 1 0.4473 0.4056 0.656 0.000 0.000 0.324 0.020
#> DRR006407 4 0.6088 0.5600 0.304 0.000 0.012 0.572 0.112
#> DRR006408 5 0.2308 0.5886 0.004 0.048 0.000 0.036 0.912
#> DRR006409 1 0.0963 0.7059 0.964 0.000 0.000 0.036 0.000
#> DRR006410 1 0.1121 0.6905 0.956 0.000 0.000 0.044 0.000
#> DRR006411 4 0.3424 0.6697 0.240 0.000 0.000 0.760 0.000
#> DRR006412 2 0.2074 0.8936 0.000 0.920 0.036 0.000 0.044
#> DRR006413 1 0.1478 0.7046 0.936 0.000 0.000 0.064 0.000
#> DRR006414 3 0.5406 0.4872 0.056 0.000 0.480 0.464 0.000
#> DRR006415 3 0.5406 0.4872 0.056 0.000 0.480 0.464 0.000
#> DRR006416 1 0.4639 0.3191 0.612 0.000 0.000 0.368 0.020
#> DRR006417 4 0.3395 0.6653 0.236 0.000 0.000 0.764 0.000
#> DRR006418 1 0.4138 0.2625 0.616 0.000 0.000 0.384 0.000
#> DRR006419 1 0.4273 0.0891 0.552 0.000 0.000 0.448 0.000
#> DRR006420 1 0.4273 0.0891 0.552 0.000 0.000 0.448 0.000
#> DRR006421 4 0.3656 0.6630 0.196 0.000 0.020 0.784 0.000
#> DRR006422 5 0.6573 0.7515 0.016 0.120 0.292 0.012 0.560
#> DRR006423 2 0.2074 0.8936 0.000 0.920 0.036 0.000 0.044
#> DRR006424 1 0.1043 0.7039 0.960 0.000 0.000 0.040 0.000
#> DRR006425 5 0.6573 0.7515 0.016 0.120 0.292 0.012 0.560
#> DRR006426 4 0.3395 0.6653 0.236 0.000 0.000 0.764 0.000
#> DRR006427 2 0.3011 0.8873 0.000 0.876 0.036 0.012 0.076
#> DRR006428 1 0.4610 0.1465 0.556 0.000 0.012 0.432 0.000
#> DRR006429 5 0.7789 0.6793 0.036 0.044 0.296 0.148 0.476
#> DRR006430 1 0.0794 0.7062 0.972 0.000 0.000 0.028 0.000
#> DRR006431 1 0.1043 0.7049 0.960 0.000 0.000 0.040 0.000
#> DRR006432 4 0.3395 0.6653 0.236 0.000 0.000 0.764 0.000
#> DRR006433 4 0.3656 0.6630 0.196 0.000 0.020 0.784 0.000
#> DRR006434 2 0.0880 0.9114 0.000 0.968 0.032 0.000 0.000
#> DRR006435 2 0.3011 0.8873 0.000 0.876 0.036 0.012 0.076
#> DRR006436 2 0.3011 0.8873 0.000 0.876 0.036 0.012 0.076
#> DRR006437 1 0.4297 -0.2210 0.528 0.000 0.000 0.472 0.000
#> DRR006438 1 0.4283 0.0740 0.544 0.000 0.000 0.456 0.000
#> DRR006439 4 0.3656 0.6630 0.196 0.000 0.020 0.784 0.000
#> DRR006440 3 0.1485 0.4623 0.000 0.032 0.948 0.020 0.000
#> DRR006441 5 0.6442 0.7668 0.032 0.048 0.296 0.032 0.592
#> DRR006442 1 0.5880 0.0476 0.484 0.000 0.100 0.416 0.000
#> DRR006443 3 0.1485 0.4623 0.000 0.032 0.948 0.020 0.000
#> DRR006444 2 0.3011 0.8873 0.000 0.876 0.036 0.012 0.076
#> DRR006445 1 0.1043 0.7071 0.960 0.000 0.000 0.040 0.000
#> DRR006446 2 0.2074 0.8936 0.000 0.920 0.036 0.000 0.044
#> DRR006447 1 0.4138 0.2625 0.616 0.000 0.000 0.384 0.000
#> DRR006448 1 0.4469 0.5503 0.756 0.000 0.000 0.148 0.096
#> DRR006449 1 0.0510 0.6996 0.984 0.000 0.000 0.016 0.000
#> DRR006450 1 0.0510 0.6996 0.984 0.000 0.000 0.016 0.000
#> DRR006451 1 0.4404 0.5556 0.760 0.000 0.000 0.152 0.088
#> DRR006452 1 0.0794 0.7049 0.972 0.000 0.000 0.028 0.000
#> DRR006453 1 0.3476 0.6259 0.804 0.000 0.000 0.176 0.020
#> DRR006454 4 0.4283 0.4665 0.456 0.000 0.000 0.544 0.000
#> DRR006455 2 0.3011 0.8873 0.000 0.876 0.036 0.012 0.076
#> DRR006456 3 0.5406 0.4872 0.056 0.000 0.480 0.464 0.000
#> DRR006457 4 0.3596 0.6665 0.200 0.000 0.016 0.784 0.000
#> DRR006458 1 0.1043 0.7049 0.960 0.000 0.000 0.040 0.000
#> DRR006459 1 0.1043 0.7049 0.960 0.000 0.000 0.040 0.000
#> DRR006460 2 0.0290 0.9163 0.000 0.992 0.008 0.000 0.000
#> DRR006461 2 0.0880 0.9114 0.000 0.968 0.032 0.000 0.000
#> DRR006462 1 0.2230 0.6770 0.884 0.000 0.000 0.116 0.000
#> DRR006463 3 0.1485 0.4623 0.000 0.032 0.948 0.020 0.000
#> DRR006464 5 0.7976 0.5099 0.124 0.000 0.268 0.180 0.428
#> DRR006465 1 0.1908 0.6802 0.908 0.000 0.000 0.092 0.000
#> DRR006466 4 0.5037 0.6568 0.236 0.000 0.032 0.700 0.032
#> DRR006467 1 0.0880 0.7060 0.968 0.000 0.000 0.032 0.000
#> DRR006468 2 0.3011 0.8873 0.000 0.876 0.036 0.012 0.076
#> DRR006469 5 0.6442 0.7668 0.032 0.048 0.296 0.032 0.592
#> DRR006470 4 0.3395 0.6653 0.236 0.000 0.000 0.764 0.000
#> DRR006471 1 0.2648 0.6425 0.848 0.000 0.000 0.152 0.000
#> DRR006472 4 0.6285 0.5901 0.272 0.000 0.072 0.600 0.056
#> DRR006473 2 0.2074 0.8936 0.000 0.920 0.036 0.000 0.044
#> DRR006474 2 0.0898 0.9131 0.000 0.972 0.020 0.000 0.008
#> DRR006475 1 0.2648 0.6425 0.848 0.000 0.000 0.152 0.000
#> DRR006476 4 0.9112 -0.2385 0.144 0.052 0.212 0.364 0.228
#> DRR006477 4 0.8736 0.2333 0.304 0.052 0.076 0.360 0.208
#> DRR006478 1 0.2074 0.6724 0.896 0.000 0.000 0.104 0.000
#> DRR006479 1 0.4283 0.0740 0.544 0.000 0.000 0.456 0.000
#> DRR006480 1 0.1043 0.7049 0.960 0.000 0.000 0.040 0.000
#> DRR006481 4 0.3427 0.6262 0.192 0.000 0.012 0.796 0.000
#> DRR006482 4 0.4278 0.4257 0.452 0.000 0.000 0.548 0.000
#> DRR006483 1 0.2648 0.6425 0.848 0.000 0.000 0.152 0.000
#> DRR006484 1 0.4304 -0.0013 0.516 0.000 0.000 0.484 0.000
#> DRR006485 3 0.1485 0.4623 0.000 0.032 0.948 0.020 0.000
#> DRR006486 1 0.2690 0.6385 0.844 0.000 0.000 0.156 0.000
#> DRR006487 3 0.5406 0.4872 0.056 0.000 0.480 0.464 0.000
#> DRR006488 2 0.3011 0.8873 0.000 0.876 0.036 0.012 0.076
#> DRR006489 1 0.0963 0.7064 0.964 0.000 0.000 0.036 0.000
#> DRR006490 1 0.4604 0.1530 0.560 0.000 0.012 0.428 0.000
#> DRR006491 1 0.5880 0.0476 0.484 0.000 0.100 0.416 0.000
#> DRR006492 1 0.4313 0.4525 0.716 0.000 0.008 0.260 0.016
#> DRR006493 3 0.5406 0.4872 0.056 0.000 0.480 0.464 0.000
#> DRR006494 1 0.1043 0.7049 0.960 0.000 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
#> DRR006374 5 0.0260 0.9128 0.000 0.000 0.000 0.008 0.992 0.000
#> DRR006375 1 0.1958 0.7020 0.896 0.000 0.004 0.000 0.000 0.100
#> DRR006376 1 0.3975 0.3617 0.544 0.000 0.000 0.004 0.000 0.452
#> DRR006377 6 0.3731 0.4883 0.112 0.064 0.004 0.012 0.000 0.808
#> DRR006378 2 0.6062 0.6471 0.000 0.572 0.004 0.268 0.100 0.056
#> DRR006379 1 0.4521 0.4806 0.616 0.028 0.004 0.004 0.000 0.348
#> DRR006380 2 0.4303 0.3492 0.000 0.784 0.020 0.044 0.120 0.032
#> DRR006381 1 0.0717 0.7113 0.976 0.000 0.016 0.000 0.000 0.008
#> DRR006382 4 0.5803 0.4994 0.000 0.028 0.408 0.472 0.092 0.000
#> DRR006383 4 0.5803 0.4994 0.000 0.028 0.408 0.472 0.092 0.000
#> DRR006384 5 0.1418 0.9026 0.000 0.032 0.000 0.024 0.944 0.000
#> DRR006385 6 0.5542 0.3867 0.384 0.000 0.120 0.004 0.000 0.492
#> DRR006386 5 0.2618 0.8879 0.000 0.052 0.000 0.076 0.872 0.000
#> DRR006387 1 0.1411 0.7020 0.936 0.000 0.004 0.000 0.000 0.060
#> DRR006388 6 0.5568 0.4337 0.380 0.000 0.124 0.004 0.000 0.492
#> DRR006389 6 0.5568 0.4337 0.380 0.000 0.124 0.004 0.000 0.492
#> DRR006390 5 0.1913 0.8893 0.000 0.080 0.000 0.012 0.908 0.000
#> DRR006391 5 0.1913 0.8893 0.000 0.080 0.000 0.012 0.908 0.000
#> DRR006392 1 0.0363 0.7110 0.988 0.000 0.012 0.000 0.000 0.000
#> DRR006393 1 0.2773 0.6516 0.836 0.000 0.008 0.004 0.000 0.152
#> DRR006394 2 0.6860 0.5470 0.096 0.504 0.004 0.264 0.004 0.128
#> DRR006395 1 0.4867 0.3664 0.608 0.000 0.068 0.004 0.000 0.320
#> DRR006396 1 0.2402 0.6862 0.856 0.000 0.004 0.000 0.000 0.140
#> DRR006397 6 0.5568 0.4337 0.380 0.000 0.124 0.004 0.000 0.492
#> DRR006398 6 0.5568 0.4337 0.380 0.000 0.124 0.004 0.000 0.492
#> DRR006399 1 0.4521 0.4806 0.616 0.028 0.004 0.004 0.000 0.348
#> DRR006400 1 0.4521 0.4806 0.616 0.028 0.004 0.004 0.000 0.348
#> DRR006401 5 0.0260 0.9128 0.000 0.000 0.000 0.008 0.992 0.000
#> DRR006402 5 0.0260 0.9128 0.000 0.000 0.000 0.008 0.992 0.000
#> DRR006403 1 0.4521 0.4806 0.616 0.028 0.004 0.004 0.000 0.348
#> DRR006404 6 0.4225 -0.3140 0.480 0.008 0.000 0.004 0.000 0.508
#> DRR006405 1 0.4220 0.2582 0.520 0.004 0.008 0.000 0.000 0.468
#> DRR006406 1 0.4220 0.2582 0.520 0.004 0.008 0.000 0.000 0.468
#> DRR006407 6 0.3731 0.4883 0.112 0.064 0.004 0.012 0.000 0.808
#> DRR006408 2 0.3234 0.3562 0.000 0.840 0.020 0.016 0.008 0.116
#> DRR006409 1 0.0603 0.7116 0.980 0.000 0.016 0.000 0.000 0.004
#> DRR006410 1 0.1411 0.7020 0.936 0.000 0.004 0.000 0.000 0.060
#> DRR006411 6 0.5442 0.5428 0.204 0.000 0.220 0.000 0.000 0.576
#> DRR006412 5 0.1913 0.8893 0.000 0.080 0.000 0.012 0.908 0.000
#> DRR006413 1 0.1168 0.7094 0.956 0.000 0.016 0.000 0.000 0.028
#> DRR006414 3 0.0891 0.6450 0.024 0.000 0.968 0.008 0.000 0.000
#> DRR006415 3 0.0891 0.6450 0.024 0.000 0.968 0.008 0.000 0.000
#> DRR006416 1 0.5027 0.3393 0.580 0.004 0.076 0.000 0.000 0.340
#> DRR006417 6 0.5607 0.5288 0.216 0.000 0.240 0.000 0.000 0.544
#> DRR006418 1 0.5012 0.3092 0.600 0.000 0.100 0.000 0.000 0.300
#> DRR006419 1 0.5369 0.2394 0.540 0.000 0.332 0.000 0.000 0.128
#> DRR006420 1 0.5379 0.2339 0.536 0.000 0.336 0.000 0.000 0.128
#> DRR006421 6 0.5664 0.4508 0.120 0.012 0.348 0.000 0.000 0.520
#> DRR006422 2 0.5876 0.6477 0.000 0.580 0.000 0.268 0.100 0.052
#> DRR006423 5 0.1913 0.8893 0.000 0.080 0.000 0.012 0.908 0.000
#> DRR006424 1 0.0935 0.7088 0.964 0.000 0.004 0.000 0.000 0.032
#> DRR006425 2 0.5876 0.6477 0.000 0.580 0.000 0.268 0.100 0.052
#> DRR006426 6 0.5607 0.5288 0.216 0.000 0.240 0.000 0.000 0.544
#> DRR006427 5 0.2618 0.8879 0.000 0.052 0.000 0.076 0.872 0.000
#> DRR006428 1 0.4671 0.1437 0.532 0.000 0.424 0.000 0.000 0.044
#> DRR006429 2 0.6639 0.5748 0.008 0.476 0.004 0.268 0.024 0.220
#> DRR006430 1 0.0260 0.7115 0.992 0.000 0.008 0.000 0.000 0.000
#> DRR006431 1 0.0603 0.7102 0.980 0.000 0.016 0.000 0.000 0.004
#> DRR006432 6 0.5607 0.5288 0.216 0.000 0.240 0.000 0.000 0.544
#> DRR006433 6 0.5664 0.4508 0.120 0.012 0.348 0.000 0.000 0.520
#> DRR006434 5 0.0790 0.9070 0.000 0.000 0.000 0.032 0.968 0.000
#> DRR006435 5 0.2618 0.8879 0.000 0.052 0.000 0.076 0.872 0.000
#> DRR006436 5 0.2618 0.8879 0.000 0.052 0.000 0.076 0.872 0.000
#> DRR006437 1 0.5243 -0.2081 0.460 0.000 0.080 0.004 0.000 0.456
#> DRR006438 1 0.5399 0.2219 0.528 0.000 0.344 0.000 0.000 0.128
#> DRR006439 6 0.5664 0.4508 0.120 0.012 0.348 0.000 0.000 0.520
#> DRR006440 4 0.2312 0.7641 0.000 0.000 0.112 0.876 0.012 0.000
#> DRR006441 2 0.5884 0.6644 0.008 0.592 0.004 0.268 0.028 0.100
#> DRR006442 3 0.4328 0.0304 0.460 0.000 0.520 0.000 0.000 0.020
#> DRR006443 4 0.2312 0.7641 0.000 0.000 0.112 0.876 0.012 0.000
#> DRR006444 5 0.2618 0.8879 0.000 0.052 0.000 0.076 0.872 0.000
#> DRR006445 1 0.1204 0.7118 0.944 0.000 0.000 0.000 0.000 0.056
#> DRR006446 5 0.1913 0.8893 0.000 0.080 0.000 0.012 0.908 0.000
#> DRR006447 1 0.5012 0.3092 0.600 0.000 0.100 0.000 0.000 0.300
#> DRR006448 1 0.4627 0.4796 0.616 0.036 0.004 0.004 0.000 0.340
#> DRR006449 1 0.0865 0.7079 0.964 0.000 0.000 0.000 0.000 0.036
#> DRR006450 1 0.0865 0.7079 0.964 0.000 0.000 0.000 0.000 0.036
#> DRR006451 1 0.4521 0.4806 0.616 0.028 0.004 0.004 0.000 0.348
#> DRR006452 1 0.0692 0.7104 0.976 0.000 0.004 0.000 0.000 0.020
#> DRR006453 1 0.3536 0.5931 0.736 0.004 0.008 0.000 0.000 0.252
#> DRR006454 6 0.5568 0.4337 0.380 0.000 0.124 0.004 0.000 0.492
#> DRR006455 5 0.2618 0.8879 0.000 0.052 0.000 0.076 0.872 0.000
#> DRR006456 3 0.0891 0.6450 0.024 0.000 0.968 0.008 0.000 0.000
#> DRR006457 6 0.5687 0.4552 0.124 0.012 0.344 0.000 0.000 0.520
#> DRR006458 1 0.0603 0.7102 0.980 0.000 0.016 0.000 0.000 0.004
#> DRR006459 1 0.0603 0.7102 0.980 0.000 0.016 0.000 0.000 0.004
#> DRR006460 5 0.0260 0.9128 0.000 0.000 0.000 0.008 0.992 0.000
#> DRR006461 5 0.0790 0.9070 0.000 0.000 0.000 0.032 0.968 0.000
#> DRR006462 1 0.2402 0.6862 0.856 0.000 0.004 0.000 0.000 0.140
#> DRR006463 4 0.2312 0.7641 0.000 0.000 0.112 0.876 0.012 0.000
#> DRR006464 2 0.7321 0.4286 0.096 0.388 0.004 0.264 0.000 0.248
#> DRR006465 1 0.2346 0.6651 0.868 0.000 0.008 0.000 0.000 0.124
#> DRR006466 6 0.5707 0.4608 0.120 0.012 0.264 0.012 0.000 0.592
#> DRR006467 1 0.0363 0.7110 0.988 0.000 0.012 0.000 0.000 0.000
#> DRR006468 5 0.2618 0.8879 0.000 0.052 0.000 0.076 0.872 0.000
#> DRR006469 2 0.5884 0.6644 0.008 0.592 0.004 0.268 0.028 0.100
#> DRR006470 6 0.5607 0.5288 0.216 0.000 0.240 0.000 0.000 0.544
#> DRR006471 1 0.2633 0.6589 0.864 0.000 0.032 0.000 0.000 0.104
#> DRR006472 6 0.5528 0.4251 0.092 0.016 0.172 0.044 0.000 0.676
#> DRR006473 5 0.1913 0.8893 0.000 0.080 0.000 0.012 0.908 0.000
#> DRR006474 5 0.0713 0.9099 0.000 0.028 0.000 0.000 0.972 0.000
#> DRR006475 1 0.2633 0.6589 0.864 0.000 0.032 0.000 0.000 0.104
#> DRR006476 6 0.6777 -0.2823 0.004 0.232 0.032 0.176 0.028 0.528
#> DRR006477 6 0.8067 0.1769 0.140 0.196 0.104 0.068 0.020 0.472
#> DRR006478 1 0.2734 0.6543 0.840 0.000 0.008 0.004 0.000 0.148
#> DRR006479 1 0.5399 0.2219 0.528 0.000 0.344 0.000 0.000 0.128
#> DRR006480 1 0.0603 0.7102 0.980 0.000 0.016 0.000 0.000 0.004
#> DRR006481 6 0.5679 0.3863 0.156 0.000 0.408 0.000 0.000 0.436
#> DRR006482 6 0.5542 0.3867 0.384 0.000 0.120 0.004 0.000 0.492
#> DRR006483 1 0.2633 0.6589 0.864 0.000 0.032 0.000 0.000 0.104
#> DRR006484 1 0.5462 0.1545 0.496 0.000 0.376 0.000 0.000 0.128
#> DRR006485 4 0.2312 0.7641 0.000 0.000 0.112 0.876 0.012 0.000
#> DRR006486 1 0.2706 0.6561 0.860 0.000 0.036 0.000 0.000 0.104
#> DRR006487 3 0.0891 0.6450 0.024 0.000 0.968 0.008 0.000 0.000
#> DRR006488 5 0.2618 0.8879 0.000 0.052 0.000 0.076 0.872 0.000
#> DRR006489 1 0.0508 0.7116 0.984 0.000 0.012 0.000 0.000 0.004
#> DRR006490 1 0.4703 0.1851 0.544 0.000 0.408 0.000 0.000 0.048
#> DRR006491 3 0.4328 0.0304 0.460 0.000 0.520 0.000 0.000 0.020
#> DRR006492 1 0.4867 0.3664 0.608 0.000 0.068 0.004 0.000 0.320
#> DRR006493 3 0.0891 0.6450 0.024 0.000 0.968 0.008 0.000 0.000
#> DRR006494 1 0.0603 0.7102 0.980 0.000 0.016 0.000 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)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.969 0.988 0.4175 0.579 0.579
#> 3 3 0.557 0.712 0.859 0.4740 0.736 0.572
#> 4 4 0.609 0.716 0.836 0.1703 0.811 0.564
#> 5 5 0.645 0.643 0.772 0.0845 0.869 0.580
#> 6 6 0.692 0.564 0.728 0.0490 0.941 0.738
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
#> DRR006374 2 0.0000 0.969 0.000 1.000
#> DRR006375 1 0.0000 0.995 1.000 0.000
#> DRR006376 1 0.0000 0.995 1.000 0.000
#> DRR006377 1 0.0000 0.995 1.000 0.000
#> DRR006378 2 0.0000 0.969 0.000 1.000
#> DRR006379 1 0.0000 0.995 1.000 0.000
#> DRR006380 2 0.0000 0.969 0.000 1.000
#> DRR006381 1 0.0000 0.995 1.000 0.000
#> DRR006382 2 0.0000 0.969 0.000 1.000
#> DRR006383 2 0.9710 0.354 0.400 0.600
#> DRR006384 2 0.0000 0.969 0.000 1.000
#> DRR006385 1 0.0000 0.995 1.000 0.000
#> DRR006386 2 0.0000 0.969 0.000 1.000
#> DRR006387 1 0.0000 0.995 1.000 0.000
#> DRR006388 1 0.0000 0.995 1.000 0.000
#> DRR006389 1 0.0000 0.995 1.000 0.000
#> DRR006390 2 0.0000 0.969 0.000 1.000
#> DRR006391 2 0.0000 0.969 0.000 1.000
#> DRR006392 1 0.0000 0.995 1.000 0.000
#> DRR006393 1 0.0000 0.995 1.000 0.000
#> DRR006394 2 0.9686 0.357 0.396 0.604
#> DRR006395 1 0.0000 0.995 1.000 0.000
#> DRR006396 1 0.0000 0.995 1.000 0.000
#> DRR006397 1 0.0000 0.995 1.000 0.000
#> DRR006398 1 0.0000 0.995 1.000 0.000
#> DRR006399 1 0.0000 0.995 1.000 0.000
#> DRR006400 1 0.0000 0.995 1.000 0.000
#> DRR006401 2 0.0000 0.969 0.000 1.000
#> DRR006402 2 0.0000 0.969 0.000 1.000
#> DRR006403 1 0.0000 0.995 1.000 0.000
#> DRR006404 1 0.0000 0.995 1.000 0.000
#> DRR006405 1 0.0000 0.995 1.000 0.000
#> DRR006406 1 0.0000 0.995 1.000 0.000
#> DRR006407 1 0.0000 0.995 1.000 0.000
#> DRR006408 2 0.7376 0.737 0.208 0.792
#> DRR006409 1 0.0000 0.995 1.000 0.000
#> DRR006410 1 0.0000 0.995 1.000 0.000
#> DRR006411 1 0.0000 0.995 1.000 0.000
#> DRR006412 2 0.0000 0.969 0.000 1.000
#> DRR006413 1 0.0000 0.995 1.000 0.000
#> DRR006414 1 0.0000 0.995 1.000 0.000
#> DRR006415 1 0.0000 0.995 1.000 0.000
#> DRR006416 1 0.0000 0.995 1.000 0.000
#> DRR006417 1 0.0000 0.995 1.000 0.000
#> DRR006418 1 0.0000 0.995 1.000 0.000
#> DRR006419 1 0.0000 0.995 1.000 0.000
#> DRR006420 1 0.0000 0.995 1.000 0.000
#> DRR006421 1 0.0000 0.995 1.000 0.000
#> DRR006422 2 0.3274 0.915 0.060 0.940
#> DRR006423 2 0.0000 0.969 0.000 1.000
#> DRR006424 1 0.0000 0.995 1.000 0.000
#> DRR006425 2 0.0000 0.969 0.000 1.000
#> DRR006426 1 0.0000 0.995 1.000 0.000
#> DRR006427 2 0.0000 0.969 0.000 1.000
#> DRR006428 1 0.0000 0.995 1.000 0.000
#> DRR006429 1 0.9661 0.318 0.608 0.392
#> DRR006430 1 0.0000 0.995 1.000 0.000
#> DRR006431 1 0.0000 0.995 1.000 0.000
#> DRR006432 1 0.0000 0.995 1.000 0.000
#> DRR006433 1 0.0000 0.995 1.000 0.000
#> DRR006434 2 0.0000 0.969 0.000 1.000
#> DRR006435 2 0.0000 0.969 0.000 1.000
#> DRR006436 2 0.0000 0.969 0.000 1.000
#> DRR006437 1 0.0000 0.995 1.000 0.000
#> DRR006438 1 0.0000 0.995 1.000 0.000
#> DRR006439 1 0.0000 0.995 1.000 0.000
#> DRR006440 2 0.0376 0.966 0.004 0.996
#> DRR006441 2 0.0000 0.969 0.000 1.000
#> DRR006442 1 0.0000 0.995 1.000 0.000
#> DRR006443 2 0.0000 0.969 0.000 1.000
#> DRR006444 2 0.0000 0.969 0.000 1.000
#> DRR006445 1 0.0000 0.995 1.000 0.000
#> DRR006446 2 0.0000 0.969 0.000 1.000
#> DRR006447 1 0.0000 0.995 1.000 0.000
#> DRR006448 1 0.0000 0.995 1.000 0.000
#> DRR006449 1 0.0000 0.995 1.000 0.000
#> DRR006450 1 0.0000 0.995 1.000 0.000
#> DRR006451 1 0.0000 0.995 1.000 0.000
#> DRR006452 1 0.0000 0.995 1.000 0.000
#> DRR006453 1 0.0000 0.995 1.000 0.000
#> DRR006454 1 0.0000 0.995 1.000 0.000
#> DRR006455 2 0.0000 0.969 0.000 1.000
#> DRR006456 1 0.0000 0.995 1.000 0.000
#> DRR006457 1 0.0000 0.995 1.000 0.000
#> DRR006458 1 0.0000 0.995 1.000 0.000
#> DRR006459 1 0.0000 0.995 1.000 0.000
#> DRR006460 2 0.0000 0.969 0.000 1.000
#> DRR006461 2 0.0000 0.969 0.000 1.000
#> DRR006462 1 0.0000 0.995 1.000 0.000
#> DRR006463 2 0.0000 0.969 0.000 1.000
#> DRR006464 1 0.0000 0.995 1.000 0.000
#> DRR006465 1 0.0000 0.995 1.000 0.000
#> DRR006466 1 0.0000 0.995 1.000 0.000
#> DRR006467 1 0.0000 0.995 1.000 0.000
#> DRR006468 2 0.0000 0.969 0.000 1.000
#> DRR006469 2 0.0000 0.969 0.000 1.000
#> DRR006470 1 0.0000 0.995 1.000 0.000
#> DRR006471 1 0.0000 0.995 1.000 0.000
#> DRR006472 1 0.0000 0.995 1.000 0.000
#> DRR006473 2 0.0000 0.969 0.000 1.000
#> DRR006474 2 0.0000 0.969 0.000 1.000
#> DRR006475 1 0.0000 0.995 1.000 0.000
#> DRR006476 1 0.1633 0.970 0.976 0.024
#> DRR006477 1 0.0000 0.995 1.000 0.000
#> DRR006478 1 0.0000 0.995 1.000 0.000
#> DRR006479 1 0.0000 0.995 1.000 0.000
#> DRR006480 1 0.0000 0.995 1.000 0.000
#> DRR006481 1 0.0000 0.995 1.000 0.000
#> DRR006482 1 0.0000 0.995 1.000 0.000
#> DRR006483 1 0.0000 0.995 1.000 0.000
#> DRR006484 1 0.0000 0.995 1.000 0.000
#> DRR006485 2 0.0000 0.969 0.000 1.000
#> DRR006486 1 0.0000 0.995 1.000 0.000
#> DRR006487 1 0.0000 0.995 1.000 0.000
#> DRR006488 2 0.0000 0.969 0.000 1.000
#> DRR006489 1 0.0000 0.995 1.000 0.000
#> DRR006490 1 0.0000 0.995 1.000 0.000
#> DRR006491 1 0.0000 0.995 1.000 0.000
#> DRR006492 1 0.0000 0.995 1.000 0.000
#> DRR006493 1 0.0000 0.995 1.000 0.000
#> DRR006494 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
#> DRR006374 2 0.2165 0.9389 0.000 0.936 0.064
#> DRR006375 1 0.0592 0.8461 0.988 0.000 0.012
#> DRR006376 1 0.2711 0.8147 0.912 0.000 0.088
#> DRR006377 3 0.4555 0.6654 0.200 0.000 0.800
#> DRR006378 2 0.5926 0.5345 0.000 0.644 0.356
#> DRR006379 1 0.2796 0.8126 0.908 0.000 0.092
#> DRR006380 3 0.5988 0.2808 0.000 0.368 0.632
#> DRR006381 1 0.0592 0.8461 0.988 0.000 0.012
#> DRR006382 3 0.6204 0.1470 0.000 0.424 0.576
#> DRR006383 3 0.5216 0.4723 0.000 0.260 0.740
#> DRR006384 2 0.1411 0.9382 0.000 0.964 0.036
#> DRR006385 1 0.1289 0.8410 0.968 0.000 0.032
#> DRR006386 2 0.0000 0.9299 0.000 1.000 0.000
#> DRR006387 1 0.1289 0.8410 0.968 0.000 0.032
#> DRR006388 1 0.5760 0.4644 0.672 0.000 0.328
#> DRR006389 1 0.5760 0.4644 0.672 0.000 0.328
#> DRR006390 2 0.2878 0.9336 0.000 0.904 0.096
#> DRR006391 2 0.2878 0.9336 0.000 0.904 0.096
#> DRR006392 1 0.0592 0.8461 0.988 0.000 0.012
#> DRR006393 1 0.0000 0.8460 1.000 0.000 0.000
#> DRR006394 3 0.2383 0.6794 0.016 0.044 0.940
#> DRR006395 1 0.2796 0.8126 0.908 0.000 0.092
#> DRR006396 1 0.1289 0.8410 0.968 0.000 0.032
#> DRR006397 1 0.5760 0.4644 0.672 0.000 0.328
#> DRR006398 1 0.5760 0.4644 0.672 0.000 0.328
#> DRR006399 1 0.2796 0.8126 0.908 0.000 0.092
#> DRR006400 1 0.2796 0.8126 0.908 0.000 0.092
#> DRR006401 2 0.1860 0.9397 0.000 0.948 0.052
#> DRR006402 2 0.1860 0.9397 0.000 0.948 0.052
#> DRR006403 1 0.2796 0.8126 0.908 0.000 0.092
#> DRR006404 1 0.2796 0.8126 0.908 0.000 0.092
#> DRR006405 1 0.2796 0.8126 0.908 0.000 0.092
#> DRR006406 1 0.2796 0.8126 0.908 0.000 0.092
#> DRR006407 3 0.6244 0.2293 0.440 0.000 0.560
#> DRR006408 3 0.7603 0.4449 0.096 0.236 0.668
#> DRR006409 1 0.1031 0.8432 0.976 0.000 0.024
#> DRR006410 1 0.0237 0.8456 0.996 0.000 0.004
#> DRR006411 3 0.6309 0.0356 0.500 0.000 0.500
#> DRR006412 2 0.2878 0.9336 0.000 0.904 0.096
#> DRR006413 1 0.0592 0.8461 0.988 0.000 0.012
#> DRR006414 1 0.5859 0.4799 0.656 0.000 0.344
#> DRR006415 3 0.4399 0.6938 0.188 0.000 0.812
#> DRR006416 1 0.6308 -0.0535 0.508 0.000 0.492
#> DRR006417 3 0.5431 0.5808 0.284 0.000 0.716
#> DRR006418 1 0.2356 0.8196 0.928 0.000 0.072
#> DRR006419 1 0.5733 0.5150 0.676 0.000 0.324
#> DRR006420 1 0.5706 0.5217 0.680 0.000 0.320
#> DRR006421 3 0.4504 0.6568 0.196 0.000 0.804
#> DRR006422 3 0.6294 0.4180 0.020 0.288 0.692
#> DRR006423 2 0.2959 0.9318 0.000 0.900 0.100
#> DRR006424 1 0.0592 0.8461 0.988 0.000 0.012
#> DRR006425 3 0.5988 0.2804 0.000 0.368 0.632
#> DRR006426 3 0.4842 0.6215 0.224 0.000 0.776
#> DRR006427 2 0.0000 0.9299 0.000 1.000 0.000
#> DRR006428 1 0.5760 0.5074 0.672 0.000 0.328
#> DRR006429 3 0.1170 0.6920 0.016 0.008 0.976
#> DRR006430 1 0.0592 0.8461 0.988 0.000 0.012
#> DRR006431 1 0.0592 0.8461 0.988 0.000 0.012
#> DRR006432 3 0.4504 0.6563 0.196 0.000 0.804
#> DRR006433 3 0.3192 0.7192 0.112 0.000 0.888
#> DRR006434 2 0.3116 0.9263 0.000 0.892 0.108
#> DRR006435 2 0.0000 0.9299 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.9299 0.000 1.000 0.000
#> DRR006437 1 0.1289 0.8410 0.968 0.000 0.032
#> DRR006438 1 0.5733 0.5150 0.676 0.000 0.324
#> DRR006439 1 0.5785 0.5015 0.668 0.000 0.332
#> DRR006440 3 0.4235 0.5942 0.000 0.176 0.824
#> DRR006441 3 0.5465 0.4536 0.000 0.288 0.712
#> DRR006442 1 0.5882 0.4722 0.652 0.000 0.348
#> DRR006443 3 0.4235 0.5942 0.000 0.176 0.824
#> DRR006444 2 0.0000 0.9299 0.000 1.000 0.000
#> DRR006445 1 0.1289 0.8410 0.968 0.000 0.032
#> DRR006446 2 0.2878 0.9336 0.000 0.904 0.096
#> DRR006447 1 0.1289 0.8410 0.968 0.000 0.032
#> DRR006448 1 0.2796 0.8126 0.908 0.000 0.092
#> DRR006449 1 0.0237 0.8456 0.996 0.000 0.004
#> DRR006450 1 0.0000 0.8460 1.000 0.000 0.000
#> DRR006451 1 0.2796 0.8126 0.908 0.000 0.092
#> DRR006452 1 0.0000 0.8460 1.000 0.000 0.000
#> DRR006453 1 0.0892 0.8436 0.980 0.000 0.020
#> DRR006454 1 0.6309 -0.0786 0.500 0.000 0.500
#> DRR006455 2 0.0000 0.9299 0.000 1.000 0.000
#> DRR006456 3 0.3551 0.7125 0.132 0.000 0.868
#> DRR006457 3 0.4931 0.6527 0.232 0.000 0.768
#> DRR006458 1 0.0892 0.8443 0.980 0.000 0.020
#> DRR006459 1 0.1031 0.8432 0.976 0.000 0.024
#> DRR006460 2 0.1411 0.9382 0.000 0.964 0.036
#> DRR006461 2 0.3941 0.8810 0.000 0.844 0.156
#> DRR006462 1 0.1289 0.8410 0.968 0.000 0.032
#> DRR006463 3 0.4452 0.5795 0.000 0.192 0.808
#> DRR006464 3 0.2796 0.7243 0.092 0.000 0.908
#> DRR006465 1 0.0000 0.8460 1.000 0.000 0.000
#> DRR006466 3 0.3116 0.7254 0.108 0.000 0.892
#> DRR006467 1 0.0592 0.8461 0.988 0.000 0.012
#> DRR006468 2 0.0000 0.9299 0.000 1.000 0.000
#> DRR006469 3 0.5560 0.4355 0.000 0.300 0.700
#> DRR006470 3 0.6305 0.0181 0.484 0.000 0.516
#> DRR006471 1 0.1031 0.8432 0.976 0.000 0.024
#> DRR006472 3 0.3038 0.7229 0.104 0.000 0.896
#> DRR006473 2 0.2959 0.9318 0.000 0.900 0.100
#> DRR006474 2 0.2959 0.9318 0.000 0.900 0.100
#> DRR006475 1 0.1031 0.8432 0.976 0.000 0.024
#> DRR006476 3 0.1031 0.6951 0.024 0.000 0.976
#> DRR006477 3 0.2261 0.7215 0.068 0.000 0.932
#> DRR006478 1 0.0592 0.8461 0.988 0.000 0.012
#> DRR006479 1 0.3116 0.7911 0.892 0.000 0.108
#> DRR006480 1 0.1031 0.8432 0.976 0.000 0.024
#> DRR006481 3 0.5327 0.6007 0.272 0.000 0.728
#> DRR006482 1 0.4399 0.7143 0.812 0.000 0.188
#> DRR006483 1 0.1031 0.8432 0.976 0.000 0.024
#> DRR006484 1 0.5785 0.5015 0.668 0.000 0.332
#> DRR006485 3 0.4291 0.5908 0.000 0.180 0.820
#> DRR006486 1 0.2878 0.8010 0.904 0.000 0.096
#> DRR006487 3 0.4399 0.6938 0.188 0.000 0.812
#> DRR006488 2 0.0000 0.9299 0.000 1.000 0.000
#> DRR006489 1 0.0592 0.8461 0.988 0.000 0.012
#> DRR006490 1 0.5760 0.5074 0.672 0.000 0.328
#> DRR006491 1 0.5835 0.4874 0.660 0.000 0.340
#> DRR006492 1 0.1643 0.8350 0.956 0.000 0.044
#> DRR006493 3 0.4399 0.6938 0.188 0.000 0.812
#> DRR006494 1 0.1031 0.8432 0.976 0.000 0.024
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.3610 0.8421 0.000 0.800 0.000 0.200
#> DRR006375 1 0.3219 0.7803 0.836 0.000 0.164 0.000
#> DRR006376 1 0.2198 0.8046 0.920 0.000 0.008 0.072
#> DRR006377 4 0.7683 0.0786 0.244 0.000 0.304 0.452
#> DRR006378 4 0.4164 0.4682 0.000 0.264 0.000 0.736
#> DRR006379 1 0.3196 0.7765 0.856 0.000 0.008 0.136
#> DRR006380 4 0.2611 0.7243 0.000 0.096 0.008 0.896
#> DRR006381 1 0.3311 0.7752 0.828 0.000 0.172 0.000
#> DRR006382 4 0.4245 0.6230 0.000 0.196 0.020 0.784
#> DRR006383 4 0.4931 0.7224 0.000 0.092 0.132 0.776
#> DRR006384 2 0.2921 0.8537 0.000 0.860 0.000 0.140
#> DRR006385 1 0.0376 0.8169 0.992 0.000 0.004 0.004
#> DRR006386 2 0.0000 0.8272 0.000 1.000 0.000 0.000
#> DRR006387 1 0.0188 0.8165 0.996 0.000 0.000 0.004
#> DRR006388 1 0.5646 0.6583 0.708 0.000 0.088 0.204
#> DRR006389 1 0.5646 0.6583 0.708 0.000 0.088 0.204
#> DRR006390 2 0.4040 0.8213 0.000 0.752 0.000 0.248
#> DRR006391 2 0.4040 0.8213 0.000 0.752 0.000 0.248
#> DRR006392 1 0.3266 0.7782 0.832 0.000 0.168 0.000
#> DRR006393 1 0.3074 0.7841 0.848 0.000 0.152 0.000
#> DRR006394 4 0.0188 0.7501 0.000 0.000 0.004 0.996
#> DRR006395 1 0.2976 0.7871 0.872 0.000 0.008 0.120
#> DRR006396 1 0.0524 0.8168 0.988 0.000 0.004 0.008
#> DRR006397 1 0.5609 0.6632 0.712 0.000 0.088 0.200
#> DRR006398 1 0.5609 0.6632 0.712 0.000 0.088 0.200
#> DRR006399 1 0.2831 0.7890 0.876 0.000 0.004 0.120
#> DRR006400 1 0.2831 0.7890 0.876 0.000 0.004 0.120
#> DRR006401 2 0.3123 0.8532 0.000 0.844 0.000 0.156
#> DRR006402 2 0.3123 0.8532 0.000 0.844 0.000 0.156
#> DRR006403 1 0.2831 0.7890 0.876 0.000 0.004 0.120
#> DRR006404 1 0.3196 0.7765 0.856 0.000 0.008 0.136
#> DRR006405 1 0.2799 0.7929 0.884 0.000 0.008 0.108
#> DRR006406 1 0.2799 0.7929 0.884 0.000 0.008 0.108
#> DRR006407 1 0.6386 0.3544 0.552 0.000 0.072 0.376
#> DRR006408 4 0.4837 0.5577 0.160 0.056 0.004 0.780
#> DRR006409 1 0.3688 0.7504 0.792 0.000 0.208 0.000
#> DRR006410 1 0.0188 0.8166 0.996 0.000 0.004 0.000
#> DRR006411 1 0.6338 0.4768 0.600 0.000 0.084 0.316
#> DRR006412 2 0.4040 0.8213 0.000 0.752 0.000 0.248
#> DRR006413 1 0.3219 0.7793 0.836 0.000 0.164 0.000
#> DRR006414 3 0.2844 0.7726 0.048 0.000 0.900 0.052
#> DRR006415 3 0.3982 0.6774 0.004 0.000 0.776 0.220
#> DRR006416 1 0.5681 0.6536 0.704 0.000 0.088 0.208
#> DRR006417 3 0.3577 0.7304 0.012 0.000 0.832 0.156
#> DRR006418 3 0.4948 -0.0776 0.440 0.000 0.560 0.000
#> DRR006419 3 0.1637 0.7714 0.060 0.000 0.940 0.000
#> DRR006420 3 0.2281 0.7499 0.096 0.000 0.904 0.000
#> DRR006421 3 0.4011 0.7025 0.008 0.000 0.784 0.208
#> DRR006422 4 0.1792 0.7341 0.000 0.068 0.000 0.932
#> DRR006423 2 0.4134 0.8107 0.000 0.740 0.000 0.260
#> DRR006424 1 0.3219 0.7793 0.836 0.000 0.164 0.000
#> DRR006425 4 0.1867 0.7339 0.000 0.072 0.000 0.928
#> DRR006426 3 0.3910 0.7331 0.024 0.000 0.820 0.156
#> DRR006427 2 0.0000 0.8272 0.000 1.000 0.000 0.000
#> DRR006428 3 0.1389 0.7753 0.048 0.000 0.952 0.000
#> DRR006429 4 0.0707 0.7508 0.000 0.000 0.020 0.980
#> DRR006430 1 0.3356 0.7738 0.824 0.000 0.176 0.000
#> DRR006431 1 0.3400 0.7715 0.820 0.000 0.180 0.000
#> DRR006432 3 0.4139 0.7222 0.024 0.000 0.800 0.176
#> DRR006433 3 0.4018 0.6844 0.004 0.000 0.772 0.224
#> DRR006434 2 0.4837 0.6680 0.000 0.648 0.004 0.348
#> DRR006435 2 0.0000 0.8272 0.000 1.000 0.000 0.000
#> DRR006436 2 0.0000 0.8272 0.000 1.000 0.000 0.000
#> DRR006437 1 0.0376 0.8169 0.992 0.000 0.004 0.004
#> DRR006438 3 0.2081 0.7566 0.084 0.000 0.916 0.000
#> DRR006439 3 0.1389 0.7753 0.048 0.000 0.952 0.000
#> DRR006440 4 0.4356 0.7305 0.000 0.064 0.124 0.812
#> DRR006441 4 0.1637 0.7504 0.000 0.060 0.000 0.940
#> DRR006442 3 0.2759 0.7725 0.044 0.000 0.904 0.052
#> DRR006443 4 0.4374 0.7313 0.000 0.068 0.120 0.812
#> DRR006444 2 0.0000 0.8272 0.000 1.000 0.000 0.000
#> DRR006445 1 0.0376 0.8169 0.992 0.000 0.004 0.004
#> DRR006446 2 0.4008 0.8236 0.000 0.756 0.000 0.244
#> DRR006447 1 0.0524 0.8168 0.988 0.000 0.008 0.004
#> DRR006448 1 0.2831 0.7890 0.876 0.000 0.004 0.120
#> DRR006449 1 0.0188 0.8166 0.996 0.000 0.004 0.000
#> DRR006450 1 0.0921 0.8150 0.972 0.000 0.028 0.000
#> DRR006451 1 0.3032 0.7845 0.868 0.000 0.008 0.124
#> DRR006452 1 0.0921 0.8150 0.972 0.000 0.028 0.000
#> DRR006453 1 0.0188 0.8166 0.996 0.000 0.004 0.000
#> DRR006454 1 0.6338 0.4768 0.600 0.000 0.084 0.316
#> DRR006455 2 0.0000 0.8272 0.000 1.000 0.000 0.000
#> DRR006456 3 0.3801 0.6787 0.000 0.000 0.780 0.220
#> DRR006457 3 0.3791 0.7071 0.004 0.000 0.796 0.200
#> DRR006458 1 0.3688 0.7504 0.792 0.000 0.208 0.000
#> DRR006459 1 0.4008 0.7161 0.756 0.000 0.244 0.000
#> DRR006460 2 0.2921 0.8537 0.000 0.860 0.000 0.140
#> DRR006461 4 0.5127 0.2346 0.000 0.356 0.012 0.632
#> DRR006462 1 0.0336 0.8162 0.992 0.000 0.000 0.008
#> DRR006463 4 0.4374 0.7313 0.000 0.068 0.120 0.812
#> DRR006464 4 0.4123 0.5334 0.008 0.000 0.220 0.772
#> DRR006465 1 0.3172 0.7817 0.840 0.000 0.160 0.000
#> DRR006466 4 0.5028 0.2119 0.004 0.000 0.400 0.596
#> DRR006467 1 0.3400 0.7715 0.820 0.000 0.180 0.000
#> DRR006468 2 0.0000 0.8272 0.000 1.000 0.000 0.000
#> DRR006469 4 0.1792 0.7469 0.000 0.068 0.000 0.932
#> DRR006470 3 0.1256 0.7759 0.028 0.000 0.964 0.008
#> DRR006471 1 0.4277 0.6720 0.720 0.000 0.280 0.000
#> DRR006472 3 0.4905 0.4481 0.004 0.000 0.632 0.364
#> DRR006473 2 0.4134 0.8107 0.000 0.740 0.000 0.260
#> DRR006474 2 0.4134 0.8107 0.000 0.740 0.000 0.260
#> DRR006475 3 0.4967 0.0221 0.452 0.000 0.548 0.000
#> DRR006476 4 0.0707 0.7508 0.000 0.000 0.020 0.980
#> DRR006477 4 0.5018 0.4033 0.012 0.000 0.332 0.656
#> DRR006478 1 0.3311 0.7752 0.828 0.000 0.172 0.000
#> DRR006479 3 0.2345 0.7447 0.100 0.000 0.900 0.000
#> DRR006480 1 0.4040 0.7115 0.752 0.000 0.248 0.000
#> DRR006481 3 0.3257 0.7299 0.004 0.000 0.844 0.152
#> DRR006482 1 0.4764 0.7359 0.788 0.000 0.088 0.124
#> DRR006483 1 0.4277 0.6720 0.720 0.000 0.280 0.000
#> DRR006484 3 0.0707 0.7760 0.020 0.000 0.980 0.000
#> DRR006485 4 0.4374 0.7313 0.000 0.068 0.120 0.812
#> DRR006486 3 0.3400 0.6689 0.180 0.000 0.820 0.000
#> DRR006487 3 0.3801 0.6787 0.000 0.000 0.780 0.220
#> DRR006488 2 0.0000 0.8272 0.000 1.000 0.000 0.000
#> DRR006489 1 0.3400 0.7715 0.820 0.000 0.180 0.000
#> DRR006490 3 0.1389 0.7753 0.048 0.000 0.952 0.000
#> DRR006491 3 0.1302 0.7759 0.044 0.000 0.956 0.000
#> DRR006492 1 0.4500 0.6142 0.684 0.000 0.316 0.000
#> DRR006493 3 0.3688 0.6934 0.000 0.000 0.792 0.208
#> DRR006494 1 0.4277 0.6720 0.720 0.000 0.280 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 5 0.4526 0.75212 0.000 0.300 0.000 0.028 0.672
#> DRR006375 1 0.2130 0.70534 0.908 0.000 0.012 0.080 0.000
#> DRR006376 4 0.3999 0.64303 0.344 0.000 0.000 0.656 0.000
#> DRR006377 4 0.5540 0.48969 0.032 0.104 0.164 0.700 0.000
#> DRR006378 2 0.2989 0.73168 0.000 0.868 0.000 0.072 0.060
#> DRR006379 4 0.3480 0.71211 0.248 0.000 0.000 0.752 0.000
#> DRR006380 2 0.2629 0.75615 0.000 0.896 0.008 0.064 0.032
#> DRR006381 1 0.0865 0.72639 0.972 0.000 0.004 0.024 0.000
#> DRR006382 2 0.3806 0.74667 0.000 0.840 0.056 0.064 0.040
#> DRR006383 2 0.4933 0.76229 0.000 0.760 0.100 0.104 0.036
#> DRR006384 5 0.4095 0.77884 0.000 0.220 0.004 0.024 0.752
#> DRR006385 1 0.5148 -0.09286 0.528 0.000 0.040 0.432 0.000
#> DRR006386 5 0.0794 0.75147 0.000 0.000 0.000 0.028 0.972
#> DRR006387 1 0.4074 0.23779 0.636 0.000 0.000 0.364 0.000
#> DRR006388 4 0.5145 0.71665 0.148 0.052 0.060 0.740 0.000
#> DRR006389 4 0.5145 0.71665 0.148 0.052 0.060 0.740 0.000
#> DRR006390 5 0.4060 0.73216 0.000 0.360 0.000 0.000 0.640
#> DRR006391 5 0.4060 0.73216 0.000 0.360 0.000 0.000 0.640
#> DRR006392 1 0.1211 0.72781 0.960 0.000 0.016 0.024 0.000
#> DRR006393 1 0.1638 0.71029 0.932 0.000 0.004 0.064 0.000
#> DRR006394 2 0.3821 0.73351 0.000 0.764 0.020 0.216 0.000
#> DRR006395 4 0.3895 0.67274 0.320 0.000 0.000 0.680 0.000
#> DRR006396 1 0.4375 0.09051 0.576 0.000 0.004 0.420 0.000
#> DRR006397 4 0.5078 0.71761 0.148 0.048 0.060 0.744 0.000
#> DRR006398 4 0.5078 0.71761 0.148 0.048 0.060 0.744 0.000
#> DRR006399 4 0.4138 0.57436 0.384 0.000 0.000 0.616 0.000
#> DRR006400 4 0.4138 0.57436 0.384 0.000 0.000 0.616 0.000
#> DRR006401 5 0.4295 0.77560 0.000 0.248 0.004 0.024 0.724
#> DRR006402 5 0.4295 0.77560 0.000 0.248 0.004 0.024 0.724
#> DRR006403 4 0.4138 0.57436 0.384 0.000 0.000 0.616 0.000
#> DRR006404 4 0.3661 0.70467 0.276 0.000 0.000 0.724 0.000
#> DRR006405 4 0.4101 0.66192 0.332 0.004 0.000 0.664 0.000
#> DRR006406 4 0.4101 0.66192 0.332 0.004 0.000 0.664 0.000
#> DRR006407 4 0.4075 0.68516 0.096 0.100 0.004 0.800 0.000
#> DRR006408 4 0.4403 -0.10827 0.000 0.436 0.004 0.560 0.000
#> DRR006409 1 0.1608 0.70863 0.928 0.000 0.072 0.000 0.000
#> DRR006410 1 0.3274 0.55613 0.780 0.000 0.000 0.220 0.000
#> DRR006411 4 0.5214 0.69931 0.120 0.076 0.060 0.744 0.000
#> DRR006412 5 0.4074 0.72800 0.000 0.364 0.000 0.000 0.636
#> DRR006413 1 0.2017 0.70228 0.912 0.000 0.008 0.080 0.000
#> DRR006414 3 0.3166 0.80908 0.104 0.016 0.860 0.020 0.000
#> DRR006415 3 0.2729 0.74478 0.000 0.060 0.884 0.056 0.000
#> DRR006416 4 0.5350 0.70226 0.156 0.056 0.064 0.724 0.000
#> DRR006417 3 0.2853 0.80246 0.036 0.028 0.892 0.044 0.000
#> DRR006418 3 0.6664 0.35431 0.356 0.012 0.468 0.164 0.000
#> DRR006419 3 0.3729 0.80129 0.124 0.012 0.824 0.040 0.000
#> DRR006420 3 0.3573 0.80269 0.124 0.012 0.832 0.032 0.000
#> DRR006421 3 0.1914 0.79295 0.004 0.032 0.932 0.032 0.000
#> DRR006422 2 0.2915 0.76422 0.000 0.860 0.000 0.116 0.024
#> DRR006423 5 0.4114 0.71434 0.000 0.376 0.000 0.000 0.624
#> DRR006424 1 0.1430 0.71264 0.944 0.000 0.004 0.052 0.000
#> DRR006425 2 0.2362 0.76148 0.000 0.900 0.000 0.076 0.024
#> DRR006426 3 0.4387 0.77148 0.048 0.040 0.796 0.116 0.000
#> DRR006427 5 0.0794 0.75147 0.000 0.000 0.000 0.028 0.972
#> DRR006428 3 0.2852 0.78751 0.172 0.000 0.828 0.000 0.000
#> DRR006429 2 0.4522 0.74379 0.000 0.736 0.068 0.196 0.000
#> DRR006430 1 0.0671 0.72984 0.980 0.000 0.016 0.004 0.000
#> DRR006431 1 0.0671 0.72984 0.980 0.000 0.016 0.004 0.000
#> DRR006432 3 0.5317 0.69556 0.036 0.068 0.712 0.184 0.000
#> DRR006433 3 0.2871 0.76456 0.000 0.040 0.872 0.088 0.000
#> DRR006434 2 0.4716 0.19418 0.000 0.656 0.000 0.036 0.308
#> DRR006435 5 0.0794 0.75147 0.000 0.000 0.000 0.028 0.972
#> DRR006436 5 0.0794 0.75147 0.000 0.000 0.000 0.028 0.972
#> DRR006437 1 0.4542 -0.06858 0.536 0.000 0.008 0.456 0.000
#> DRR006438 3 0.3684 0.77362 0.192 0.004 0.788 0.016 0.000
#> DRR006439 3 0.2753 0.80549 0.136 0.000 0.856 0.008 0.000
#> DRR006440 2 0.4199 0.74897 0.000 0.772 0.160 0.068 0.000
#> DRR006441 2 0.1732 0.77537 0.000 0.920 0.000 0.080 0.000
#> DRR006442 3 0.3590 0.79305 0.148 0.016 0.820 0.016 0.000
#> DRR006443 2 0.4199 0.74897 0.000 0.772 0.160 0.068 0.000
#> DRR006444 5 0.0794 0.75147 0.000 0.000 0.000 0.028 0.972
#> DRR006445 1 0.4504 0.03977 0.564 0.000 0.008 0.428 0.000
#> DRR006446 5 0.4060 0.73216 0.000 0.360 0.000 0.000 0.640
#> DRR006447 1 0.5587 -0.24049 0.472 0.012 0.044 0.472 0.000
#> DRR006448 4 0.4045 0.59988 0.356 0.000 0.000 0.644 0.000
#> DRR006449 1 0.3300 0.57212 0.792 0.000 0.004 0.204 0.000
#> DRR006450 1 0.3282 0.60412 0.804 0.000 0.008 0.188 0.000
#> DRR006451 4 0.3586 0.70362 0.264 0.000 0.000 0.736 0.000
#> DRR006452 1 0.2929 0.64321 0.840 0.000 0.008 0.152 0.000
#> DRR006453 1 0.3957 0.45718 0.712 0.000 0.008 0.280 0.000
#> DRR006454 4 0.5258 0.70097 0.124 0.076 0.060 0.740 0.000
#> DRR006455 5 0.0794 0.75147 0.000 0.000 0.000 0.028 0.972
#> DRR006456 3 0.3180 0.72472 0.000 0.076 0.856 0.068 0.000
#> DRR006457 3 0.1982 0.79438 0.012 0.028 0.932 0.028 0.000
#> DRR006458 1 0.1478 0.71220 0.936 0.000 0.064 0.000 0.000
#> DRR006459 1 0.2020 0.69250 0.900 0.000 0.100 0.000 0.000
#> DRR006460 5 0.4095 0.77884 0.000 0.220 0.004 0.024 0.752
#> DRR006461 2 0.3216 0.68692 0.000 0.856 0.004 0.044 0.096
#> DRR006462 1 0.4291 -0.13628 0.536 0.000 0.000 0.464 0.000
#> DRR006463 2 0.4199 0.74897 0.000 0.772 0.160 0.068 0.000
#> DRR006464 2 0.5998 0.52649 0.004 0.544 0.112 0.340 0.000
#> DRR006465 1 0.1281 0.72545 0.956 0.000 0.012 0.032 0.000
#> DRR006466 3 0.6388 0.11755 0.000 0.312 0.496 0.192 0.000
#> DRR006467 1 0.0671 0.72984 0.980 0.000 0.016 0.004 0.000
#> DRR006468 5 0.0794 0.75147 0.000 0.000 0.000 0.028 0.972
#> DRR006469 2 0.1732 0.77537 0.000 0.920 0.000 0.080 0.000
#> DRR006470 3 0.3617 0.80445 0.088 0.012 0.840 0.060 0.000
#> DRR006471 1 0.2574 0.67528 0.876 0.000 0.112 0.012 0.000
#> DRR006472 3 0.5045 0.60435 0.000 0.108 0.696 0.196 0.000
#> DRR006473 5 0.4114 0.71434 0.000 0.376 0.000 0.000 0.624
#> DRR006474 5 0.4555 0.72923 0.000 0.344 0.000 0.020 0.636
#> DRR006475 1 0.3280 0.60784 0.812 0.000 0.176 0.012 0.000
#> DRR006476 2 0.4455 0.75265 0.000 0.744 0.068 0.188 0.000
#> DRR006477 3 0.6732 -0.00883 0.000 0.272 0.412 0.316 0.000
#> DRR006478 1 0.0798 0.72927 0.976 0.000 0.016 0.008 0.000
#> DRR006479 3 0.3718 0.77049 0.196 0.004 0.784 0.016 0.000
#> DRR006480 1 0.1908 0.69738 0.908 0.000 0.092 0.000 0.000
#> DRR006481 3 0.2436 0.80574 0.036 0.020 0.912 0.032 0.000
#> DRR006482 4 0.4933 0.70256 0.248 0.004 0.060 0.688 0.000
#> DRR006483 1 0.2574 0.67528 0.876 0.000 0.112 0.012 0.000
#> DRR006484 3 0.3031 0.80865 0.120 0.004 0.856 0.020 0.000
#> DRR006485 2 0.4199 0.74897 0.000 0.772 0.160 0.068 0.000
#> DRR006486 1 0.4644 -0.16580 0.528 0.000 0.460 0.012 0.000
#> DRR006487 3 0.2729 0.74478 0.000 0.060 0.884 0.056 0.000
#> DRR006488 5 0.0794 0.75147 0.000 0.000 0.000 0.028 0.972
#> DRR006489 1 0.0671 0.72984 0.980 0.000 0.016 0.004 0.000
#> DRR006490 3 0.3010 0.78737 0.172 0.000 0.824 0.004 0.000
#> DRR006491 3 0.3154 0.79416 0.148 0.004 0.836 0.012 0.000
#> DRR006492 1 0.2806 0.65802 0.844 0.000 0.152 0.004 0.000
#> DRR006493 3 0.2592 0.75040 0.000 0.052 0.892 0.056 0.000
#> DRR006494 1 0.2074 0.68946 0.896 0.000 0.104 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 5 0.4370 0.6795 0.000 0.356 0.000 0.020 0.616 0.008
#> DRR006375 1 0.1556 0.7855 0.920 0.000 0.000 0.000 0.000 0.080
#> DRR006376 6 0.5564 0.2417 0.148 0.000 0.000 0.352 0.000 0.500
#> DRR006377 4 0.5921 0.5718 0.016 0.024 0.080 0.532 0.000 0.348
#> DRR006378 2 0.4961 0.6251 0.000 0.720 0.012 0.160 0.076 0.032
#> DRR006379 6 0.5147 0.1850 0.096 0.000 0.000 0.356 0.000 0.548
#> DRR006380 2 0.3262 0.6959 0.000 0.828 0.004 0.132 0.008 0.028
#> DRR006381 1 0.2092 0.7647 0.876 0.000 0.000 0.000 0.000 0.124
#> DRR006382 2 0.2312 0.6839 0.000 0.896 0.012 0.080 0.008 0.004
#> DRR006383 2 0.3664 0.6799 0.000 0.816 0.052 0.112 0.008 0.012
#> DRR006384 5 0.3957 0.7145 0.000 0.280 0.000 0.020 0.696 0.004
#> DRR006385 6 0.3050 0.4118 0.236 0.000 0.000 0.000 0.000 0.764
#> DRR006386 5 0.1556 0.6756 0.000 0.000 0.000 0.080 0.920 0.000
#> DRR006387 1 0.4814 0.0515 0.532 0.000 0.000 0.056 0.000 0.412
#> DRR006388 6 0.4062 0.1608 0.024 0.012 0.052 0.116 0.000 0.796
#> DRR006389 6 0.4062 0.1608 0.024 0.012 0.052 0.116 0.000 0.796
#> DRR006390 5 0.4829 0.6681 0.000 0.356 0.000 0.056 0.584 0.004
#> DRR006391 5 0.4829 0.6681 0.000 0.356 0.000 0.056 0.584 0.004
#> DRR006392 1 0.0713 0.8045 0.972 0.000 0.000 0.000 0.000 0.028
#> DRR006393 1 0.1957 0.7624 0.888 0.000 0.000 0.000 0.000 0.112
#> DRR006394 2 0.6003 0.4952 0.000 0.536 0.028 0.288 0.000 0.148
#> DRR006395 6 0.5536 0.2355 0.144 0.000 0.000 0.352 0.000 0.504
#> DRR006396 6 0.4118 0.3983 0.312 0.000 0.000 0.028 0.000 0.660
#> DRR006397 6 0.3883 0.1741 0.024 0.012 0.052 0.100 0.000 0.812
#> DRR006398 6 0.3883 0.1741 0.024 0.012 0.052 0.100 0.000 0.812
#> DRR006399 6 0.5778 0.2677 0.184 0.000 0.000 0.352 0.000 0.464
#> DRR006400 6 0.5778 0.2677 0.184 0.000 0.000 0.352 0.000 0.464
#> DRR006401 5 0.4135 0.7124 0.000 0.300 0.000 0.032 0.668 0.000
#> DRR006402 5 0.4135 0.7124 0.000 0.300 0.000 0.032 0.668 0.000
#> DRR006403 6 0.5778 0.2677 0.184 0.000 0.000 0.352 0.000 0.464
#> DRR006404 6 0.5362 0.1887 0.120 0.000 0.000 0.356 0.000 0.524
#> DRR006405 6 0.5571 0.2101 0.144 0.000 0.000 0.372 0.000 0.484
#> DRR006406 6 0.5571 0.2101 0.144 0.000 0.000 0.372 0.000 0.484
#> DRR006407 4 0.5383 0.4935 0.020 0.020 0.028 0.508 0.000 0.424
#> DRR006408 4 0.5908 0.4529 0.000 0.256 0.008 0.520 0.000 0.216
#> DRR006409 1 0.0837 0.7995 0.972 0.000 0.020 0.004 0.000 0.004
#> DRR006410 1 0.4408 0.4509 0.664 0.000 0.000 0.056 0.000 0.280
#> DRR006411 6 0.4434 0.0417 0.016 0.008 0.064 0.164 0.000 0.748
#> DRR006412 5 0.4840 0.6646 0.000 0.360 0.000 0.056 0.580 0.004
#> DRR006413 1 0.3737 0.4055 0.608 0.000 0.000 0.000 0.000 0.392
#> DRR006414 3 0.3327 0.7910 0.044 0.000 0.844 0.076 0.000 0.036
#> DRR006415 3 0.2912 0.7635 0.000 0.040 0.844 0.116 0.000 0.000
#> DRR006416 6 0.4726 0.0628 0.032 0.012 0.056 0.164 0.000 0.736
#> DRR006417 3 0.4056 0.7388 0.020 0.000 0.780 0.124 0.000 0.076
#> DRR006418 6 0.7345 -0.0362 0.156 0.000 0.280 0.172 0.000 0.392
#> DRR006419 3 0.4475 0.7379 0.036 0.000 0.756 0.108 0.000 0.100
#> DRR006420 3 0.4008 0.7755 0.060 0.000 0.800 0.080 0.000 0.060
#> DRR006421 3 0.1036 0.7943 0.004 0.000 0.964 0.024 0.000 0.008
#> DRR006422 2 0.4262 0.6725 0.000 0.744 0.016 0.180 0.000 0.060
#> DRR006423 5 0.4808 0.6612 0.000 0.368 0.000 0.052 0.576 0.004
#> DRR006424 1 0.1765 0.7771 0.904 0.000 0.000 0.000 0.000 0.096
#> DRR006425 2 0.4018 0.6738 0.000 0.768 0.012 0.176 0.008 0.036
#> DRR006426 3 0.5287 0.6125 0.016 0.004 0.660 0.176 0.000 0.144
#> DRR006427 5 0.1556 0.6756 0.000 0.000 0.000 0.080 0.920 0.000
#> DRR006428 3 0.2618 0.7870 0.116 0.000 0.860 0.024 0.000 0.000
#> DRR006429 2 0.6139 0.5534 0.000 0.548 0.060 0.280 0.000 0.112
#> DRR006430 1 0.0632 0.8052 0.976 0.000 0.000 0.000 0.000 0.024
#> DRR006431 1 0.0508 0.8048 0.984 0.000 0.000 0.004 0.000 0.012
#> DRR006432 3 0.5722 0.5361 0.016 0.004 0.600 0.200 0.000 0.180
#> DRR006433 3 0.2393 0.7675 0.004 0.000 0.884 0.092 0.000 0.020
#> DRR006434 2 0.4063 0.2517 0.000 0.712 0.000 0.028 0.252 0.008
#> DRR006435 5 0.1556 0.6756 0.000 0.000 0.000 0.080 0.920 0.000
#> DRR006436 5 0.1556 0.6756 0.000 0.000 0.000 0.080 0.920 0.000
#> DRR006437 6 0.3797 0.4160 0.292 0.000 0.000 0.016 0.000 0.692
#> DRR006438 3 0.3742 0.7712 0.120 0.000 0.796 0.076 0.000 0.008
#> DRR006439 3 0.2146 0.7885 0.116 0.000 0.880 0.004 0.000 0.000
#> DRR006440 2 0.3925 0.6653 0.000 0.764 0.064 0.168 0.000 0.004
#> DRR006441 2 0.3901 0.6869 0.000 0.764 0.012 0.184 0.000 0.040
#> DRR006442 3 0.3221 0.7845 0.096 0.000 0.828 0.076 0.000 0.000
#> DRR006443 2 0.3869 0.6678 0.000 0.768 0.060 0.168 0.000 0.004
#> DRR006444 5 0.1556 0.6756 0.000 0.000 0.000 0.080 0.920 0.000
#> DRR006445 6 0.3874 0.3044 0.356 0.000 0.000 0.008 0.000 0.636
#> DRR006446 5 0.4765 0.6755 0.000 0.352 0.000 0.052 0.592 0.004
#> DRR006447 6 0.4521 0.3541 0.236 0.000 0.008 0.064 0.000 0.692
#> DRR006448 6 0.5592 0.2817 0.156 0.000 0.000 0.340 0.000 0.504
#> DRR006449 1 0.4101 0.4751 0.664 0.000 0.000 0.028 0.000 0.308
#> DRR006450 1 0.3862 0.1853 0.524 0.000 0.000 0.000 0.000 0.476
#> DRR006451 6 0.5098 0.2164 0.092 0.000 0.000 0.352 0.000 0.556
#> DRR006452 1 0.3810 0.3239 0.572 0.000 0.000 0.000 0.000 0.428
#> DRR006453 6 0.3843 0.0121 0.452 0.000 0.000 0.000 0.000 0.548
#> DRR006454 6 0.4062 0.1567 0.024 0.012 0.052 0.116 0.000 0.796
#> DRR006455 5 0.1556 0.6756 0.000 0.000 0.000 0.080 0.920 0.000
#> DRR006456 3 0.3784 0.7198 0.000 0.080 0.776 0.144 0.000 0.000
#> DRR006457 3 0.1168 0.7964 0.016 0.000 0.956 0.028 0.000 0.000
#> DRR006458 1 0.0748 0.8006 0.976 0.000 0.016 0.004 0.000 0.004
#> DRR006459 1 0.0777 0.7967 0.972 0.000 0.024 0.004 0.000 0.000
#> DRR006460 5 0.3840 0.7151 0.000 0.284 0.000 0.020 0.696 0.000
#> DRR006461 2 0.1867 0.6601 0.000 0.924 0.000 0.036 0.036 0.004
#> DRR006462 6 0.4945 0.3994 0.304 0.000 0.000 0.092 0.000 0.604
#> DRR006463 2 0.3869 0.6678 0.000 0.768 0.060 0.168 0.000 0.004
#> DRR006464 2 0.7312 0.0788 0.000 0.328 0.100 0.308 0.000 0.264
#> DRR006465 1 0.1141 0.7961 0.948 0.000 0.000 0.000 0.000 0.052
#> DRR006466 3 0.6681 0.1318 0.000 0.252 0.488 0.192 0.000 0.068
#> DRR006467 1 0.0632 0.8059 0.976 0.000 0.000 0.000 0.000 0.024
#> DRR006468 5 0.1556 0.6756 0.000 0.000 0.000 0.080 0.920 0.000
#> DRR006469 2 0.3932 0.6876 0.000 0.760 0.012 0.188 0.000 0.040
#> DRR006470 3 0.4419 0.7219 0.028 0.000 0.752 0.140 0.000 0.080
#> DRR006471 1 0.2703 0.7340 0.876 0.000 0.064 0.052 0.000 0.008
#> DRR006472 3 0.5670 0.5529 0.000 0.056 0.640 0.176 0.000 0.128
#> DRR006473 5 0.4808 0.6612 0.000 0.368 0.000 0.052 0.576 0.004
#> DRR006474 5 0.4394 0.6816 0.000 0.364 0.000 0.020 0.608 0.008
#> DRR006475 1 0.3029 0.7112 0.852 0.000 0.088 0.052 0.000 0.008
#> DRR006476 2 0.6005 0.5828 0.000 0.560 0.060 0.284 0.000 0.096
#> DRR006477 3 0.6501 0.3592 0.000 0.152 0.512 0.268 0.000 0.068
#> DRR006478 1 0.0858 0.8058 0.968 0.000 0.000 0.004 0.000 0.028
#> DRR006479 3 0.3852 0.7654 0.136 0.000 0.784 0.072 0.000 0.008
#> DRR006480 1 0.0777 0.7972 0.972 0.000 0.024 0.004 0.000 0.000
#> DRR006481 3 0.2203 0.7875 0.016 0.000 0.896 0.084 0.000 0.004
#> DRR006482 6 0.2547 0.3724 0.112 0.000 0.016 0.004 0.000 0.868
#> DRR006483 1 0.2703 0.7340 0.876 0.000 0.064 0.052 0.000 0.008
#> DRR006484 3 0.3426 0.7797 0.116 0.000 0.816 0.064 0.000 0.004
#> DRR006485 2 0.3869 0.6678 0.000 0.768 0.060 0.168 0.000 0.004
#> DRR006486 1 0.4284 0.5136 0.720 0.000 0.216 0.056 0.000 0.008
#> DRR006487 3 0.2912 0.7635 0.000 0.040 0.844 0.116 0.000 0.000
#> DRR006488 5 0.1556 0.6756 0.000 0.000 0.000 0.080 0.920 0.000
#> DRR006489 1 0.0790 0.8046 0.968 0.000 0.000 0.000 0.000 0.032
#> DRR006490 3 0.2618 0.7870 0.116 0.000 0.860 0.024 0.000 0.000
#> DRR006491 3 0.2971 0.7892 0.104 0.000 0.844 0.052 0.000 0.000
#> DRR006492 1 0.3656 0.6839 0.784 0.000 0.164 0.004 0.000 0.048
#> DRR006493 3 0.2912 0.7635 0.000 0.040 0.844 0.116 0.000 0.000
#> DRR006494 1 0.1082 0.7884 0.956 0.000 0.040 0.004 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)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.959 0.984 0.4560 0.548 0.548
#> 3 3 1.000 0.969 0.987 0.4286 0.785 0.613
#> 4 4 0.814 0.730 0.861 0.1356 0.883 0.674
#> 5 5 0.816 0.809 0.863 0.0501 0.959 0.843
#> 6 6 0.821 0.781 0.863 0.0368 0.972 0.880
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DRR006374 2 0.0000 0.984 0.000 1.000
#> DRR006375 1 0.0000 0.983 1.000 0.000
#> DRR006376 1 0.0000 0.983 1.000 0.000
#> DRR006377 1 0.0000 0.983 1.000 0.000
#> DRR006378 2 0.0000 0.984 0.000 1.000
#> DRR006379 1 0.0000 0.983 1.000 0.000
#> DRR006380 2 0.0000 0.984 0.000 1.000
#> DRR006381 1 0.0000 0.983 1.000 0.000
#> DRR006382 2 0.0000 0.984 0.000 1.000
#> DRR006383 2 0.0000 0.984 0.000 1.000
#> DRR006384 2 0.0000 0.984 0.000 1.000
#> DRR006385 1 0.0000 0.983 1.000 0.000
#> DRR006386 2 0.0000 0.984 0.000 1.000
#> DRR006387 1 0.0000 0.983 1.000 0.000
#> DRR006388 1 0.0000 0.983 1.000 0.000
#> DRR006389 1 0.0000 0.983 1.000 0.000
#> DRR006390 2 0.0000 0.984 0.000 1.000
#> DRR006391 2 0.0000 0.984 0.000 1.000
#> DRR006392 1 0.0000 0.983 1.000 0.000
#> DRR006393 1 0.0000 0.983 1.000 0.000
#> DRR006394 2 0.0000 0.984 0.000 1.000
#> DRR006395 1 0.0000 0.983 1.000 0.000
#> DRR006396 1 0.0000 0.983 1.000 0.000
#> DRR006397 1 0.0000 0.983 1.000 0.000
#> DRR006398 1 0.0000 0.983 1.000 0.000
#> DRR006399 1 0.0000 0.983 1.000 0.000
#> DRR006400 1 0.0000 0.983 1.000 0.000
#> DRR006401 2 0.0000 0.984 0.000 1.000
#> DRR006402 2 0.0000 0.984 0.000 1.000
#> DRR006403 1 0.0000 0.983 1.000 0.000
#> DRR006404 1 0.0000 0.983 1.000 0.000
#> DRR006405 1 0.0000 0.983 1.000 0.000
#> DRR006406 1 0.0000 0.983 1.000 0.000
#> DRR006407 1 0.8763 0.574 0.704 0.296
#> DRR006408 2 0.0000 0.984 0.000 1.000
#> DRR006409 1 0.0000 0.983 1.000 0.000
#> DRR006410 1 0.0000 0.983 1.000 0.000
#> DRR006411 1 0.7219 0.742 0.800 0.200
#> DRR006412 2 0.0000 0.984 0.000 1.000
#> DRR006413 1 0.0000 0.983 1.000 0.000
#> DRR006414 1 0.0000 0.983 1.000 0.000
#> DRR006415 1 0.0000 0.983 1.000 0.000
#> DRR006416 1 0.0000 0.983 1.000 0.000
#> DRR006417 1 0.0000 0.983 1.000 0.000
#> DRR006418 1 0.0000 0.983 1.000 0.000
#> DRR006419 1 0.0000 0.983 1.000 0.000
#> DRR006420 1 0.0000 0.983 1.000 0.000
#> DRR006421 1 0.0000 0.983 1.000 0.000
#> DRR006422 2 0.0000 0.984 0.000 1.000
#> DRR006423 2 0.0000 0.984 0.000 1.000
#> DRR006424 1 0.0000 0.983 1.000 0.000
#> DRR006425 2 0.0000 0.984 0.000 1.000
#> DRR006426 1 0.0000 0.983 1.000 0.000
#> DRR006427 2 0.0000 0.984 0.000 1.000
#> DRR006428 1 0.0000 0.983 1.000 0.000
#> DRR006429 2 0.0000 0.984 0.000 1.000
#> DRR006430 1 0.0000 0.983 1.000 0.000
#> DRR006431 1 0.0000 0.983 1.000 0.000
#> DRR006432 1 0.0000 0.983 1.000 0.000
#> DRR006433 1 0.0000 0.983 1.000 0.000
#> DRR006434 2 0.0000 0.984 0.000 1.000
#> DRR006435 2 0.0000 0.984 0.000 1.000
#> DRR006436 2 0.0000 0.984 0.000 1.000
#> DRR006437 1 0.0000 0.983 1.000 0.000
#> DRR006438 1 0.0000 0.983 1.000 0.000
#> DRR006439 1 0.0000 0.983 1.000 0.000
#> DRR006440 2 0.0000 0.984 0.000 1.000
#> DRR006441 2 0.0000 0.984 0.000 1.000
#> DRR006442 1 0.0000 0.983 1.000 0.000
#> DRR006443 2 0.0000 0.984 0.000 1.000
#> DRR006444 2 0.0000 0.984 0.000 1.000
#> DRR006445 1 0.0000 0.983 1.000 0.000
#> DRR006446 2 0.0000 0.984 0.000 1.000
#> DRR006447 1 0.0000 0.983 1.000 0.000
#> DRR006448 1 0.0000 0.983 1.000 0.000
#> DRR006449 1 0.0000 0.983 1.000 0.000
#> DRR006450 1 0.0000 0.983 1.000 0.000
#> DRR006451 1 0.0000 0.983 1.000 0.000
#> DRR006452 1 0.0000 0.983 1.000 0.000
#> DRR006453 1 0.0000 0.983 1.000 0.000
#> DRR006454 1 0.0000 0.983 1.000 0.000
#> DRR006455 2 0.0000 0.984 0.000 1.000
#> DRR006456 1 0.9710 0.335 0.600 0.400
#> DRR006457 1 0.0000 0.983 1.000 0.000
#> DRR006458 1 0.0000 0.983 1.000 0.000
#> DRR006459 1 0.0000 0.983 1.000 0.000
#> DRR006460 2 0.0000 0.984 0.000 1.000
#> DRR006461 2 0.0000 0.984 0.000 1.000
#> DRR006462 1 0.0000 0.983 1.000 0.000
#> DRR006463 2 0.0000 0.984 0.000 1.000
#> DRR006464 2 0.6887 0.765 0.184 0.816
#> DRR006465 1 0.0000 0.983 1.000 0.000
#> DRR006466 2 0.0376 0.981 0.004 0.996
#> DRR006467 1 0.0000 0.983 1.000 0.000
#> DRR006468 2 0.0000 0.984 0.000 1.000
#> DRR006469 2 0.0000 0.984 0.000 1.000
#> DRR006470 1 0.0000 0.983 1.000 0.000
#> DRR006471 1 0.0000 0.983 1.000 0.000
#> DRR006472 2 0.9775 0.288 0.412 0.588
#> DRR006473 2 0.0000 0.984 0.000 1.000
#> DRR006474 2 0.0000 0.984 0.000 1.000
#> DRR006475 1 0.0000 0.983 1.000 0.000
#> DRR006476 2 0.0000 0.984 0.000 1.000
#> DRR006477 1 0.9710 0.335 0.600 0.400
#> DRR006478 1 0.0000 0.983 1.000 0.000
#> DRR006479 1 0.0000 0.983 1.000 0.000
#> DRR006480 1 0.0000 0.983 1.000 0.000
#> DRR006481 1 0.0000 0.983 1.000 0.000
#> DRR006482 1 0.0000 0.983 1.000 0.000
#> DRR006483 1 0.0000 0.983 1.000 0.000
#> DRR006484 1 0.0000 0.983 1.000 0.000
#> DRR006485 2 0.0000 0.984 0.000 1.000
#> DRR006486 1 0.0000 0.983 1.000 0.000
#> DRR006487 1 0.1633 0.960 0.976 0.024
#> DRR006488 2 0.0000 0.984 0.000 1.000
#> DRR006489 1 0.0000 0.983 1.000 0.000
#> DRR006490 1 0.0000 0.983 1.000 0.000
#> DRR006491 1 0.0000 0.983 1.000 0.000
#> DRR006492 1 0.0000 0.983 1.000 0.000
#> DRR006493 1 0.0000 0.983 1.000 0.000
#> DRR006494 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
#> DRR006374 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006375 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006376 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006377 3 0.4504 0.748 0.196 0.00 0.804
#> DRR006378 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006379 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006380 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006381 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006382 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006383 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006384 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006385 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006386 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006387 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006388 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006389 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006390 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006391 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006392 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006393 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006394 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006395 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006396 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006397 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006398 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006399 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006400 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006401 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006402 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006403 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006404 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006405 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006406 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006407 1 0.4291 0.780 0.820 0.18 0.000
#> DRR006408 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006409 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006410 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006411 1 0.2537 0.904 0.920 0.08 0.000
#> DRR006412 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006413 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006414 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006415 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006416 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006417 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006418 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006419 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006420 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006421 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006422 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006423 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006424 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006425 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006426 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006427 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006428 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006429 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006430 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006431 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006432 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006433 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006434 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006435 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006436 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006437 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006438 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006439 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006440 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006441 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006442 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006443 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006444 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006445 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006446 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006447 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006448 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006449 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006450 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006451 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006452 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006453 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006454 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006455 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006456 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006457 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006458 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006459 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006460 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006461 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006462 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006463 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006464 2 0.4555 0.746 0.000 0.80 0.200
#> DRR006465 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006466 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006467 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006468 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006469 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006470 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006471 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006472 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006473 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006474 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006475 3 0.6274 0.158 0.456 0.00 0.544
#> DRR006476 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006477 3 0.4555 0.733 0.000 0.20 0.800
#> DRR006478 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006479 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006480 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006481 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006482 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006483 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006484 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006485 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006486 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006487 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006488 2 0.0000 0.995 0.000 1.00 0.000
#> DRR006489 1 0.0000 0.990 1.000 0.00 0.000
#> DRR006490 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006491 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006492 1 0.4555 0.742 0.800 0.00 0.200
#> DRR006493 3 0.0000 0.967 0.000 0.00 1.000
#> DRR006494 1 0.0424 0.983 0.992 0.00 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006375 1 0.2814 0.5723 0.868 0.000 0.000 0.132
#> DRR006376 4 0.4866 0.5466 0.404 0.000 0.000 0.596
#> DRR006377 1 0.7236 -0.0799 0.520 0.000 0.168 0.312
#> DRR006378 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006379 4 0.4866 0.5466 0.404 0.000 0.000 0.596
#> DRR006380 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006381 1 0.4830 0.3937 0.608 0.000 0.000 0.392
#> DRR006382 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006383 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006384 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006385 4 0.4454 0.3303 0.308 0.000 0.000 0.692
#> DRR006386 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006387 1 0.4790 -0.1208 0.620 0.000 0.000 0.380
#> DRR006388 4 0.2704 0.5215 0.124 0.000 0.000 0.876
#> DRR006389 4 0.2704 0.5215 0.124 0.000 0.000 0.876
#> DRR006390 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006391 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006392 1 0.1557 0.6573 0.944 0.000 0.000 0.056
#> DRR006393 1 0.1557 0.6573 0.944 0.000 0.000 0.056
#> DRR006394 2 0.0817 0.9625 0.000 0.976 0.000 0.024
#> DRR006395 4 0.4866 0.5466 0.404 0.000 0.000 0.596
#> DRR006396 4 0.4304 0.3826 0.284 0.000 0.000 0.716
#> DRR006397 4 0.2408 0.5339 0.104 0.000 0.000 0.896
#> DRR006398 4 0.2408 0.5339 0.104 0.000 0.000 0.896
#> DRR006399 4 0.4866 0.5466 0.404 0.000 0.000 0.596
#> DRR006400 4 0.4866 0.5466 0.404 0.000 0.000 0.596
#> DRR006401 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006402 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006403 4 0.4866 0.5466 0.404 0.000 0.000 0.596
#> DRR006404 4 0.4866 0.5466 0.404 0.000 0.000 0.596
#> DRR006405 4 0.4999 0.3963 0.492 0.000 0.000 0.508
#> DRR006406 4 0.4999 0.3963 0.492 0.000 0.000 0.508
#> DRR006407 4 0.4605 0.5312 0.336 0.000 0.000 0.664
#> DRR006408 2 0.3688 0.7527 0.000 0.792 0.000 0.208
#> DRR006409 1 0.0000 0.6559 1.000 0.000 0.000 0.000
#> DRR006410 1 0.4431 0.1607 0.696 0.000 0.000 0.304
#> DRR006411 4 0.0336 0.5345 0.008 0.000 0.000 0.992
#> DRR006412 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006413 1 0.4830 0.3937 0.608 0.000 0.000 0.392
#> DRR006414 3 0.0000 0.9464 0.000 0.000 1.000 0.000
#> DRR006415 3 0.0000 0.9464 0.000 0.000 1.000 0.000
#> DRR006416 1 0.4967 0.3319 0.548 0.000 0.000 0.452
#> DRR006417 3 0.2342 0.9396 0.080 0.000 0.912 0.008
#> DRR006418 1 0.4866 0.3295 0.596 0.000 0.000 0.404
#> DRR006419 3 0.2081 0.9389 0.084 0.000 0.916 0.000
#> DRR006420 3 0.3219 0.8661 0.164 0.000 0.836 0.000
#> DRR006421 3 0.1557 0.9492 0.056 0.000 0.944 0.000
#> DRR006422 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006423 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006424 1 0.1637 0.6565 0.940 0.000 0.000 0.060
#> DRR006425 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006426 3 0.3828 0.9050 0.084 0.000 0.848 0.068
#> DRR006427 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006428 3 0.1557 0.9492 0.056 0.000 0.944 0.000
#> DRR006429 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006430 1 0.1557 0.6573 0.944 0.000 0.000 0.056
#> DRR006431 1 0.1557 0.6573 0.944 0.000 0.000 0.056
#> DRR006432 3 0.4083 0.8919 0.100 0.000 0.832 0.068
#> DRR006433 3 0.0000 0.9464 0.000 0.000 1.000 0.000
#> DRR006434 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006435 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006436 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006437 4 0.4304 0.3826 0.284 0.000 0.000 0.716
#> DRR006438 3 0.2081 0.9389 0.084 0.000 0.916 0.000
#> DRR006439 3 0.1637 0.9481 0.060 0.000 0.940 0.000
#> DRR006440 2 0.1867 0.9286 0.000 0.928 0.072 0.000
#> DRR006441 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006442 3 0.0000 0.9464 0.000 0.000 1.000 0.000
#> DRR006443 2 0.1867 0.9286 0.000 0.928 0.072 0.000
#> DRR006444 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006445 1 0.4972 0.2926 0.544 0.000 0.000 0.456
#> DRR006446 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006447 1 0.4998 0.2788 0.512 0.000 0.000 0.488
#> DRR006448 4 0.4855 0.5475 0.400 0.000 0.000 0.600
#> DRR006449 1 0.2704 0.5851 0.876 0.000 0.000 0.124
#> DRR006450 1 0.4830 0.3937 0.608 0.000 0.000 0.392
#> DRR006451 4 0.1792 0.5550 0.068 0.000 0.000 0.932
#> DRR006452 1 0.4916 0.3495 0.576 0.000 0.000 0.424
#> DRR006453 1 0.4830 0.3937 0.608 0.000 0.000 0.392
#> DRR006454 4 0.1637 0.5425 0.060 0.000 0.000 0.940
#> DRR006455 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006456 3 0.0000 0.9464 0.000 0.000 1.000 0.000
#> DRR006457 3 0.0707 0.9493 0.020 0.000 0.980 0.000
#> DRR006458 1 0.0000 0.6559 1.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.6559 1.000 0.000 0.000 0.000
#> DRR006460 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006461 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006462 1 0.4925 -0.2607 0.572 0.000 0.000 0.428
#> DRR006463 2 0.1867 0.9286 0.000 0.928 0.072 0.000
#> DRR006464 2 0.5882 0.7333 0.112 0.756 0.064 0.068
#> DRR006465 1 0.1557 0.6573 0.944 0.000 0.000 0.056
#> DRR006466 3 0.0000 0.9464 0.000 0.000 1.000 0.000
#> DRR006467 1 0.1557 0.6573 0.944 0.000 0.000 0.056
#> DRR006468 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006469 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006470 3 0.3828 0.9050 0.084 0.000 0.848 0.068
#> DRR006471 1 0.0000 0.6559 1.000 0.000 0.000 0.000
#> DRR006472 3 0.0672 0.9478 0.008 0.000 0.984 0.008
#> DRR006473 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006474 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006475 1 0.0817 0.6402 0.976 0.000 0.024 0.000
#> DRR006476 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006477 3 0.2918 0.8267 0.000 0.116 0.876 0.008
#> DRR006478 1 0.1474 0.6580 0.948 0.000 0.000 0.052
#> DRR006479 3 0.2081 0.9389 0.084 0.000 0.916 0.000
#> DRR006480 1 0.0000 0.6559 1.000 0.000 0.000 0.000
#> DRR006481 3 0.1557 0.9492 0.056 0.000 0.944 0.000
#> DRR006482 4 0.4304 0.3826 0.284 0.000 0.000 0.716
#> DRR006483 1 0.0000 0.6559 1.000 0.000 0.000 0.000
#> DRR006484 3 0.1557 0.9492 0.056 0.000 0.944 0.000
#> DRR006485 2 0.1867 0.9286 0.000 0.928 0.072 0.000
#> DRR006486 1 0.4985 -0.0659 0.532 0.000 0.468 0.000
#> DRR006487 3 0.0000 0.9464 0.000 0.000 1.000 0.000
#> DRR006488 2 0.0000 0.9807 0.000 1.000 0.000 0.000
#> DRR006489 1 0.1557 0.6573 0.944 0.000 0.000 0.056
#> DRR006490 3 0.1557 0.9492 0.056 0.000 0.944 0.000
#> DRR006491 3 0.0188 0.9473 0.004 0.000 0.996 0.000
#> DRR006492 1 0.2589 0.5493 0.884 0.000 0.116 0.000
#> DRR006493 3 0.0000 0.9464 0.000 0.000 1.000 0.000
#> DRR006494 1 0.0000 0.6559 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
#> DRR006374 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006375 1 0.3146 0.7167 0.844 0.000 0.000 0.028 0.128
#> DRR006376 4 0.5190 0.8546 0.096 0.000 0.000 0.668 0.236
#> DRR006377 4 0.2302 0.5765 0.048 0.000 0.016 0.916 0.020
#> DRR006378 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006379 4 0.5142 0.8528 0.088 0.000 0.000 0.668 0.244
#> DRR006380 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006381 1 0.2179 0.7924 0.888 0.000 0.000 0.000 0.112
#> DRR006382 2 0.0771 0.9519 0.000 0.976 0.000 0.020 0.004
#> DRR006383 2 0.0771 0.9519 0.000 0.976 0.000 0.020 0.004
#> DRR006384 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006385 5 0.2660 0.7601 0.128 0.000 0.000 0.008 0.864
#> DRR006386 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006387 1 0.4380 0.4887 0.708 0.000 0.000 0.032 0.260
#> DRR006388 5 0.1582 0.7762 0.028 0.000 0.000 0.028 0.944
#> DRR006389 5 0.1582 0.7762 0.028 0.000 0.000 0.028 0.944
#> DRR006390 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006391 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006392 1 0.0162 0.8526 0.996 0.000 0.000 0.000 0.004
#> DRR006393 1 0.0162 0.8526 0.996 0.000 0.000 0.000 0.004
#> DRR006394 2 0.1300 0.9321 0.000 0.956 0.000 0.028 0.016
#> DRR006395 4 0.5190 0.8546 0.096 0.000 0.000 0.668 0.236
#> DRR006396 5 0.4718 0.4332 0.344 0.000 0.000 0.028 0.628
#> DRR006397 5 0.0865 0.7785 0.024 0.000 0.000 0.004 0.972
#> DRR006398 5 0.0865 0.7785 0.024 0.000 0.000 0.004 0.972
#> DRR006399 4 0.5142 0.8528 0.088 0.000 0.000 0.668 0.244
#> DRR006400 4 0.5142 0.8528 0.088 0.000 0.000 0.668 0.244
#> DRR006401 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006403 4 0.5190 0.8546 0.096 0.000 0.000 0.668 0.236
#> DRR006404 4 0.5190 0.8546 0.096 0.000 0.000 0.668 0.236
#> DRR006405 4 0.5115 0.7731 0.168 0.000 0.000 0.696 0.136
#> DRR006406 4 0.5115 0.7731 0.168 0.000 0.000 0.696 0.136
#> DRR006407 4 0.4325 0.8021 0.044 0.000 0.000 0.736 0.220
#> DRR006408 4 0.4150 0.3590 0.000 0.388 0.000 0.612 0.000
#> DRR006409 1 0.0404 0.8500 0.988 0.000 0.012 0.000 0.000
#> DRR006410 1 0.3944 0.6029 0.768 0.000 0.000 0.032 0.200
#> DRR006411 5 0.2280 0.6739 0.000 0.000 0.000 0.120 0.880
#> DRR006412 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006413 1 0.3837 0.5467 0.692 0.000 0.000 0.000 0.308
#> DRR006414 3 0.0798 0.8359 0.016 0.000 0.976 0.008 0.000
#> DRR006415 3 0.1205 0.8271 0.000 0.000 0.956 0.040 0.004
#> DRR006416 1 0.4520 0.5656 0.684 0.000 0.000 0.032 0.284
#> DRR006417 3 0.5866 0.7159 0.004 0.000 0.604 0.260 0.132
#> DRR006418 1 0.7036 0.0460 0.420 0.000 0.012 0.280 0.288
#> DRR006419 3 0.6153 0.7304 0.024 0.000 0.620 0.220 0.136
#> DRR006420 3 0.5221 0.7345 0.172 0.000 0.696 0.128 0.004
#> DRR006421 3 0.2439 0.8502 0.004 0.000 0.876 0.120 0.000
#> DRR006422 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006423 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006424 1 0.0510 0.8494 0.984 0.000 0.000 0.000 0.016
#> DRR006425 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006426 3 0.6410 0.6783 0.016 0.000 0.556 0.280 0.148
#> DRR006427 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006428 3 0.2416 0.8515 0.012 0.000 0.888 0.100 0.000
#> DRR006429 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006430 1 0.0162 0.8526 0.996 0.000 0.000 0.000 0.004
#> DRR006431 1 0.0162 0.8526 0.996 0.000 0.000 0.000 0.004
#> DRR006432 3 0.6427 0.6755 0.016 0.000 0.552 0.284 0.148
#> DRR006433 3 0.0609 0.8353 0.000 0.000 0.980 0.020 0.000
#> DRR006434 2 0.0290 0.9620 0.000 0.992 0.000 0.008 0.000
#> DRR006435 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006437 5 0.3146 0.7372 0.128 0.000 0.000 0.028 0.844
#> DRR006438 3 0.3536 0.8372 0.032 0.000 0.812 0.156 0.000
#> DRR006439 3 0.2722 0.8496 0.020 0.000 0.872 0.108 0.000
#> DRR006440 2 0.3712 0.8161 0.000 0.820 0.124 0.052 0.004
#> DRR006441 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006442 3 0.0898 0.8357 0.008 0.000 0.972 0.020 0.000
#> DRR006443 2 0.3712 0.8161 0.000 0.820 0.124 0.052 0.004
#> DRR006444 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006445 1 0.4283 0.4573 0.644 0.000 0.000 0.008 0.348
#> DRR006446 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006447 5 0.3615 0.6907 0.156 0.000 0.000 0.036 0.808
#> DRR006448 4 0.4995 0.8350 0.068 0.000 0.000 0.668 0.264
#> DRR006449 1 0.1697 0.8171 0.932 0.000 0.000 0.008 0.060
#> DRR006450 1 0.3837 0.5473 0.692 0.000 0.000 0.000 0.308
#> DRR006451 4 0.4348 0.7558 0.016 0.000 0.000 0.668 0.316
#> DRR006452 1 0.4147 0.5248 0.676 0.000 0.000 0.008 0.316
#> DRR006453 1 0.2605 0.7613 0.852 0.000 0.000 0.000 0.148
#> DRR006454 5 0.0898 0.7744 0.020 0.000 0.000 0.008 0.972
#> DRR006455 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006456 3 0.1282 0.8252 0.000 0.000 0.952 0.044 0.004
#> DRR006457 3 0.2074 0.8515 0.000 0.000 0.896 0.104 0.000
#> DRR006458 1 0.0000 0.8519 1.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0404 0.8500 0.988 0.000 0.012 0.000 0.000
#> DRR006460 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006461 2 0.0290 0.9620 0.000 0.992 0.000 0.008 0.000
#> DRR006462 5 0.5115 0.0997 0.480 0.000 0.000 0.036 0.484
#> DRR006463 2 0.3712 0.8161 0.000 0.820 0.124 0.052 0.004
#> DRR006464 2 0.6433 0.3989 0.004 0.560 0.012 0.280 0.144
#> DRR006465 1 0.0162 0.8526 0.996 0.000 0.000 0.000 0.004
#> DRR006466 3 0.1430 0.8221 0.000 0.000 0.944 0.052 0.004
#> DRR006467 1 0.0162 0.8526 0.996 0.000 0.000 0.000 0.004
#> DRR006468 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006469 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006470 3 0.6308 0.6866 0.016 0.000 0.568 0.280 0.136
#> DRR006471 1 0.1012 0.8432 0.968 0.000 0.020 0.012 0.000
#> DRR006472 3 0.4326 0.8042 0.000 0.000 0.708 0.264 0.028
#> DRR006473 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006474 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006475 1 0.1403 0.8345 0.952 0.000 0.024 0.024 0.000
#> DRR006476 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006477 3 0.4527 0.5356 0.000 0.028 0.696 0.272 0.004
#> DRR006478 1 0.0162 0.8526 0.996 0.000 0.000 0.000 0.004
#> DRR006479 3 0.4454 0.7955 0.112 0.000 0.760 0.128 0.000
#> DRR006480 1 0.0404 0.8500 0.988 0.000 0.012 0.000 0.000
#> DRR006481 3 0.2848 0.8439 0.004 0.000 0.840 0.156 0.000
#> DRR006482 5 0.2624 0.7582 0.116 0.000 0.000 0.012 0.872
#> DRR006483 1 0.1012 0.8432 0.968 0.000 0.020 0.012 0.000
#> DRR006484 3 0.2439 0.8502 0.004 0.000 0.876 0.120 0.000
#> DRR006485 2 0.3712 0.8161 0.000 0.820 0.124 0.052 0.004
#> DRR006486 1 0.2570 0.7809 0.888 0.000 0.084 0.028 0.000
#> DRR006487 3 0.1205 0.8271 0.000 0.000 0.956 0.040 0.004
#> DRR006488 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000
#> DRR006489 1 0.0162 0.8526 0.996 0.000 0.000 0.000 0.004
#> DRR006490 3 0.2470 0.8513 0.012 0.000 0.884 0.104 0.000
#> DRR006491 3 0.0290 0.8396 0.008 0.000 0.992 0.000 0.000
#> DRR006492 1 0.1638 0.8202 0.932 0.000 0.064 0.004 0.000
#> DRR006493 3 0.1043 0.8286 0.000 0.000 0.960 0.040 0.000
#> DRR006494 1 0.0510 0.8490 0.984 0.000 0.016 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.0260 0.9319 0.000 0.992 0.000 0.008 0.000 0.000
#> DRR006375 1 0.2066 0.8033 0.904 0.000 0.000 0.024 0.000 0.072
#> DRR006376 4 0.2558 0.9106 0.028 0.000 0.000 0.868 0.000 0.104
#> DRR006377 4 0.1672 0.8103 0.004 0.000 0.000 0.932 0.048 0.016
#> DRR006378 2 0.0622 0.9304 0.000 0.980 0.000 0.012 0.008 0.000
#> DRR006379 4 0.2445 0.9097 0.020 0.000 0.000 0.872 0.000 0.108
#> DRR006380 2 0.0993 0.9210 0.000 0.964 0.000 0.012 0.024 0.000
#> DRR006381 1 0.2994 0.7056 0.788 0.000 0.000 0.004 0.000 0.208
#> DRR006382 2 0.2776 0.8463 0.000 0.860 0.004 0.020 0.112 0.004
#> DRR006383 2 0.3164 0.8346 0.000 0.844 0.020 0.020 0.112 0.004
#> DRR006384 2 0.0146 0.9330 0.000 0.996 0.000 0.004 0.000 0.000
#> DRR006385 6 0.1225 0.8348 0.036 0.000 0.000 0.012 0.000 0.952
#> DRR006386 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006387 1 0.4325 0.6021 0.692 0.000 0.000 0.064 0.000 0.244
#> DRR006388 6 0.1442 0.8184 0.004 0.000 0.000 0.040 0.012 0.944
#> DRR006389 6 0.1442 0.8184 0.004 0.000 0.000 0.040 0.012 0.944
#> DRR006390 2 0.0622 0.9304 0.000 0.980 0.000 0.012 0.008 0.000
#> DRR006391 2 0.0622 0.9304 0.000 0.980 0.000 0.012 0.008 0.000
#> DRR006392 1 0.0291 0.8530 0.992 0.000 0.000 0.004 0.000 0.004
#> DRR006393 1 0.0291 0.8530 0.992 0.000 0.000 0.004 0.000 0.004
#> DRR006394 2 0.2737 0.8424 0.000 0.868 0.000 0.096 0.012 0.024
#> DRR006395 4 0.2573 0.9087 0.024 0.000 0.000 0.864 0.000 0.112
#> DRR006396 6 0.3817 0.5695 0.252 0.000 0.000 0.028 0.000 0.720
#> DRR006397 6 0.0767 0.8364 0.004 0.000 0.000 0.008 0.012 0.976
#> DRR006398 6 0.0767 0.8364 0.004 0.000 0.000 0.008 0.012 0.976
#> DRR006399 4 0.2480 0.9111 0.024 0.000 0.000 0.872 0.000 0.104
#> DRR006400 4 0.2480 0.9111 0.024 0.000 0.000 0.872 0.000 0.104
#> DRR006401 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006403 4 0.2480 0.9111 0.024 0.000 0.000 0.872 0.000 0.104
#> DRR006404 4 0.2558 0.9106 0.028 0.000 0.000 0.868 0.000 0.104
#> DRR006405 4 0.1320 0.8583 0.036 0.000 0.000 0.948 0.000 0.016
#> DRR006406 4 0.1320 0.8583 0.036 0.000 0.000 0.948 0.000 0.016
#> DRR006407 4 0.1364 0.8574 0.004 0.000 0.000 0.944 0.004 0.048
#> DRR006408 4 0.3927 0.4389 0.000 0.344 0.000 0.644 0.012 0.000
#> DRR006409 1 0.0363 0.8512 0.988 0.000 0.012 0.000 0.000 0.000
#> DRR006410 1 0.2897 0.7656 0.852 0.000 0.000 0.060 0.000 0.088
#> DRR006411 6 0.4552 0.3330 0.000 0.000 0.000 0.060 0.300 0.640
#> DRR006412 2 0.0520 0.9314 0.000 0.984 0.000 0.008 0.008 0.000
#> DRR006413 1 0.3979 0.2419 0.540 0.000 0.000 0.004 0.000 0.456
#> DRR006414 3 0.1297 0.7731 0.012 0.000 0.948 0.000 0.040 0.000
#> DRR006415 3 0.2772 0.7167 0.000 0.000 0.816 0.004 0.180 0.000
#> DRR006416 1 0.4871 0.4939 0.616 0.000 0.000 0.088 0.000 0.296
#> DRR006417 5 0.4094 0.7494 0.000 0.000 0.324 0.000 0.652 0.024
#> DRR006418 5 0.6115 0.6119 0.108 0.000 0.036 0.072 0.652 0.132
#> DRR006419 5 0.4274 0.7319 0.004 0.000 0.336 0.000 0.636 0.024
#> DRR006420 3 0.5478 0.0937 0.352 0.000 0.512 0.000 0.136 0.000
#> DRR006421 3 0.1524 0.7591 0.008 0.000 0.932 0.000 0.060 0.000
#> DRR006422 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006423 2 0.0260 0.9327 0.000 0.992 0.000 0.000 0.008 0.000
#> DRR006424 1 0.0622 0.8504 0.980 0.000 0.000 0.008 0.000 0.012
#> DRR006425 2 0.0405 0.9322 0.000 0.988 0.000 0.004 0.008 0.000
#> DRR006426 5 0.5291 0.7828 0.000 0.000 0.232 0.060 0.652 0.056
#> DRR006427 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006428 3 0.1320 0.7675 0.016 0.000 0.948 0.000 0.036 0.000
#> DRR006429 2 0.0622 0.9304 0.000 0.980 0.000 0.012 0.008 0.000
#> DRR006430 1 0.0146 0.8534 0.996 0.000 0.000 0.004 0.000 0.000
#> DRR006431 1 0.0146 0.8534 0.996 0.000 0.000 0.004 0.000 0.000
#> DRR006432 5 0.5222 0.7713 0.000 0.000 0.168 0.076 0.688 0.068
#> DRR006433 3 0.2488 0.7555 0.004 0.000 0.864 0.008 0.124 0.000
#> DRR006434 2 0.1889 0.8915 0.000 0.920 0.000 0.020 0.056 0.004
#> DRR006435 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006437 6 0.1700 0.8238 0.048 0.000 0.000 0.024 0.000 0.928
#> DRR006438 3 0.2768 0.6608 0.012 0.000 0.832 0.000 0.156 0.000
#> DRR006439 3 0.1657 0.7585 0.016 0.000 0.928 0.000 0.056 0.000
#> DRR006440 2 0.5282 0.5490 0.000 0.600 0.052 0.028 0.316 0.004
#> DRR006441 2 0.0622 0.9304 0.000 0.980 0.000 0.012 0.008 0.000
#> DRR006442 3 0.1500 0.7714 0.012 0.000 0.936 0.000 0.052 0.000
#> DRR006443 2 0.5282 0.5490 0.000 0.600 0.052 0.028 0.316 0.004
#> DRR006444 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006445 1 0.3982 0.2299 0.536 0.000 0.000 0.004 0.000 0.460
#> DRR006446 2 0.0520 0.9314 0.000 0.984 0.000 0.008 0.008 0.000
#> DRR006447 6 0.2582 0.7768 0.032 0.000 0.000 0.060 0.020 0.888
#> DRR006448 4 0.2527 0.9091 0.024 0.000 0.000 0.868 0.000 0.108
#> DRR006449 1 0.2199 0.8077 0.892 0.000 0.000 0.020 0.000 0.088
#> DRR006450 1 0.3975 0.2523 0.544 0.000 0.000 0.004 0.000 0.452
#> DRR006451 4 0.2404 0.9072 0.016 0.000 0.000 0.872 0.000 0.112
#> DRR006452 1 0.3975 0.2523 0.544 0.000 0.000 0.004 0.000 0.452
#> DRR006453 1 0.2838 0.7276 0.808 0.000 0.000 0.004 0.000 0.188
#> DRR006454 6 0.0964 0.8365 0.004 0.000 0.000 0.016 0.012 0.968
#> DRR006455 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006456 3 0.3342 0.6773 0.000 0.000 0.760 0.012 0.228 0.000
#> DRR006457 3 0.1333 0.7655 0.008 0.000 0.944 0.000 0.048 0.000
#> DRR006458 1 0.0146 0.8528 0.996 0.000 0.004 0.000 0.000 0.000
#> DRR006459 1 0.0363 0.8512 0.988 0.000 0.012 0.000 0.000 0.000
#> DRR006460 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006461 2 0.2237 0.8749 0.000 0.896 0.000 0.020 0.080 0.004
#> DRR006462 6 0.4911 0.4801 0.276 0.000 0.000 0.100 0.000 0.624
#> DRR006463 2 0.5282 0.5490 0.000 0.600 0.052 0.028 0.316 0.004
#> DRR006464 5 0.5662 0.5393 0.000 0.184 0.000 0.100 0.644 0.072
#> DRR006465 1 0.0146 0.8534 0.996 0.000 0.000 0.004 0.000 0.000
#> DRR006466 3 0.4119 0.5776 0.000 0.000 0.644 0.016 0.336 0.004
#> DRR006467 1 0.0146 0.8534 0.996 0.000 0.000 0.004 0.000 0.000
#> DRR006468 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006469 2 0.0622 0.9304 0.000 0.980 0.000 0.012 0.008 0.000
#> DRR006470 5 0.4536 0.7690 0.000 0.000 0.300 0.012 0.652 0.036
#> DRR006471 1 0.0458 0.8500 0.984 0.000 0.016 0.000 0.000 0.000
#> DRR006472 5 0.3101 0.7062 0.000 0.000 0.244 0.000 0.756 0.000
#> DRR006473 2 0.0260 0.9327 0.000 0.992 0.000 0.000 0.008 0.000
#> DRR006474 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006475 1 0.1285 0.8275 0.944 0.000 0.052 0.000 0.004 0.000
#> DRR006476 2 0.0717 0.9287 0.000 0.976 0.000 0.008 0.016 0.000
#> DRR006477 3 0.4367 0.6195 0.000 0.008 0.696 0.048 0.248 0.000
#> DRR006478 1 0.0146 0.8534 0.996 0.000 0.000 0.004 0.000 0.000
#> DRR006479 3 0.4340 0.5021 0.176 0.000 0.720 0.000 0.104 0.000
#> DRR006480 1 0.0458 0.8500 0.984 0.000 0.016 0.000 0.000 0.000
#> DRR006481 3 0.2664 0.6297 0.000 0.000 0.816 0.000 0.184 0.000
#> DRR006482 6 0.1334 0.8334 0.032 0.000 0.000 0.020 0.000 0.948
#> DRR006483 1 0.0363 0.8512 0.988 0.000 0.012 0.000 0.000 0.000
#> DRR006484 3 0.1686 0.7550 0.012 0.000 0.924 0.000 0.064 0.000
#> DRR006485 2 0.5282 0.5490 0.000 0.600 0.052 0.028 0.316 0.004
#> DRR006486 1 0.2191 0.7713 0.876 0.000 0.120 0.000 0.004 0.000
#> DRR006487 3 0.2871 0.7094 0.000 0.000 0.804 0.004 0.192 0.000
#> DRR006488 2 0.0000 0.9338 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006489 1 0.0291 0.8530 0.992 0.000 0.000 0.004 0.000 0.004
#> DRR006490 3 0.1391 0.7642 0.016 0.000 0.944 0.000 0.040 0.000
#> DRR006491 3 0.0508 0.7734 0.012 0.000 0.984 0.000 0.004 0.000
#> DRR006492 1 0.2362 0.7598 0.860 0.000 0.136 0.000 0.004 0.000
#> DRR006493 3 0.2838 0.7122 0.000 0.000 0.808 0.004 0.188 0.000
#> DRR006494 1 0.0458 0.8500 0.984 0.000 0.016 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)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.984 0.993 0.4361 0.566 0.566
#> 3 3 0.588 0.831 0.850 0.4792 0.731 0.538
#> 4 4 0.743 0.816 0.915 0.0822 0.968 0.903
#> 5 5 0.775 0.794 0.900 0.1181 0.812 0.465
#> 6 6 0.808 0.843 0.911 0.0474 0.921 0.670
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
#> DRR006374 2 0.000 0.991 0.000 1.000
#> DRR006375 1 0.000 0.993 1.000 0.000
#> DRR006376 1 0.000 0.993 1.000 0.000
#> DRR006377 1 0.000 0.993 1.000 0.000
#> DRR006378 2 0.000 0.991 0.000 1.000
#> DRR006379 1 0.000 0.993 1.000 0.000
#> DRR006380 2 0.000 0.991 0.000 1.000
#> DRR006381 1 0.000 0.993 1.000 0.000
#> DRR006382 2 0.000 0.991 0.000 1.000
#> DRR006383 2 0.653 0.801 0.168 0.832
#> DRR006384 2 0.000 0.991 0.000 1.000
#> DRR006385 1 0.000 0.993 1.000 0.000
#> DRR006386 2 0.000 0.991 0.000 1.000
#> DRR006387 1 0.000 0.993 1.000 0.000
#> DRR006388 1 0.000 0.993 1.000 0.000
#> DRR006389 1 0.000 0.993 1.000 0.000
#> DRR006390 2 0.000 0.991 0.000 1.000
#> DRR006391 2 0.000 0.991 0.000 1.000
#> DRR006392 1 0.000 0.993 1.000 0.000
#> DRR006393 1 0.000 0.993 1.000 0.000
#> DRR006394 2 0.000 0.991 0.000 1.000
#> DRR006395 1 0.000 0.993 1.000 0.000
#> DRR006396 1 0.000 0.993 1.000 0.000
#> DRR006397 1 0.000 0.993 1.000 0.000
#> DRR006398 1 0.000 0.993 1.000 0.000
#> DRR006399 1 0.000 0.993 1.000 0.000
#> DRR006400 1 0.000 0.993 1.000 0.000
#> DRR006401 2 0.000 0.991 0.000 1.000
#> DRR006402 2 0.000 0.991 0.000 1.000
#> DRR006403 1 0.000 0.993 1.000 0.000
#> DRR006404 1 0.000 0.993 1.000 0.000
#> DRR006405 1 0.000 0.993 1.000 0.000
#> DRR006406 1 0.000 0.993 1.000 0.000
#> DRR006407 1 0.000 0.993 1.000 0.000
#> DRR006408 2 0.000 0.991 0.000 1.000
#> DRR006409 1 0.000 0.993 1.000 0.000
#> DRR006410 1 0.000 0.993 1.000 0.000
#> DRR006411 1 0.000 0.993 1.000 0.000
#> DRR006412 2 0.000 0.991 0.000 1.000
#> DRR006413 1 0.000 0.993 1.000 0.000
#> DRR006414 1 0.000 0.993 1.000 0.000
#> DRR006415 1 0.000 0.993 1.000 0.000
#> DRR006416 1 0.000 0.993 1.000 0.000
#> DRR006417 1 0.000 0.993 1.000 0.000
#> DRR006418 1 0.000 0.993 1.000 0.000
#> DRR006419 1 0.000 0.993 1.000 0.000
#> DRR006420 1 0.000 0.993 1.000 0.000
#> DRR006421 1 0.000 0.993 1.000 0.000
#> DRR006422 2 0.000 0.991 0.000 1.000
#> DRR006423 2 0.000 0.991 0.000 1.000
#> DRR006424 1 0.000 0.993 1.000 0.000
#> DRR006425 2 0.000 0.991 0.000 1.000
#> DRR006426 1 0.000 0.993 1.000 0.000
#> DRR006427 2 0.000 0.991 0.000 1.000
#> DRR006428 1 0.000 0.993 1.000 0.000
#> DRR006429 2 0.000 0.991 0.000 1.000
#> DRR006430 1 0.000 0.993 1.000 0.000
#> DRR006431 1 0.000 0.993 1.000 0.000
#> DRR006432 1 0.000 0.993 1.000 0.000
#> DRR006433 1 0.000 0.993 1.000 0.000
#> DRR006434 2 0.000 0.991 0.000 1.000
#> DRR006435 2 0.000 0.991 0.000 1.000
#> DRR006436 2 0.000 0.991 0.000 1.000
#> DRR006437 1 0.000 0.993 1.000 0.000
#> DRR006438 1 0.000 0.993 1.000 0.000
#> DRR006439 1 0.000 0.993 1.000 0.000
#> DRR006440 2 0.000 0.991 0.000 1.000
#> DRR006441 2 0.000 0.991 0.000 1.000
#> DRR006442 1 0.000 0.993 1.000 0.000
#> DRR006443 2 0.000 0.991 0.000 1.000
#> DRR006444 2 0.000 0.991 0.000 1.000
#> DRR006445 1 0.000 0.993 1.000 0.000
#> DRR006446 2 0.000 0.991 0.000 1.000
#> DRR006447 1 0.000 0.993 1.000 0.000
#> DRR006448 1 0.000 0.993 1.000 0.000
#> DRR006449 1 0.000 0.993 1.000 0.000
#> DRR006450 1 0.000 0.993 1.000 0.000
#> DRR006451 1 0.000 0.993 1.000 0.000
#> DRR006452 1 0.000 0.993 1.000 0.000
#> DRR006453 1 0.000 0.993 1.000 0.000
#> DRR006454 1 0.000 0.993 1.000 0.000
#> DRR006455 2 0.000 0.991 0.000 1.000
#> DRR006456 1 0.311 0.936 0.944 0.056
#> DRR006457 1 0.000 0.993 1.000 0.000
#> DRR006458 1 0.000 0.993 1.000 0.000
#> DRR006459 1 0.000 0.993 1.000 0.000
#> DRR006460 2 0.000 0.991 0.000 1.000
#> DRR006461 2 0.000 0.991 0.000 1.000
#> DRR006462 1 0.000 0.993 1.000 0.000
#> DRR006463 2 0.000 0.991 0.000 1.000
#> DRR006464 1 0.876 0.580 0.704 0.296
#> DRR006465 1 0.000 0.993 1.000 0.000
#> DRR006466 1 0.000 0.993 1.000 0.000
#> DRR006467 1 0.000 0.993 1.000 0.000
#> DRR006468 2 0.000 0.991 0.000 1.000
#> DRR006469 2 0.000 0.991 0.000 1.000
#> DRR006470 1 0.000 0.993 1.000 0.000
#> DRR006471 1 0.000 0.993 1.000 0.000
#> DRR006472 1 0.000 0.993 1.000 0.000
#> DRR006473 2 0.000 0.991 0.000 1.000
#> DRR006474 2 0.000 0.991 0.000 1.000
#> DRR006475 1 0.000 0.993 1.000 0.000
#> DRR006476 2 0.662 0.796 0.172 0.828
#> DRR006477 1 0.697 0.766 0.812 0.188
#> DRR006478 1 0.000 0.993 1.000 0.000
#> DRR006479 1 0.000 0.993 1.000 0.000
#> DRR006480 1 0.000 0.993 1.000 0.000
#> DRR006481 1 0.000 0.993 1.000 0.000
#> DRR006482 1 0.000 0.993 1.000 0.000
#> DRR006483 1 0.000 0.993 1.000 0.000
#> DRR006484 1 0.000 0.993 1.000 0.000
#> DRR006485 2 0.000 0.991 0.000 1.000
#> DRR006486 1 0.000 0.993 1.000 0.000
#> DRR006487 1 0.000 0.993 1.000 0.000
#> DRR006488 2 0.000 0.991 0.000 1.000
#> DRR006489 1 0.000 0.993 1.000 0.000
#> DRR006490 1 0.000 0.993 1.000 0.000
#> DRR006491 1 0.000 0.993 1.000 0.000
#> DRR006492 1 0.000 0.993 1.000 0.000
#> DRR006493 1 0.000 0.993 1.000 0.000
#> DRR006494 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
#> DRR006374 2 0.0000 0.914 0.000 1.000 0.000
#> DRR006375 1 0.5216 0.450 0.740 0.000 0.260
#> DRR006376 1 0.3752 0.718 0.856 0.000 0.144
#> DRR006377 3 0.3116 0.865 0.108 0.000 0.892
#> DRR006378 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006379 1 0.4291 0.755 0.820 0.000 0.180
#> DRR006380 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006381 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006382 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006383 2 0.6267 0.232 0.000 0.548 0.452
#> DRR006384 2 0.0000 0.914 0.000 1.000 0.000
#> DRR006385 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006386 2 0.3116 0.880 0.000 0.892 0.108
#> DRR006387 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006388 1 0.5016 0.712 0.760 0.000 0.240
#> DRR006389 1 0.5016 0.712 0.760 0.000 0.240
#> DRR006390 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006391 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006392 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006393 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006394 2 0.2959 0.887 0.000 0.900 0.100
#> DRR006395 3 0.3941 0.876 0.156 0.000 0.844
#> DRR006396 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006397 1 0.4974 0.715 0.764 0.000 0.236
#> DRR006398 1 0.4974 0.715 0.764 0.000 0.236
#> DRR006399 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006400 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006401 2 0.0000 0.914 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.914 0.000 1.000 0.000
#> DRR006403 3 0.6045 0.704 0.380 0.000 0.620
#> DRR006404 3 0.5835 0.732 0.340 0.000 0.660
#> DRR006405 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006406 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006407 3 0.3619 0.855 0.136 0.000 0.864
#> DRR006408 1 0.7637 0.420 0.616 0.320 0.064
#> DRR006409 3 0.5926 0.735 0.356 0.000 0.644
#> DRR006410 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006411 1 0.5016 0.709 0.760 0.000 0.240
#> DRR006412 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006413 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006414 3 0.3686 0.878 0.140 0.000 0.860
#> DRR006415 3 0.3116 0.865 0.108 0.000 0.892
#> DRR006416 1 0.5058 0.706 0.756 0.000 0.244
#> DRR006417 3 0.3816 0.879 0.148 0.000 0.852
#> DRR006418 1 0.4062 0.771 0.836 0.000 0.164
#> DRR006419 3 0.3816 0.879 0.148 0.000 0.852
#> DRR006420 3 0.3752 0.879 0.144 0.000 0.856
#> DRR006421 3 0.3116 0.865 0.108 0.000 0.892
#> DRR006422 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006423 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006424 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006425 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006426 3 0.3816 0.879 0.148 0.000 0.852
#> DRR006427 2 0.3116 0.880 0.000 0.892 0.108
#> DRR006428 3 0.3816 0.879 0.148 0.000 0.852
#> DRR006429 2 0.5291 0.705 0.000 0.732 0.268
#> DRR006430 1 0.1860 0.845 0.948 0.000 0.052
#> DRR006431 3 0.5926 0.735 0.356 0.000 0.644
#> DRR006432 3 0.3116 0.865 0.108 0.000 0.892
#> DRR006433 3 0.3116 0.865 0.108 0.000 0.892
#> DRR006434 2 0.1411 0.922 0.000 0.964 0.036
#> DRR006435 2 0.3116 0.880 0.000 0.892 0.108
#> DRR006436 2 0.3116 0.880 0.000 0.892 0.108
#> DRR006437 1 0.0237 0.888 0.996 0.000 0.004
#> DRR006438 3 0.3816 0.879 0.148 0.000 0.852
#> DRR006439 3 0.3816 0.879 0.148 0.000 0.852
#> DRR006440 2 0.5291 0.706 0.000 0.732 0.268
#> DRR006441 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006442 3 0.3686 0.878 0.140 0.000 0.860
#> DRR006443 2 0.4002 0.832 0.000 0.840 0.160
#> DRR006444 2 0.3116 0.880 0.000 0.892 0.108
#> DRR006445 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006446 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006447 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006448 1 0.1163 0.871 0.972 0.000 0.028
#> DRR006449 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006450 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006451 1 0.1860 0.862 0.948 0.000 0.052
#> DRR006452 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006453 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006454 1 0.5138 0.697 0.748 0.000 0.252
#> DRR006455 2 0.3116 0.880 0.000 0.892 0.108
#> DRR006456 3 0.3713 0.836 0.076 0.032 0.892
#> DRR006457 3 0.3412 0.873 0.124 0.000 0.876
#> DRR006458 3 0.5926 0.735 0.356 0.000 0.644
#> DRR006459 3 0.5926 0.735 0.356 0.000 0.644
#> DRR006460 2 0.0000 0.914 0.000 1.000 0.000
#> DRR006461 2 0.1411 0.922 0.000 0.964 0.036
#> DRR006462 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006463 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006464 2 0.9606 0.134 0.208 0.440 0.352
#> DRR006465 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006466 3 0.3116 0.865 0.108 0.000 0.892
#> DRR006467 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006468 2 0.3116 0.880 0.000 0.892 0.108
#> DRR006469 2 0.1753 0.919 0.000 0.952 0.048
#> DRR006470 3 0.3816 0.879 0.148 0.000 0.852
#> DRR006471 3 0.6079 0.692 0.388 0.000 0.612
#> DRR006472 3 0.3116 0.865 0.108 0.000 0.892
#> DRR006473 2 0.1529 0.922 0.000 0.960 0.040
#> DRR006474 2 0.1411 0.922 0.000 0.964 0.036
#> DRR006475 3 0.5926 0.735 0.356 0.000 0.644
#> DRR006476 3 0.4504 0.643 0.000 0.196 0.804
#> DRR006477 3 0.3375 0.759 0.008 0.100 0.892
#> DRR006478 3 0.5988 0.720 0.368 0.000 0.632
#> DRR006479 3 0.4605 0.851 0.204 0.000 0.796
#> DRR006480 3 0.5926 0.735 0.356 0.000 0.644
#> DRR006481 3 0.3686 0.878 0.140 0.000 0.860
#> DRR006482 1 0.5098 0.700 0.752 0.000 0.248
#> DRR006483 3 0.5926 0.735 0.356 0.000 0.644
#> DRR006484 3 0.3816 0.879 0.148 0.000 0.852
#> DRR006485 2 0.2165 0.911 0.000 0.936 0.064
#> DRR006486 3 0.5905 0.738 0.352 0.000 0.648
#> DRR006487 3 0.3116 0.865 0.108 0.000 0.892
#> DRR006488 2 0.3116 0.880 0.000 0.892 0.108
#> DRR006489 1 0.0000 0.891 1.000 0.000 0.000
#> DRR006490 3 0.3816 0.879 0.148 0.000 0.852
#> DRR006491 3 0.3816 0.879 0.148 0.000 0.852
#> DRR006492 3 0.4002 0.875 0.160 0.000 0.840
#> DRR006493 3 0.3116 0.865 0.108 0.000 0.892
#> DRR006494 3 0.5926 0.735 0.356 0.000 0.644
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006375 1 0.4134 0.533 0.740 0.000 0.260 0
#> DRR006376 1 0.2973 0.742 0.856 0.000 0.144 0
#> DRR006377 3 0.0592 0.868 0.016 0.000 0.984 0
#> DRR006378 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006379 1 0.3528 0.747 0.808 0.000 0.192 0
#> DRR006380 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006381 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006382 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006383 2 0.4888 0.254 0.000 0.588 0.412 0
#> DRR006384 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006385 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006386 4 0.0000 1.000 0.000 0.000 0.000 1
#> DRR006387 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006388 1 0.4164 0.684 0.736 0.000 0.264 0
#> DRR006389 1 0.4164 0.684 0.736 0.000 0.264 0
#> DRR006390 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006391 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006392 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006393 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006394 2 0.2973 0.768 0.000 0.856 0.144 0
#> DRR006395 3 0.0921 0.864 0.028 0.000 0.972 0
#> DRR006396 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006397 1 0.4164 0.684 0.736 0.000 0.264 0
#> DRR006398 1 0.4164 0.684 0.736 0.000 0.264 0
#> DRR006399 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006400 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006401 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006402 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006403 3 0.4543 0.648 0.324 0.000 0.676 0
#> DRR006404 3 0.4661 0.576 0.348 0.000 0.652 0
#> DRR006405 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006406 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006407 3 0.1940 0.828 0.076 0.000 0.924 0
#> DRR006408 1 0.5452 0.423 0.616 0.360 0.024 0
#> DRR006409 3 0.4356 0.689 0.292 0.000 0.708 0
#> DRR006410 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006411 1 0.4250 0.670 0.724 0.000 0.276 0
#> DRR006412 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006413 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006414 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006415 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006416 1 0.4222 0.675 0.728 0.000 0.272 0
#> DRR006417 3 0.0592 0.868 0.016 0.000 0.984 0
#> DRR006418 1 0.3907 0.712 0.768 0.000 0.232 0
#> DRR006419 3 0.0592 0.868 0.016 0.000 0.984 0
#> DRR006420 3 0.0592 0.868 0.016 0.000 0.984 0
#> DRR006421 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006422 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006423 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006424 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006425 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006426 3 0.0592 0.868 0.016 0.000 0.984 0
#> DRR006427 4 0.0000 1.000 0.000 0.000 0.000 1
#> DRR006428 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006429 2 0.4730 0.500 0.000 0.636 0.364 0
#> DRR006430 1 0.1474 0.837 0.948 0.000 0.052 0
#> DRR006431 3 0.4356 0.689 0.292 0.000 0.708 0
#> DRR006432 3 0.0592 0.868 0.016 0.000 0.984 0
#> DRR006433 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006434 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006435 4 0.0000 1.000 0.000 0.000 0.000 1
#> DRR006436 4 0.0000 1.000 0.000 0.000 0.000 1
#> DRR006437 1 0.0707 0.863 0.980 0.000 0.020 0
#> DRR006438 3 0.0592 0.868 0.016 0.000 0.984 0
#> DRR006439 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006440 2 0.4746 0.495 0.000 0.632 0.368 0
#> DRR006441 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006442 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006443 2 0.3649 0.698 0.000 0.796 0.204 0
#> DRR006444 4 0.0000 1.000 0.000 0.000 0.000 1
#> DRR006445 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006446 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006447 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006448 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006449 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006450 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006451 1 0.1474 0.848 0.948 0.000 0.052 0
#> DRR006452 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006453 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006454 1 0.4406 0.645 0.700 0.000 0.300 0
#> DRR006455 4 0.0000 1.000 0.000 0.000 0.000 1
#> DRR006456 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006457 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006458 3 0.4356 0.689 0.292 0.000 0.708 0
#> DRR006459 3 0.4356 0.689 0.292 0.000 0.708 0
#> DRR006460 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006461 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006462 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006463 2 0.0592 0.885 0.000 0.984 0.016 0
#> DRR006464 2 0.7416 0.205 0.168 0.440 0.392 0
#> DRR006465 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006466 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006467 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006468 4 0.0000 1.000 0.000 0.000 0.000 1
#> DRR006469 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006470 3 0.0592 0.868 0.016 0.000 0.984 0
#> DRR006471 3 0.4817 0.541 0.388 0.000 0.612 0
#> DRR006472 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006473 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006474 2 0.0000 0.896 0.000 1.000 0.000 0
#> DRR006475 3 0.4356 0.689 0.292 0.000 0.708 0
#> DRR006476 3 0.3074 0.721 0.000 0.152 0.848 0
#> DRR006477 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006478 3 0.4431 0.675 0.304 0.000 0.696 0
#> DRR006479 3 0.1867 0.842 0.072 0.000 0.928 0
#> DRR006480 3 0.4356 0.689 0.292 0.000 0.708 0
#> DRR006481 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006482 1 0.4643 0.588 0.656 0.000 0.344 0
#> DRR006483 3 0.4356 0.689 0.292 0.000 0.708 0
#> DRR006484 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006485 2 0.2469 0.804 0.000 0.892 0.108 0
#> DRR006486 3 0.4304 0.695 0.284 0.000 0.716 0
#> DRR006487 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006488 4 0.0000 1.000 0.000 0.000 0.000 1
#> DRR006489 1 0.0000 0.876 1.000 0.000 0.000 0
#> DRR006490 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006491 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006492 3 0.0921 0.865 0.028 0.000 0.972 0
#> DRR006493 3 0.0000 0.867 0.000 0.000 1.000 0
#> DRR006494 3 0.4356 0.689 0.292 0.000 0.708 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006375 1 0.1671 0.81380 0.924 0.000 0.000 0.076 0
#> DRR006376 1 0.5341 0.24364 0.564 0.000 0.376 0.060 0
#> DRR006377 3 0.1341 0.88237 0.000 0.000 0.944 0.056 0
#> DRR006378 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006379 4 0.0290 0.77710 0.008 0.000 0.000 0.992 0
#> DRR006380 2 0.0510 0.93824 0.000 0.984 0.000 0.016 0
#> DRR006381 1 0.2852 0.76321 0.828 0.000 0.000 0.172 0
#> DRR006382 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006383 4 0.4656 0.14268 0.000 0.480 0.012 0.508 0
#> DRR006384 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006385 4 0.1341 0.77768 0.056 0.000 0.000 0.944 0
#> DRR006386 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006387 1 0.2891 0.76059 0.824 0.000 0.000 0.176 0
#> DRR006388 4 0.1410 0.79444 0.000 0.000 0.060 0.940 0
#> DRR006389 4 0.1410 0.79444 0.000 0.000 0.060 0.940 0
#> DRR006390 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006391 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006392 1 0.0000 0.83632 1.000 0.000 0.000 0.000 0
#> DRR006393 1 0.1197 0.82452 0.952 0.000 0.000 0.048 0
#> DRR006394 4 0.6237 0.48319 0.000 0.276 0.188 0.536 0
#> DRR006395 3 0.4393 0.73033 0.076 0.000 0.756 0.168 0
#> DRR006396 4 0.4294 0.00952 0.468 0.000 0.000 0.532 0
#> DRR006397 4 0.1341 0.79373 0.000 0.000 0.056 0.944 0
#> DRR006398 4 0.1341 0.79373 0.000 0.000 0.056 0.944 0
#> DRR006399 1 0.6778 0.27062 0.380 0.000 0.280 0.340 0
#> DRR006400 1 0.6755 0.24691 0.376 0.000 0.264 0.360 0
#> DRR006401 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006402 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006403 3 0.6537 -0.15370 0.400 0.000 0.404 0.196 0
#> DRR006404 4 0.0609 0.77739 0.020 0.000 0.000 0.980 0
#> DRR006405 4 0.3039 0.70766 0.192 0.000 0.000 0.808 0
#> DRR006406 4 0.3039 0.70766 0.192 0.000 0.000 0.808 0
#> DRR006407 4 0.2929 0.74059 0.000 0.000 0.180 0.820 0
#> DRR006408 4 0.3109 0.66990 0.000 0.200 0.000 0.800 0
#> DRR006409 1 0.4294 0.05273 0.532 0.000 0.468 0.000 0
#> DRR006410 1 0.2891 0.76059 0.824 0.000 0.000 0.176 0
#> DRR006411 4 0.1732 0.79398 0.000 0.000 0.080 0.920 0
#> DRR006412 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006413 4 0.4291 0.02193 0.464 0.000 0.000 0.536 0
#> DRR006414 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006415 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006416 4 0.3074 0.75215 0.000 0.000 0.196 0.804 0
#> DRR006417 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006418 4 0.3779 0.76862 0.052 0.000 0.144 0.804 0
#> DRR006419 3 0.0880 0.91105 0.032 0.000 0.968 0.000 0
#> DRR006420 3 0.0992 0.90722 0.008 0.000 0.968 0.024 0
#> DRR006421 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006422 2 0.3109 0.70387 0.000 0.800 0.000 0.200 0
#> DRR006423 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006424 1 0.2891 0.76059 0.824 0.000 0.000 0.176 0
#> DRR006425 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006426 3 0.0609 0.91587 0.020 0.000 0.980 0.000 0
#> DRR006427 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006428 3 0.2852 0.77815 0.172 0.000 0.828 0.000 0
#> DRR006429 4 0.3395 0.72755 0.000 0.000 0.236 0.764 0
#> DRR006430 1 0.0000 0.83632 1.000 0.000 0.000 0.000 0
#> DRR006431 1 0.0000 0.83632 1.000 0.000 0.000 0.000 0
#> DRR006432 4 0.3977 0.73461 0.032 0.000 0.204 0.764 0
#> DRR006433 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006434 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006435 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006436 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006437 4 0.3508 0.57376 0.252 0.000 0.000 0.748 0
#> DRR006438 3 0.2813 0.78290 0.168 0.000 0.832 0.000 0
#> DRR006439 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006440 2 0.3508 0.65804 0.000 0.748 0.252 0.000 0
#> DRR006441 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006442 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006443 2 0.3143 0.72457 0.000 0.796 0.204 0.000 0
#> DRR006444 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006445 4 0.1544 0.77511 0.068 0.000 0.000 0.932 0
#> DRR006446 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006447 4 0.2104 0.78764 0.060 0.000 0.024 0.916 0
#> DRR006448 4 0.1197 0.76650 0.048 0.000 0.000 0.952 0
#> DRR006449 1 0.1043 0.82615 0.960 0.000 0.000 0.040 0
#> DRR006450 1 0.1965 0.80047 0.904 0.000 0.000 0.096 0
#> DRR006451 4 0.0290 0.77710 0.008 0.000 0.000 0.992 0
#> DRR006452 1 0.2891 0.76059 0.824 0.000 0.000 0.176 0
#> DRR006453 4 0.3366 0.69562 0.232 0.000 0.000 0.768 0
#> DRR006454 4 0.3395 0.72755 0.000 0.000 0.236 0.764 0
#> DRR006455 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006456 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006457 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006458 1 0.0000 0.83632 1.000 0.000 0.000 0.000 0
#> DRR006459 1 0.0510 0.83175 0.984 0.000 0.016 0.000 0
#> DRR006460 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006461 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006462 1 0.2929 0.75793 0.820 0.000 0.000 0.180 0
#> DRR006463 2 0.0880 0.92491 0.000 0.968 0.032 0.000 0
#> DRR006464 4 0.3395 0.72755 0.000 0.000 0.236 0.764 0
#> DRR006465 1 0.0162 0.83577 0.996 0.000 0.004 0.000 0
#> DRR006466 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006467 1 0.0000 0.83632 1.000 0.000 0.000 0.000 0
#> DRR006468 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006469 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006470 3 0.0880 0.91105 0.032 0.000 0.968 0.000 0
#> DRR006471 1 0.0162 0.83577 0.996 0.000 0.004 0.000 0
#> DRR006472 3 0.3177 0.64097 0.000 0.000 0.792 0.208 0
#> DRR006473 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006474 2 0.0000 0.95234 0.000 1.000 0.000 0.000 0
#> DRR006475 1 0.0404 0.83323 0.988 0.000 0.012 0.000 0
#> DRR006476 4 0.3395 0.72755 0.000 0.000 0.236 0.764 0
#> DRR006477 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006478 1 0.0000 0.83632 1.000 0.000 0.000 0.000 0
#> DRR006479 3 0.3274 0.71002 0.220 0.000 0.780 0.000 0
#> DRR006480 1 0.0290 0.83451 0.992 0.000 0.008 0.000 0
#> DRR006481 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006482 4 0.1671 0.79362 0.000 0.000 0.076 0.924 0
#> DRR006483 1 0.0290 0.83451 0.992 0.000 0.008 0.000 0
#> DRR006484 3 0.0880 0.91105 0.032 0.000 0.968 0.000 0
#> DRR006485 2 0.2852 0.76508 0.000 0.828 0.172 0.000 0
#> DRR006486 1 0.4126 0.31026 0.620 0.000 0.380 0.000 0
#> DRR006487 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006488 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006489 1 0.0000 0.83632 1.000 0.000 0.000 0.000 0
#> DRR006490 3 0.0880 0.91105 0.032 0.000 0.968 0.000 0
#> DRR006491 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006492 3 0.1197 0.90178 0.048 0.000 0.952 0.000 0
#> DRR006493 3 0.0000 0.92137 0.000 0.000 1.000 0.000 0
#> DRR006494 1 0.1121 0.81437 0.956 0.000 0.044 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006375 1 0.1663 0.8575 0.912 0.000 0.000 0.088 0 0.000
#> DRR006376 4 0.2697 0.7260 0.188 0.000 0.000 0.812 0 0.000
#> DRR006377 4 0.3572 0.7140 0.032 0.000 0.204 0.764 0 0.000
#> DRR006378 2 0.0146 0.9515 0.000 0.996 0.000 0.000 0 0.004
#> DRR006379 4 0.0363 0.8231 0.000 0.000 0.000 0.988 0 0.012
#> DRR006380 2 0.0260 0.9468 0.000 0.992 0.000 0.000 0 0.008
#> DRR006381 1 0.2768 0.8192 0.832 0.000 0.000 0.156 0 0.012
#> DRR006382 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006383 6 0.3868 0.0835 0.000 0.496 0.000 0.000 0 0.504
#> DRR006384 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006385 6 0.2669 0.8078 0.008 0.000 0.000 0.156 0 0.836
#> DRR006386 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006387 1 0.3176 0.8068 0.812 0.000 0.000 0.156 0 0.032
#> DRR006388 6 0.2442 0.8192 0.000 0.000 0.004 0.144 0 0.852
#> DRR006389 6 0.2442 0.8192 0.000 0.000 0.004 0.144 0 0.852
#> DRR006390 2 0.0146 0.9515 0.000 0.996 0.000 0.000 0 0.004
#> DRR006391 2 0.0146 0.9515 0.000 0.996 0.000 0.000 0 0.004
#> DRR006392 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006393 1 0.1364 0.8634 0.944 0.000 0.004 0.004 0 0.048
#> DRR006394 6 0.5249 0.5127 0.000 0.244 0.156 0.000 0 0.600
#> DRR006395 4 0.3753 0.7567 0.016 0.000 0.156 0.788 0 0.040
#> DRR006396 1 0.3542 0.7911 0.788 0.000 0.000 0.160 0 0.052
#> DRR006397 6 0.2340 0.8174 0.000 0.000 0.000 0.148 0 0.852
#> DRR006398 6 0.2340 0.8174 0.000 0.000 0.000 0.148 0 0.852
#> DRR006399 4 0.0363 0.8231 0.000 0.000 0.000 0.988 0 0.012
#> DRR006400 4 0.0363 0.8231 0.000 0.000 0.000 0.988 0 0.012
#> DRR006401 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006402 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006403 4 0.0363 0.8207 0.012 0.000 0.000 0.988 0 0.000
#> DRR006404 4 0.0363 0.8231 0.000 0.000 0.000 0.988 0 0.012
#> DRR006405 4 0.3947 0.7588 0.136 0.000 0.000 0.764 0 0.100
#> DRR006406 4 0.3893 0.7512 0.156 0.000 0.000 0.764 0 0.080
#> DRR006407 4 0.3877 0.7330 0.000 0.000 0.160 0.764 0 0.076
#> DRR006408 4 0.3649 0.6773 0.000 0.196 0.000 0.764 0 0.040
#> DRR006409 3 0.2730 0.8051 0.192 0.000 0.808 0.000 0 0.000
#> DRR006410 1 0.2768 0.8192 0.832 0.000 0.000 0.156 0 0.012
#> DRR006411 6 0.0865 0.8248 0.000 0.000 0.000 0.036 0 0.964
#> DRR006412 2 0.0146 0.9515 0.000 0.996 0.000 0.000 0 0.004
#> DRR006413 1 0.3344 0.8011 0.804 0.000 0.000 0.152 0 0.044
#> DRR006414 3 0.0000 0.8993 0.000 0.000 1.000 0.000 0 0.000
#> DRR006415 3 0.0146 0.8988 0.000 0.000 0.996 0.000 0 0.004
#> DRR006416 6 0.0865 0.8211 0.000 0.000 0.036 0.000 0 0.964
#> DRR006417 3 0.2613 0.8573 0.000 0.000 0.848 0.012 0 0.140
#> DRR006418 6 0.0260 0.8153 0.000 0.000 0.008 0.000 0 0.992
#> DRR006419 3 0.2768 0.8502 0.000 0.000 0.832 0.012 0 0.156
#> DRR006420 3 0.2768 0.8502 0.000 0.000 0.832 0.012 0 0.156
#> DRR006421 3 0.0000 0.8993 0.000 0.000 1.000 0.000 0 0.000
#> DRR006422 2 0.2793 0.7033 0.000 0.800 0.000 0.000 0 0.200
#> DRR006423 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006424 1 0.2768 0.8192 0.832 0.000 0.000 0.156 0 0.012
#> DRR006425 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006426 3 0.2872 0.8513 0.004 0.000 0.832 0.012 0 0.152
#> DRR006427 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006428 3 0.3219 0.8461 0.132 0.000 0.828 0.012 0 0.028
#> DRR006429 6 0.2597 0.7609 0.000 0.000 0.176 0.000 0 0.824
#> DRR006430 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006431 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006432 6 0.0622 0.8086 0.000 0.000 0.008 0.012 0 0.980
#> DRR006433 3 0.0000 0.8993 0.000 0.000 1.000 0.000 0 0.000
#> DRR006434 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006435 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006436 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006437 1 0.5406 0.4464 0.568 0.000 0.000 0.160 0 0.272
#> DRR006438 3 0.3219 0.8461 0.132 0.000 0.828 0.012 0 0.028
#> DRR006439 3 0.0000 0.8993 0.000 0.000 1.000 0.000 0 0.000
#> DRR006440 2 0.2793 0.7654 0.000 0.800 0.200 0.000 0 0.000
#> DRR006441 2 0.0146 0.9515 0.000 0.996 0.000 0.000 0 0.004
#> DRR006442 3 0.0146 0.8988 0.000 0.000 0.996 0.000 0 0.004
#> DRR006443 2 0.2703 0.7945 0.000 0.824 0.172 0.000 0 0.004
#> DRR006444 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006445 6 0.2442 0.8178 0.004 0.000 0.000 0.144 0 0.852
#> DRR006446 2 0.0146 0.9515 0.000 0.996 0.000 0.000 0 0.004
#> DRR006447 6 0.1218 0.8244 0.012 0.000 0.004 0.028 0 0.956
#> DRR006448 4 0.0363 0.8231 0.000 0.000 0.000 0.988 0 0.012
#> DRR006449 1 0.0508 0.8779 0.984 0.000 0.000 0.004 0 0.012
#> DRR006450 1 0.2234 0.8271 0.872 0.000 0.000 0.004 0 0.124
#> DRR006451 4 0.3371 0.4346 0.000 0.000 0.000 0.708 0 0.292
#> DRR006452 1 0.3025 0.8127 0.820 0.000 0.000 0.156 0 0.024
#> DRR006453 6 0.1910 0.7742 0.108 0.000 0.000 0.000 0 0.892
#> DRR006454 6 0.2454 0.7723 0.000 0.000 0.160 0.000 0 0.840
#> DRR006455 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006456 3 0.0146 0.8988 0.000 0.000 0.996 0.000 0 0.004
#> DRR006457 3 0.0000 0.8993 0.000 0.000 1.000 0.000 0 0.000
#> DRR006458 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006459 1 0.1327 0.8355 0.936 0.000 0.064 0.000 0 0.000
#> DRR006460 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006461 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006462 1 0.3078 0.7948 0.796 0.000 0.000 0.192 0 0.012
#> DRR006463 2 0.2668 0.7978 0.000 0.828 0.168 0.000 0 0.004
#> DRR006464 6 0.1075 0.8160 0.000 0.000 0.048 0.000 0 0.952
#> DRR006465 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006466 3 0.0000 0.8993 0.000 0.000 1.000 0.000 0 0.000
#> DRR006467 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006468 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006469 2 0.0146 0.9515 0.000 0.996 0.000 0.000 0 0.004
#> DRR006470 3 0.3056 0.8531 0.016 0.000 0.832 0.012 0 0.140
#> DRR006471 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006472 3 0.2854 0.6589 0.000 0.000 0.792 0.000 0 0.208
#> DRR006473 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006474 2 0.0000 0.9521 0.000 1.000 0.000 0.000 0 0.000
#> DRR006475 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006476 6 0.2597 0.7609 0.000 0.000 0.176 0.000 0 0.824
#> DRR006477 3 0.0000 0.8993 0.000 0.000 1.000 0.000 0 0.000
#> DRR006478 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006479 3 0.3219 0.8461 0.132 0.000 0.828 0.012 0 0.028
#> DRR006480 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006481 3 0.1074 0.8949 0.000 0.000 0.960 0.012 0 0.028
#> DRR006482 6 0.2416 0.8119 0.000 0.000 0.000 0.156 0 0.844
#> DRR006483 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006484 3 0.3178 0.8488 0.128 0.000 0.832 0.012 0 0.028
#> DRR006485 2 0.2703 0.7945 0.000 0.824 0.172 0.000 0 0.004
#> DRR006486 1 0.2912 0.6571 0.784 0.000 0.216 0.000 0 0.000
#> DRR006487 3 0.0146 0.8988 0.000 0.000 0.996 0.000 0 0.004
#> DRR006488 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006489 1 0.0000 0.8808 1.000 0.000 0.000 0.000 0 0.000
#> DRR006490 3 0.3252 0.8484 0.128 0.000 0.828 0.012 0 0.032
#> DRR006491 3 0.0603 0.8987 0.000 0.000 0.980 0.004 0 0.016
#> DRR006492 3 0.2632 0.8303 0.164 0.000 0.832 0.000 0 0.004
#> DRR006493 3 0.0146 0.8988 0.000 0.000 0.996 0.000 0 0.004
#> DRR006494 1 0.3076 0.6193 0.760 0.000 0.240 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)
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 16187 rows and 121 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 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 0.636 0.847 0.907 0.4788 0.498 0.498
#> 3 3 0.370 0.664 0.792 0.1130 0.940 0.880
#> 4 4 0.786 0.852 0.913 0.2994 0.752 0.495
#> 5 5 0.633 0.671 0.781 0.0829 0.937 0.799
#> 6 6 0.623 0.613 0.711 0.0333 0.903 0.683
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
#> DRR006374 2 0.1633 0.866 0.024 0.976
#> DRR006375 1 0.0376 0.941 0.996 0.004
#> DRR006376 1 0.0000 0.943 1.000 0.000
#> DRR006377 1 0.3114 0.922 0.944 0.056
#> DRR006378 2 0.2603 0.872 0.044 0.956
#> DRR006379 1 0.0000 0.943 1.000 0.000
#> DRR006380 2 0.3114 0.873 0.056 0.944
#> DRR006381 1 0.0938 0.938 0.988 0.012
#> DRR006382 2 0.1633 0.866 0.024 0.976
#> DRR006383 2 0.1633 0.866 0.024 0.976
#> DRR006384 2 0.3114 0.873 0.056 0.944
#> DRR006385 1 0.0938 0.938 0.988 0.012
#> DRR006386 2 0.2043 0.866 0.032 0.968
#> DRR006387 1 0.0000 0.943 1.000 0.000
#> DRR006388 1 0.0938 0.938 0.988 0.012
#> DRR006389 1 0.0938 0.938 0.988 0.012
#> DRR006390 2 0.2603 0.872 0.044 0.956
#> DRR006391 2 0.2603 0.872 0.044 0.956
#> DRR006392 1 0.0000 0.943 1.000 0.000
#> DRR006393 1 0.0000 0.943 1.000 0.000
#> DRR006394 2 0.2603 0.872 0.044 0.956
#> DRR006395 1 0.0000 0.943 1.000 0.000
#> DRR006396 1 0.0000 0.943 1.000 0.000
#> DRR006397 1 0.1414 0.939 0.980 0.020
#> DRR006398 1 0.1633 0.939 0.976 0.024
#> DRR006399 1 0.1633 0.936 0.976 0.024
#> DRR006400 1 0.1633 0.936 0.976 0.024
#> DRR006401 2 0.3114 0.873 0.056 0.944
#> DRR006402 2 0.3114 0.873 0.056 0.944
#> DRR006403 1 0.1633 0.936 0.976 0.024
#> DRR006404 1 0.0000 0.943 1.000 0.000
#> DRR006405 1 0.0000 0.943 1.000 0.000
#> DRR006406 1 0.0000 0.943 1.000 0.000
#> DRR006407 1 0.0000 0.943 1.000 0.000
#> DRR006408 2 0.9522 0.540 0.372 0.628
#> DRR006409 1 0.6148 0.826 0.848 0.152
#> DRR006410 1 0.0000 0.943 1.000 0.000
#> DRR006411 1 0.1843 0.934 0.972 0.028
#> DRR006412 2 0.2603 0.872 0.044 0.956
#> DRR006413 1 0.0000 0.943 1.000 0.000
#> DRR006414 2 0.9170 0.619 0.332 0.668
#> DRR006415 2 0.8499 0.691 0.276 0.724
#> DRR006416 1 0.1414 0.939 0.980 0.020
#> DRR006417 1 0.7139 0.763 0.804 0.196
#> DRR006418 1 0.1633 0.936 0.976 0.024
#> DRR006419 1 0.5408 0.861 0.876 0.124
#> DRR006420 1 0.3274 0.919 0.940 0.060
#> DRR006421 2 0.9170 0.619 0.332 0.668
#> DRR006422 2 0.2603 0.872 0.044 0.956
#> DRR006423 2 0.2603 0.872 0.044 0.956
#> DRR006424 1 0.0376 0.941 0.996 0.004
#> DRR006425 2 0.3114 0.873 0.056 0.944
#> DRR006426 1 0.3879 0.908 0.924 0.076
#> DRR006427 2 0.2043 0.866 0.032 0.968
#> DRR006428 2 0.9170 0.619 0.332 0.668
#> DRR006429 2 0.3114 0.873 0.056 0.944
#> DRR006430 1 0.0000 0.943 1.000 0.000
#> DRR006431 1 0.2423 0.926 0.960 0.040
#> DRR006432 1 0.6973 0.790 0.812 0.188
#> DRR006433 2 0.9170 0.619 0.332 0.668
#> DRR006434 2 0.1633 0.866 0.024 0.976
#> DRR006435 2 0.2043 0.866 0.032 0.968
#> DRR006436 2 0.2043 0.866 0.032 0.968
#> DRR006437 1 0.0000 0.943 1.000 0.000
#> DRR006438 1 0.9896 0.112 0.560 0.440
#> DRR006439 2 0.9170 0.619 0.332 0.668
#> DRR006440 2 0.1633 0.866 0.024 0.976
#> DRR006441 2 0.2603 0.872 0.044 0.956
#> DRR006442 2 0.9170 0.619 0.332 0.668
#> DRR006443 2 0.1633 0.866 0.024 0.976
#> DRR006444 2 0.2043 0.866 0.032 0.968
#> DRR006445 1 0.0000 0.943 1.000 0.000
#> DRR006446 2 0.2603 0.872 0.044 0.956
#> DRR006447 1 0.1633 0.936 0.976 0.024
#> DRR006448 1 0.1633 0.936 0.976 0.024
#> DRR006449 1 0.0000 0.943 1.000 0.000
#> DRR006450 1 0.0938 0.938 0.988 0.012
#> DRR006451 1 0.0000 0.943 1.000 0.000
#> DRR006452 1 0.0000 0.943 1.000 0.000
#> DRR006453 1 0.0938 0.938 0.988 0.012
#> DRR006454 1 0.0938 0.938 0.988 0.012
#> DRR006455 2 0.2043 0.866 0.032 0.968
#> DRR006456 2 0.6247 0.801 0.156 0.844
#> DRR006457 2 0.9170 0.619 0.332 0.668
#> DRR006458 1 0.3114 0.922 0.944 0.056
#> DRR006459 1 0.3114 0.922 0.944 0.056
#> DRR006460 2 0.3114 0.873 0.056 0.944
#> DRR006461 2 0.1633 0.866 0.024 0.976
#> DRR006462 1 0.0000 0.943 1.000 0.000
#> DRR006463 2 0.1633 0.866 0.024 0.976
#> DRR006464 2 0.6048 0.820 0.148 0.852
#> DRR006465 1 0.0000 0.943 1.000 0.000
#> DRR006466 2 0.6247 0.801 0.156 0.844
#> DRR006467 1 0.0938 0.938 0.988 0.012
#> DRR006468 2 0.2043 0.866 0.032 0.968
#> DRR006469 2 0.2603 0.872 0.044 0.956
#> DRR006470 1 0.5519 0.857 0.872 0.128
#> DRR006471 1 0.3114 0.922 0.944 0.056
#> DRR006472 2 0.8955 0.647 0.312 0.688
#> DRR006473 2 0.3114 0.873 0.056 0.944
#> DRR006474 2 0.3114 0.873 0.056 0.944
#> DRR006475 1 0.6973 0.776 0.812 0.188
#> DRR006476 2 0.2603 0.872 0.044 0.956
#> DRR006477 2 0.6247 0.801 0.156 0.844
#> DRR006478 1 0.0938 0.938 0.988 0.012
#> DRR006479 1 0.9491 0.375 0.632 0.368
#> DRR006480 1 0.3114 0.922 0.944 0.056
#> DRR006481 2 0.9209 0.611 0.336 0.664
#> DRR006482 1 0.0000 0.943 1.000 0.000
#> DRR006483 1 0.3114 0.922 0.944 0.056
#> DRR006484 2 0.9170 0.619 0.332 0.668
#> DRR006485 2 0.1633 0.866 0.024 0.976
#> DRR006486 1 0.7299 0.750 0.796 0.204
#> DRR006487 2 0.8555 0.686 0.280 0.720
#> DRR006488 2 0.2043 0.866 0.032 0.968
#> DRR006489 1 0.0938 0.938 0.988 0.012
#> DRR006490 2 0.9170 0.619 0.332 0.668
#> DRR006491 2 0.9170 0.619 0.332 0.668
#> DRR006492 1 0.9209 0.466 0.664 0.336
#> DRR006493 2 0.9087 0.631 0.324 0.676
#> DRR006494 1 0.3114 0.922 0.944 0.056
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.6140 -0.0660 0.000 0.596 0.404
#> DRR006375 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006376 1 0.0424 0.8440 0.992 0.008 0.000
#> DRR006377 1 0.4750 0.7991 0.784 0.216 0.000
#> DRR006378 2 0.4931 0.5242 0.000 0.768 0.232
#> DRR006379 1 0.2356 0.8562 0.928 0.072 0.000
#> DRR006380 2 0.4887 0.4600 0.000 0.772 0.228
#> DRR006381 1 0.0237 0.8429 0.996 0.004 0.000
#> DRR006382 2 0.4605 0.3806 0.000 0.796 0.204
#> DRR006383 2 0.7762 0.4477 0.120 0.668 0.212
#> DRR006384 3 0.6302 -0.2001 0.000 0.480 0.520
#> DRR006385 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006386 3 0.0237 0.9058 0.000 0.004 0.996
#> DRR006387 1 0.2066 0.8549 0.940 0.060 0.000
#> DRR006388 1 0.3349 0.8513 0.888 0.108 0.004
#> DRR006389 1 0.3644 0.8494 0.872 0.124 0.004
#> DRR006390 2 0.4974 0.5201 0.000 0.764 0.236
#> DRR006391 2 0.4974 0.5201 0.000 0.764 0.236
#> DRR006392 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006393 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006394 2 0.4931 0.5242 0.000 0.768 0.232
#> DRR006395 1 0.2448 0.8561 0.924 0.076 0.000
#> DRR006396 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006397 1 0.3193 0.8487 0.896 0.100 0.004
#> DRR006398 1 0.3272 0.8481 0.892 0.104 0.004
#> DRR006399 1 0.5008 0.8261 0.804 0.180 0.016
#> DRR006400 1 0.5008 0.8261 0.804 0.180 0.016
#> DRR006401 2 0.5497 0.4712 0.000 0.708 0.292
#> DRR006402 2 0.5497 0.4712 0.000 0.708 0.292
#> DRR006403 1 0.3941 0.8420 0.844 0.156 0.000
#> DRR006404 1 0.2448 0.8561 0.924 0.076 0.000
#> DRR006405 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006406 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006407 1 0.3686 0.8485 0.860 0.140 0.000
#> DRR006408 2 0.5988 0.5345 0.168 0.776 0.056
#> DRR006409 1 0.4346 0.8277 0.816 0.184 0.000
#> DRR006410 1 0.2261 0.8558 0.932 0.068 0.000
#> DRR006411 1 0.6264 0.7131 0.716 0.256 0.028
#> DRR006412 2 0.4974 0.5201 0.000 0.764 0.236
#> DRR006413 1 0.2356 0.8562 0.928 0.072 0.000
#> DRR006414 2 0.6008 0.4234 0.372 0.628 0.000
#> DRR006415 2 0.8460 0.5549 0.264 0.600 0.136
#> DRR006416 1 0.4521 0.8297 0.816 0.180 0.004
#> DRR006417 1 0.5591 0.6550 0.696 0.304 0.000
#> DRR006418 1 0.4654 0.8075 0.792 0.208 0.000
#> DRR006419 1 0.5098 0.7571 0.752 0.248 0.000
#> DRR006420 1 0.4750 0.7992 0.784 0.216 0.000
#> DRR006421 2 0.6045 0.4068 0.380 0.620 0.000
#> DRR006422 2 0.3879 0.5345 0.000 0.848 0.152
#> DRR006423 2 0.4974 0.5201 0.000 0.764 0.236
#> DRR006424 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006425 2 0.4931 0.5242 0.000 0.768 0.232
#> DRR006426 1 0.5178 0.7447 0.744 0.256 0.000
#> DRR006427 3 0.2066 0.8286 0.000 0.060 0.940
#> DRR006428 2 0.6008 0.4234 0.372 0.628 0.000
#> DRR006429 2 0.5335 0.5274 0.008 0.760 0.232
#> DRR006430 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006431 1 0.2625 0.8566 0.916 0.084 0.000
#> DRR006432 1 0.5216 0.7383 0.740 0.260 0.000
#> DRR006433 2 0.6008 0.4234 0.372 0.628 0.000
#> DRR006434 2 0.5988 0.0401 0.000 0.632 0.368
#> DRR006435 3 0.0237 0.9058 0.000 0.004 0.996
#> DRR006436 3 0.0237 0.9058 0.000 0.004 0.996
#> DRR006437 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006438 1 0.5216 0.7387 0.740 0.260 0.000
#> DRR006439 2 0.6045 0.4068 0.380 0.620 0.000
#> DRR006440 2 0.2711 0.4874 0.000 0.912 0.088
#> DRR006441 2 0.4931 0.5242 0.000 0.768 0.232
#> DRR006442 2 0.6388 0.5459 0.284 0.692 0.024
#> DRR006443 2 0.3686 0.4559 0.000 0.860 0.140
#> DRR006444 3 0.0237 0.9058 0.000 0.004 0.996
#> DRR006445 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006446 2 0.4974 0.5201 0.000 0.764 0.236
#> DRR006447 1 0.4399 0.8250 0.812 0.188 0.000
#> DRR006448 1 0.5062 0.8231 0.800 0.184 0.016
#> DRR006449 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006450 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006451 1 0.1411 0.8506 0.964 0.036 0.000
#> DRR006452 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006453 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006454 1 0.4575 0.8276 0.812 0.184 0.004
#> DRR006455 3 0.0237 0.9058 0.000 0.004 0.996
#> DRR006456 2 0.7865 0.4446 0.124 0.660 0.216
#> DRR006457 2 0.5968 0.4367 0.364 0.636 0.000
#> DRR006458 1 0.3752 0.8463 0.856 0.144 0.000
#> DRR006459 1 0.3340 0.8523 0.880 0.120 0.000
#> DRR006460 2 0.6045 0.4207 0.000 0.620 0.380
#> DRR006461 2 0.6095 -0.0306 0.000 0.608 0.392
#> DRR006462 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006463 2 0.5621 0.1908 0.000 0.692 0.308
#> DRR006464 2 0.7581 0.1428 0.464 0.496 0.040
#> DRR006465 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006466 2 0.5431 0.5424 0.284 0.716 0.000
#> DRR006467 1 0.0237 0.8392 0.996 0.000 0.004
#> DRR006468 3 0.0237 0.9058 0.000 0.004 0.996
#> DRR006469 2 0.4931 0.5242 0.000 0.768 0.232
#> DRR006470 1 0.5178 0.7447 0.744 0.256 0.000
#> DRR006471 1 0.4504 0.8185 0.804 0.196 0.000
#> DRR006472 2 0.6062 0.3975 0.384 0.616 0.000
#> DRR006473 2 0.4974 0.5201 0.000 0.764 0.236
#> DRR006474 2 0.5529 0.4448 0.000 0.704 0.296
#> DRR006475 1 0.4504 0.8185 0.804 0.196 0.000
#> DRR006476 2 0.3412 0.5339 0.000 0.876 0.124
#> DRR006477 2 0.7500 0.5364 0.164 0.696 0.140
#> DRR006478 1 0.0000 0.8414 1.000 0.000 0.000
#> DRR006479 1 0.4974 0.7739 0.764 0.236 0.000
#> DRR006480 1 0.4346 0.8277 0.816 0.184 0.000
#> DRR006481 2 0.6045 0.4068 0.380 0.620 0.000
#> DRR006482 1 0.0237 0.8428 0.996 0.004 0.000
#> DRR006483 1 0.4504 0.8185 0.804 0.196 0.000
#> DRR006484 2 0.6008 0.4234 0.372 0.628 0.000
#> DRR006485 2 0.5591 0.2002 0.000 0.696 0.304
#> DRR006486 1 0.5363 0.7125 0.724 0.276 0.000
#> DRR006487 2 0.8565 0.5509 0.264 0.592 0.144
#> DRR006488 3 0.0237 0.9058 0.000 0.004 0.996
#> DRR006489 1 0.0237 0.8392 0.996 0.000 0.004
#> DRR006490 2 0.5968 0.4367 0.364 0.636 0.000
#> DRR006491 2 0.6155 0.4895 0.328 0.664 0.008
#> DRR006492 1 0.5363 0.7119 0.724 0.276 0.000
#> DRR006493 2 0.8513 0.5531 0.264 0.596 0.140
#> DRR006494 1 0.4346 0.8277 0.816 0.184 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.2216 0.9346 0.000 0.908 0.092 0.000
#> DRR006375 1 0.0336 0.8978 0.992 0.000 0.008 0.000
#> DRR006376 1 0.0000 0.8940 1.000 0.000 0.000 0.000
#> DRR006377 3 0.3224 0.8499 0.120 0.016 0.864 0.000
#> DRR006378 2 0.2214 0.9344 0.028 0.928 0.044 0.000
#> DRR006379 1 0.0707 0.8831 0.980 0.020 0.000 0.000
#> DRR006380 2 0.1824 0.9362 0.000 0.936 0.060 0.004
#> DRR006381 1 0.0817 0.8938 0.976 0.000 0.024 0.000
#> DRR006382 2 0.2216 0.9346 0.000 0.908 0.092 0.000
#> DRR006383 2 0.3569 0.8244 0.000 0.804 0.196 0.000
#> DRR006384 2 0.1824 0.9362 0.000 0.936 0.060 0.004
#> DRR006385 1 0.0804 0.8958 0.980 0.012 0.008 0.000
#> DRR006386 4 0.0188 0.9987 0.000 0.004 0.000 0.996
#> DRR006387 1 0.1042 0.8837 0.972 0.020 0.008 0.000
#> DRR006388 1 0.5472 0.1102 0.544 0.016 0.440 0.000
#> DRR006389 1 0.5663 0.0951 0.536 0.024 0.440 0.000
#> DRR006390 2 0.1733 0.9245 0.028 0.948 0.024 0.000
#> DRR006391 2 0.1733 0.9245 0.028 0.948 0.024 0.000
#> DRR006392 1 0.0336 0.8978 0.992 0.000 0.008 0.000
#> DRR006393 1 0.0817 0.8938 0.976 0.000 0.024 0.000
#> DRR006394 2 0.2300 0.9329 0.028 0.924 0.048 0.000
#> DRR006395 1 0.2032 0.8822 0.936 0.028 0.036 0.000
#> DRR006396 1 0.0336 0.8978 0.992 0.000 0.008 0.000
#> DRR006397 1 0.5800 0.1542 0.548 0.032 0.420 0.000
#> DRR006398 1 0.5800 0.1542 0.548 0.032 0.420 0.000
#> DRR006399 1 0.2722 0.8594 0.904 0.032 0.064 0.000
#> DRR006400 1 0.2722 0.8594 0.904 0.032 0.064 0.000
#> DRR006401 2 0.2197 0.9386 0.000 0.916 0.080 0.004
#> DRR006402 2 0.2197 0.9386 0.000 0.916 0.080 0.004
#> DRR006403 1 0.2300 0.8689 0.924 0.028 0.048 0.000
#> DRR006404 1 0.1510 0.8788 0.956 0.028 0.016 0.000
#> DRR006405 1 0.0336 0.8978 0.992 0.000 0.008 0.000
#> DRR006406 1 0.0336 0.8978 0.992 0.000 0.008 0.000
#> DRR006407 3 0.5511 0.5172 0.332 0.032 0.636 0.000
#> DRR006408 2 0.2311 0.9379 0.004 0.916 0.076 0.004
#> DRR006409 1 0.3485 0.8306 0.856 0.028 0.116 0.000
#> DRR006410 1 0.1411 0.8875 0.960 0.020 0.020 0.000
#> DRR006411 3 0.3372 0.8573 0.096 0.036 0.868 0.000
#> DRR006412 2 0.1733 0.9245 0.028 0.948 0.024 0.000
#> DRR006413 1 0.1677 0.8801 0.948 0.012 0.040 0.000
#> DRR006414 3 0.0895 0.8774 0.004 0.020 0.976 0.000
#> DRR006415 3 0.0707 0.8757 0.000 0.020 0.980 0.000
#> DRR006416 3 0.5157 0.6352 0.284 0.028 0.688 0.000
#> DRR006417 3 0.2131 0.8760 0.036 0.032 0.932 0.000
#> DRR006418 3 0.3342 0.8573 0.100 0.032 0.868 0.000
#> DRR006419 3 0.2131 0.8760 0.036 0.032 0.932 0.000
#> DRR006420 3 0.2773 0.8584 0.116 0.004 0.880 0.000
#> DRR006421 3 0.0524 0.8815 0.008 0.004 0.988 0.000
#> DRR006422 2 0.2797 0.9373 0.032 0.900 0.068 0.000
#> DRR006423 2 0.2214 0.9344 0.028 0.928 0.044 0.000
#> DRR006424 1 0.0524 0.8977 0.988 0.004 0.008 0.000
#> DRR006425 2 0.2385 0.9370 0.028 0.920 0.052 0.000
#> DRR006426 3 0.2131 0.8760 0.036 0.032 0.932 0.000
#> DRR006427 4 0.0524 0.9905 0.000 0.008 0.004 0.988
#> DRR006428 3 0.0895 0.8774 0.004 0.020 0.976 0.000
#> DRR006429 2 0.2385 0.9310 0.028 0.920 0.052 0.000
#> DRR006430 1 0.0336 0.8978 0.992 0.000 0.008 0.000
#> DRR006431 1 0.1722 0.8854 0.944 0.008 0.048 0.000
#> DRR006432 3 0.2131 0.8760 0.036 0.032 0.932 0.000
#> DRR006433 3 0.0817 0.8761 0.000 0.024 0.976 0.000
#> DRR006434 2 0.2216 0.9346 0.000 0.908 0.092 0.000
#> DRR006435 4 0.0188 0.9987 0.000 0.004 0.000 0.996
#> DRR006436 4 0.0188 0.9987 0.000 0.004 0.000 0.996
#> DRR006437 1 0.0336 0.8978 0.992 0.000 0.008 0.000
#> DRR006438 3 0.1256 0.8835 0.028 0.008 0.964 0.000
#> DRR006439 3 0.1211 0.8827 0.040 0.000 0.960 0.000
#> DRR006440 2 0.2216 0.9346 0.000 0.908 0.092 0.000
#> DRR006441 2 0.2214 0.9344 0.028 0.928 0.044 0.000
#> DRR006442 3 0.0707 0.8757 0.000 0.020 0.980 0.000
#> DRR006443 2 0.2216 0.9346 0.000 0.908 0.092 0.000
#> DRR006444 4 0.0188 0.9987 0.000 0.004 0.000 0.996
#> DRR006445 1 0.0524 0.8977 0.988 0.004 0.008 0.000
#> DRR006446 2 0.1733 0.9245 0.028 0.948 0.024 0.000
#> DRR006447 1 0.3215 0.8382 0.876 0.032 0.092 0.000
#> DRR006448 1 0.5180 0.6649 0.740 0.196 0.064 0.000
#> DRR006449 1 0.0336 0.8978 0.992 0.000 0.008 0.000
#> DRR006450 1 0.0524 0.8977 0.988 0.004 0.008 0.000
#> DRR006451 1 0.0188 0.8947 0.996 0.004 0.000 0.000
#> DRR006452 1 0.0804 0.8958 0.980 0.012 0.008 0.000
#> DRR006453 1 0.0524 0.8977 0.988 0.004 0.008 0.000
#> DRR006454 3 0.3810 0.7991 0.188 0.008 0.804 0.000
#> DRR006455 4 0.0188 0.9987 0.000 0.004 0.000 0.996
#> DRR006456 3 0.1557 0.8561 0.000 0.056 0.944 0.000
#> DRR006457 3 0.0707 0.8757 0.000 0.020 0.980 0.000
#> DRR006458 1 0.1890 0.8807 0.936 0.008 0.056 0.000
#> DRR006459 1 0.1807 0.8839 0.940 0.008 0.052 0.000
#> DRR006460 2 0.1824 0.9362 0.000 0.936 0.060 0.004
#> DRR006461 2 0.2216 0.9346 0.000 0.908 0.092 0.000
#> DRR006462 1 0.0188 0.8962 0.996 0.000 0.004 0.000
#> DRR006463 2 0.2216 0.9346 0.000 0.908 0.092 0.000
#> DRR006464 3 0.3435 0.8468 0.036 0.100 0.864 0.000
#> DRR006465 1 0.0336 0.8978 0.992 0.000 0.008 0.000
#> DRR006466 3 0.0592 0.8771 0.000 0.016 0.984 0.000
#> DRR006467 1 0.0524 0.8977 0.988 0.004 0.008 0.000
#> DRR006468 4 0.0188 0.9987 0.000 0.004 0.000 0.996
#> DRR006469 2 0.2214 0.9344 0.028 0.928 0.044 0.000
#> DRR006470 3 0.2131 0.8760 0.036 0.032 0.932 0.000
#> DRR006471 3 0.4323 0.7950 0.184 0.028 0.788 0.000
#> DRR006472 3 0.1388 0.8832 0.028 0.012 0.960 0.000
#> DRR006473 2 0.2214 0.9344 0.028 0.928 0.044 0.000
#> DRR006474 2 0.2335 0.9417 0.020 0.920 0.060 0.000
#> DRR006475 3 0.3494 0.8160 0.172 0.004 0.824 0.000
#> DRR006476 2 0.2125 0.9383 0.004 0.920 0.076 0.000
#> DRR006477 3 0.5386 0.6717 0.056 0.236 0.708 0.000
#> DRR006478 1 0.0336 0.8978 0.992 0.000 0.008 0.000
#> DRR006479 3 0.2412 0.8697 0.084 0.008 0.908 0.000
#> DRR006480 3 0.5263 0.1709 0.448 0.008 0.544 0.000
#> DRR006481 3 0.0336 0.8815 0.008 0.000 0.992 0.000
#> DRR006482 1 0.2868 0.8099 0.864 0.000 0.136 0.000
#> DRR006483 3 0.4353 0.7458 0.232 0.012 0.756 0.000
#> DRR006484 3 0.0804 0.8805 0.008 0.012 0.980 0.000
#> DRR006485 2 0.2216 0.9346 0.000 0.908 0.092 0.000
#> DRR006486 3 0.2542 0.8718 0.084 0.012 0.904 0.000
#> DRR006487 3 0.0707 0.8757 0.000 0.020 0.980 0.000
#> DRR006488 4 0.0188 0.9987 0.000 0.004 0.000 0.996
#> DRR006489 1 0.0524 0.8977 0.988 0.004 0.008 0.000
#> DRR006490 3 0.0707 0.8757 0.000 0.020 0.980 0.000
#> DRR006491 3 0.0707 0.8757 0.000 0.020 0.980 0.000
#> DRR006492 1 0.5105 0.6192 0.696 0.028 0.276 0.000
#> DRR006493 3 0.0707 0.8757 0.000 0.020 0.980 0.000
#> DRR006494 1 0.5125 0.3745 0.604 0.008 0.388 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.0510 0.8582 0.000 0.984 0.016 0.000 0.000
#> DRR006375 1 0.2329 0.6270 0.876 0.000 0.000 0.124 0.000
#> DRR006376 1 0.4183 0.7188 0.668 0.000 0.008 0.324 0.000
#> DRR006377 3 0.5037 -0.1794 0.040 0.000 0.584 0.376 0.000
#> DRR006378 2 0.3569 0.8428 0.000 0.816 0.152 0.028 0.004
#> DRR006379 1 0.4183 0.7198 0.668 0.000 0.008 0.324 0.000
#> DRR006380 2 0.1211 0.8605 0.000 0.960 0.016 0.024 0.000
#> DRR006381 1 0.0798 0.5906 0.976 0.000 0.008 0.016 0.000
#> DRR006382 2 0.0798 0.8578 0.000 0.976 0.016 0.008 0.000
#> DRR006383 2 0.3284 0.7158 0.000 0.828 0.148 0.024 0.000
#> DRR006384 2 0.1117 0.8604 0.000 0.964 0.016 0.020 0.000
#> DRR006385 1 0.4218 0.7137 0.660 0.000 0.008 0.332 0.000
#> DRR006386 5 0.0000 0.9971 0.000 0.000 0.000 0.000 1.000
#> DRR006387 1 0.3143 0.6984 0.796 0.000 0.000 0.204 0.000
#> DRR006388 4 0.5964 0.8926 0.180 0.000 0.232 0.588 0.000
#> DRR006389 4 0.5964 0.8926 0.180 0.000 0.232 0.588 0.000
#> DRR006390 2 0.3441 0.8378 0.000 0.828 0.140 0.028 0.004
#> DRR006391 2 0.3441 0.8378 0.000 0.828 0.140 0.028 0.004
#> DRR006392 1 0.2377 0.6240 0.872 0.000 0.000 0.128 0.000
#> DRR006393 1 0.3421 0.6935 0.788 0.000 0.008 0.204 0.000
#> DRR006394 2 0.4803 0.7886 0.004 0.712 0.220 0.064 0.000
#> DRR006395 1 0.3455 0.6925 0.784 0.000 0.008 0.208 0.000
#> DRR006396 1 0.3913 0.7224 0.676 0.000 0.000 0.324 0.000
#> DRR006397 4 0.5968 0.9118 0.156 0.000 0.268 0.576 0.000
#> DRR006398 4 0.6117 0.8826 0.156 0.000 0.304 0.540 0.000
#> DRR006399 1 0.4199 0.6187 0.692 0.004 0.008 0.296 0.000
#> DRR006400 1 0.4199 0.6187 0.692 0.004 0.008 0.296 0.000
#> DRR006401 2 0.2947 0.8423 0.088 0.876 0.016 0.020 0.000
#> DRR006402 2 0.2947 0.8423 0.088 0.876 0.016 0.020 0.000
#> DRR006403 1 0.3519 0.6893 0.776 0.000 0.008 0.216 0.000
#> DRR006404 1 0.3659 0.6896 0.768 0.000 0.012 0.220 0.000
#> DRR006405 1 0.4235 0.7080 0.656 0.000 0.008 0.336 0.000
#> DRR006406 1 0.4118 0.7111 0.660 0.000 0.004 0.336 0.000
#> DRR006407 4 0.6023 0.9030 0.168 0.000 0.260 0.572 0.000
#> DRR006408 2 0.3730 0.8195 0.120 0.828 0.024 0.028 0.000
#> DRR006409 1 0.4305 0.5388 0.780 0.004 0.128 0.088 0.000
#> DRR006410 1 0.3143 0.6984 0.796 0.000 0.000 0.204 0.000
#> DRR006411 3 0.2891 0.4006 0.000 0.000 0.824 0.176 0.000
#> DRR006412 2 0.3441 0.8378 0.000 0.828 0.140 0.028 0.004
#> DRR006413 1 0.4623 0.7133 0.664 0.000 0.032 0.304 0.000
#> DRR006414 3 0.5774 0.6367 0.000 0.156 0.612 0.232 0.000
#> DRR006415 3 0.6244 0.5947 0.000 0.200 0.540 0.260 0.000
#> DRR006416 4 0.5965 0.8478 0.128 0.000 0.328 0.544 0.000
#> DRR006417 3 0.0290 0.6280 0.000 0.000 0.992 0.008 0.000
#> DRR006418 3 0.1364 0.6081 0.012 0.000 0.952 0.036 0.000
#> DRR006419 3 0.0162 0.6295 0.000 0.000 0.996 0.004 0.000
#> DRR006420 3 0.2179 0.6342 0.112 0.000 0.888 0.000 0.000
#> DRR006421 3 0.3273 0.6458 0.112 0.036 0.848 0.004 0.000
#> DRR006422 2 0.3707 0.8217 0.120 0.828 0.036 0.016 0.000
#> DRR006423 2 0.3310 0.8408 0.000 0.836 0.136 0.024 0.004
#> DRR006424 1 0.2930 0.6586 0.832 0.000 0.004 0.164 0.000
#> DRR006425 2 0.3902 0.8449 0.068 0.824 0.092 0.016 0.000
#> DRR006426 3 0.0880 0.6170 0.000 0.000 0.968 0.032 0.000
#> DRR006427 5 0.0510 0.9794 0.000 0.016 0.000 0.000 0.984
#> DRR006428 3 0.5673 0.6429 0.000 0.156 0.628 0.216 0.000
#> DRR006429 2 0.5054 0.7738 0.004 0.696 0.216 0.084 0.000
#> DRR006430 1 0.2377 0.6240 0.872 0.000 0.000 0.128 0.000
#> DRR006431 1 0.0693 0.5887 0.980 0.000 0.008 0.012 0.000
#> DRR006432 3 0.1121 0.6101 0.000 0.000 0.956 0.044 0.000
#> DRR006433 3 0.5750 0.6382 0.000 0.156 0.616 0.228 0.000
#> DRR006434 2 0.0510 0.8582 0.000 0.984 0.016 0.000 0.000
#> DRR006435 5 0.0000 0.9971 0.000 0.000 0.000 0.000 1.000
#> DRR006436 5 0.0000 0.9971 0.000 0.000 0.000 0.000 1.000
#> DRR006437 1 0.3913 0.7224 0.676 0.000 0.000 0.324 0.000
#> DRR006438 3 0.2574 0.6389 0.112 0.012 0.876 0.000 0.000
#> DRR006439 3 0.5580 0.6531 0.092 0.056 0.712 0.140 0.000
#> DRR006440 2 0.1469 0.8525 0.000 0.948 0.016 0.036 0.000
#> DRR006441 2 0.3525 0.8474 0.000 0.816 0.156 0.024 0.004
#> DRR006442 3 0.5798 0.6358 0.000 0.156 0.608 0.236 0.000
#> DRR006443 2 0.1469 0.8525 0.000 0.948 0.016 0.036 0.000
#> DRR006444 5 0.0000 0.9971 0.000 0.000 0.000 0.000 1.000
#> DRR006445 1 0.4066 0.7214 0.672 0.000 0.004 0.324 0.000
#> DRR006446 2 0.3441 0.8378 0.000 0.828 0.140 0.028 0.004
#> DRR006447 3 0.6301 -0.5268 0.252 0.000 0.532 0.216 0.000
#> DRR006448 1 0.6476 0.2230 0.500 0.140 0.012 0.348 0.000
#> DRR006449 1 0.3796 0.7275 0.700 0.000 0.000 0.300 0.000
#> DRR006450 1 0.3990 0.7240 0.688 0.000 0.004 0.308 0.000
#> DRR006451 1 0.4066 0.7217 0.672 0.000 0.004 0.324 0.000
#> DRR006452 1 0.4183 0.7199 0.668 0.000 0.008 0.324 0.000
#> DRR006453 1 0.4084 0.7194 0.668 0.000 0.004 0.328 0.000
#> DRR006454 3 0.6672 -0.5055 0.232 0.000 0.392 0.376 0.000
#> DRR006455 5 0.0000 0.9971 0.000 0.000 0.000 0.000 1.000
#> DRR006456 3 0.6523 0.5417 0.000 0.248 0.484 0.268 0.000
#> DRR006457 3 0.5774 0.6367 0.000 0.156 0.612 0.232 0.000
#> DRR006458 1 0.0693 0.5887 0.980 0.000 0.008 0.012 0.000
#> DRR006459 1 0.1106 0.5700 0.964 0.000 0.024 0.012 0.000
#> DRR006460 2 0.1117 0.8604 0.000 0.964 0.016 0.020 0.000
#> DRR006461 2 0.0510 0.8582 0.000 0.984 0.016 0.000 0.000
#> DRR006462 1 0.3913 0.7224 0.676 0.000 0.000 0.324 0.000
#> DRR006463 2 0.1469 0.8525 0.000 0.948 0.016 0.036 0.000
#> DRR006464 3 0.4830 0.3055 0.000 0.256 0.684 0.060 0.000
#> DRR006465 1 0.3913 0.7224 0.676 0.000 0.000 0.324 0.000
#> DRR006466 3 0.5444 0.6426 0.000 0.180 0.660 0.160 0.000
#> DRR006467 1 0.2536 0.6228 0.868 0.000 0.004 0.128 0.000
#> DRR006468 5 0.0000 0.9971 0.000 0.000 0.000 0.000 1.000
#> DRR006469 2 0.4191 0.8402 0.000 0.780 0.156 0.060 0.004
#> DRR006470 3 0.0880 0.6170 0.000 0.000 0.968 0.032 0.000
#> DRR006471 3 0.3789 0.4207 0.212 0.000 0.768 0.020 0.000
#> DRR006472 3 0.4327 0.6303 0.112 0.004 0.780 0.104 0.000
#> DRR006473 2 0.3354 0.8386 0.000 0.832 0.140 0.024 0.004
#> DRR006474 2 0.0960 0.8601 0.000 0.972 0.016 0.008 0.004
#> DRR006475 3 0.4126 0.3564 0.380 0.000 0.620 0.000 0.000
#> DRR006476 2 0.4497 0.8157 0.116 0.788 0.032 0.064 0.000
#> DRR006477 2 0.5771 -0.1737 0.000 0.480 0.432 0.088 0.000
#> DRR006478 1 0.3913 0.7224 0.676 0.000 0.000 0.324 0.000
#> DRR006479 3 0.3039 0.6179 0.152 0.012 0.836 0.000 0.000
#> DRR006480 1 0.4063 0.0726 0.708 0.000 0.280 0.012 0.000
#> DRR006481 3 0.2769 0.6494 0.092 0.032 0.876 0.000 0.000
#> DRR006482 1 0.4619 0.6067 0.720 0.000 0.064 0.216 0.000
#> DRR006483 3 0.5516 0.2662 0.220 0.000 0.644 0.136 0.000
#> DRR006484 3 0.4884 0.6607 0.000 0.152 0.720 0.128 0.000
#> DRR006485 2 0.1469 0.8525 0.000 0.948 0.016 0.036 0.000
#> DRR006486 3 0.2818 0.6309 0.128 0.004 0.860 0.008 0.000
#> DRR006487 3 0.6244 0.5947 0.000 0.200 0.540 0.260 0.000
#> DRR006488 5 0.0000 0.9971 0.000 0.000 0.000 0.000 1.000
#> DRR006489 1 0.2536 0.6228 0.868 0.000 0.004 0.128 0.000
#> DRR006490 3 0.5163 0.6566 0.000 0.156 0.692 0.152 0.000
#> DRR006491 3 0.5798 0.6358 0.000 0.156 0.608 0.236 0.000
#> DRR006492 1 0.6421 0.0353 0.528 0.012 0.316 0.144 0.000
#> DRR006493 3 0.6166 0.6040 0.000 0.200 0.556 0.244 0.000
#> DRR006494 1 0.3840 0.2152 0.772 0.008 0.208 0.012 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.3481 0.686 0.000 0.792 0.160 NA 0.000 0.000
#> DRR006375 1 0.2686 0.762 0.876 0.000 0.012 NA 0.000 0.032
#> DRR006376 1 0.0547 0.773 0.980 0.000 0.000 NA 0.000 0.020
#> DRR006377 6 0.7203 0.243 0.276 0.000 0.204 NA 0.000 0.408
#> DRR006378 2 0.4675 0.639 0.000 0.560 0.000 NA 0.000 0.048
#> DRR006379 1 0.1325 0.770 0.956 0.016 0.004 NA 0.000 0.012
#> DRR006380 2 0.0291 0.679 0.000 0.992 0.004 NA 0.000 0.000
#> DRR006381 1 0.4926 0.677 0.668 0.000 0.028 NA 0.000 0.060
#> DRR006382 2 0.3088 0.677 0.000 0.808 0.172 NA 0.000 0.000
#> DRR006383 2 0.3965 0.642 0.000 0.720 0.248 NA 0.000 0.008
#> DRR006384 2 0.1075 0.677 0.000 0.952 0.000 NA 0.000 0.000
#> DRR006385 1 0.1615 0.765 0.928 0.000 0.004 NA 0.000 0.064
#> DRR006386 5 0.0146 0.972 0.000 0.004 0.000 NA 0.996 0.000
#> DRR006387 1 0.0520 0.772 0.984 0.000 0.008 NA 0.000 0.000
#> DRR006388 1 0.7143 0.319 0.400 0.000 0.320 NA 0.000 0.156
#> DRR006389 1 0.7163 0.312 0.396 0.000 0.320 NA 0.000 0.160
#> DRR006390 2 0.4756 0.617 0.000 0.488 0.000 NA 0.000 0.048
#> DRR006391 2 0.4756 0.617 0.000 0.488 0.000 NA 0.000 0.048
#> DRR006392 1 0.4316 0.703 0.728 0.000 0.020 NA 0.000 0.044
#> DRR006393 1 0.0260 0.772 0.992 0.000 0.008 NA 0.000 0.000
#> DRR006394 2 0.7187 0.594 0.128 0.492 0.024 NA 0.000 0.108
#> DRR006395 1 0.3384 0.696 0.812 0.120 0.068 NA 0.000 0.000
#> DRR006396 1 0.1398 0.769 0.940 0.000 0.000 NA 0.000 0.052
#> DRR006397 1 0.7301 0.255 0.364 0.000 0.320 NA 0.000 0.192
#> DRR006398 1 0.7328 0.238 0.356 0.000 0.320 NA 0.000 0.200
#> DRR006399 1 0.6722 0.307 0.472 0.304 0.096 NA 0.000 0.000
#> DRR006400 1 0.6722 0.307 0.472 0.304 0.096 NA 0.000 0.000
#> DRR006401 2 0.1075 0.677 0.000 0.952 0.000 NA 0.000 0.000
#> DRR006402 2 0.1075 0.677 0.000 0.952 0.000 NA 0.000 0.000
#> DRR006403 1 0.4506 0.522 0.652 0.300 0.040 NA 0.000 0.000
#> DRR006404 1 0.3823 0.683 0.788 0.148 0.052 NA 0.000 0.004
#> DRR006405 1 0.0862 0.772 0.972 0.000 0.004 NA 0.000 0.016
#> DRR006406 1 0.0862 0.772 0.972 0.000 0.004 NA 0.000 0.016
#> DRR006407 3 0.8311 -0.338 0.300 0.136 0.340 NA 0.000 0.104
#> DRR006408 2 0.0436 0.680 0.000 0.988 0.004 NA 0.000 0.004
#> DRR006409 1 0.7909 0.200 0.316 0.156 0.268 NA 0.000 0.016
#> DRR006410 1 0.0717 0.772 0.976 0.016 0.008 NA 0.000 0.000
#> DRR006411 6 0.0692 0.649 0.000 0.020 0.004 NA 0.000 0.976
#> DRR006412 2 0.4756 0.617 0.000 0.488 0.000 NA 0.000 0.048
#> DRR006413 1 0.2697 0.695 0.812 0.000 0.000 NA 0.000 0.188
#> DRR006414 3 0.4658 0.798 0.000 0.048 0.568 NA 0.000 0.384
#> DRR006415 3 0.4727 0.790 0.000 0.056 0.576 NA 0.000 0.368
#> DRR006416 1 0.7317 0.241 0.356 0.000 0.324 NA 0.000 0.196
#> DRR006417 6 0.0146 0.656 0.000 0.000 0.004 NA 0.000 0.996
#> DRR006418 6 0.0000 0.656 0.000 0.000 0.000 NA 0.000 1.000
#> DRR006419 6 0.0000 0.656 0.000 0.000 0.000 NA 0.000 1.000
#> DRR006420 6 0.5349 0.302 0.316 0.020 0.080 NA 0.000 0.584
#> DRR006421 3 0.4709 0.704 0.004 0.036 0.516 NA 0.000 0.444
#> DRR006422 2 0.4620 0.661 0.132 0.756 0.016 NA 0.000 0.028
#> DRR006423 2 0.4731 0.634 0.000 0.524 0.000 NA 0.000 0.048
#> DRR006424 1 0.1615 0.766 0.928 0.000 0.004 NA 0.000 0.064
#> DRR006425 2 0.5994 0.642 0.132 0.612 0.008 NA 0.000 0.048
#> DRR006426 6 0.0146 0.656 0.000 0.000 0.004 NA 0.000 0.996
#> DRR006427 5 0.2706 0.802 0.000 0.160 0.000 NA 0.832 0.000
#> DRR006428 3 0.4666 0.796 0.000 0.048 0.564 NA 0.000 0.388
#> DRR006429 2 0.7012 0.498 0.256 0.512 0.028 NA 0.000 0.092
#> DRR006430 1 0.3809 0.707 0.756 0.000 0.020 NA 0.000 0.016
#> DRR006431 1 0.3770 0.685 0.728 0.000 0.028 NA 0.000 0.000
#> DRR006432 6 0.0405 0.655 0.000 0.004 0.008 NA 0.000 0.988
#> DRR006433 3 0.5449 0.721 0.000 0.124 0.488 NA 0.000 0.388
#> DRR006434 2 0.3417 0.686 0.000 0.796 0.160 NA 0.000 0.000
#> DRR006435 5 0.0000 0.974 0.000 0.000 0.000 NA 1.000 0.000
#> DRR006436 5 0.0000 0.974 0.000 0.000 0.000 NA 1.000 0.000
#> DRR006437 1 0.0603 0.774 0.980 0.000 0.000 NA 0.000 0.016
#> DRR006438 6 0.4535 0.175 0.048 0.020 0.232 NA 0.000 0.700
#> DRR006439 3 0.4508 0.781 0.000 0.036 0.568 NA 0.000 0.396
#> DRR006440 2 0.4975 0.553 0.000 0.616 0.316 NA 0.000 0.028
#> DRR006441 2 0.6358 0.638 0.124 0.572 0.012 NA 0.000 0.060
#> DRR006442 3 0.4658 0.798 0.000 0.048 0.568 NA 0.000 0.384
#> DRR006443 2 0.4859 0.545 0.000 0.616 0.324 NA 0.000 0.020
#> DRR006444 5 0.0000 0.974 0.000 0.000 0.000 NA 1.000 0.000
#> DRR006445 1 0.1829 0.765 0.920 0.000 0.004 NA 0.000 0.064
#> DRR006446 2 0.4756 0.617 0.000 0.488 0.000 NA 0.000 0.048
#> DRR006447 6 0.2100 0.585 0.112 0.000 0.004 NA 0.000 0.884
#> DRR006448 2 0.7034 0.115 0.328 0.416 0.084 NA 0.000 0.004
#> DRR006449 1 0.0405 0.773 0.988 0.000 0.004 NA 0.000 0.000
#> DRR006450 1 0.1867 0.767 0.916 0.000 0.000 NA 0.000 0.064
#> DRR006451 1 0.0870 0.772 0.972 0.000 0.004 NA 0.000 0.012
#> DRR006452 1 0.1471 0.765 0.932 0.000 0.000 NA 0.000 0.064
#> DRR006453 1 0.1615 0.765 0.928 0.000 0.004 NA 0.000 0.064
#> DRR006454 1 0.7274 0.279 0.484 0.020 0.188 NA 0.000 0.204
#> DRR006455 5 0.0146 0.972 0.000 0.004 0.000 NA 0.996 0.000
#> DRR006456 3 0.6281 0.573 0.000 0.160 0.508 NA 0.000 0.292
#> DRR006457 3 0.4610 0.795 0.000 0.044 0.568 NA 0.000 0.388
#> DRR006458 1 0.3770 0.685 0.728 0.000 0.028 NA 0.000 0.000
#> DRR006459 1 0.4015 0.680 0.720 0.000 0.028 NA 0.000 0.008
#> DRR006460 2 0.1075 0.677 0.000 0.952 0.000 NA 0.000 0.000
#> DRR006461 2 0.2706 0.685 0.000 0.832 0.160 NA 0.000 0.000
#> DRR006462 1 0.0146 0.773 0.996 0.000 0.000 NA 0.000 0.000
#> DRR006463 2 0.4859 0.545 0.000 0.616 0.324 NA 0.000 0.020
#> DRR006464 6 0.2926 0.536 0.000 0.112 0.012 NA 0.000 0.852
#> DRR006465 1 0.0000 0.773 1.000 0.000 0.000 NA 0.000 0.000
#> DRR006466 3 0.5325 0.711 0.000 0.108 0.500 NA 0.000 0.392
#> DRR006467 1 0.4606 0.698 0.708 0.000 0.020 NA 0.000 0.064
#> DRR006468 5 0.0000 0.974 0.000 0.000 0.000 NA 1.000 0.000
#> DRR006469 2 0.5197 0.628 0.000 0.512 0.020 NA 0.000 0.048
#> DRR006470 6 0.0146 0.656 0.000 0.000 0.004 NA 0.000 0.996
#> DRR006471 6 0.0790 0.651 0.032 0.000 0.000 NA 0.000 0.968
#> DRR006472 6 0.5191 -0.535 0.032 0.032 0.448 NA 0.000 0.488
#> DRR006473 2 0.4731 0.634 0.000 0.524 0.000 NA 0.000 0.048
#> DRR006474 2 0.3254 0.695 0.000 0.816 0.136 NA 0.000 0.000
#> DRR006475 6 0.4733 0.440 0.204 0.020 0.076 NA 0.000 0.700
#> DRR006476 2 0.3993 0.681 0.000 0.780 0.148 NA 0.000 0.032
#> DRR006477 2 0.5214 0.229 0.000 0.632 0.056 NA 0.000 0.272
#> DRR006478 1 0.0260 0.774 0.992 0.000 0.000 NA 0.000 0.008
#> DRR006479 6 0.5223 -0.320 0.060 0.020 0.352 NA 0.000 0.568
#> DRR006480 1 0.6849 0.441 0.532 0.020 0.108 NA 0.000 0.100
#> DRR006481 6 0.4219 -0.371 0.000 0.020 0.388 NA 0.000 0.592
#> DRR006482 1 0.1138 0.770 0.960 0.004 0.012 NA 0.000 0.024
#> DRR006483 6 0.2340 0.568 0.148 0.000 0.000 NA 0.000 0.852
#> DRR006484 3 0.4620 0.749 0.000 0.040 0.532 NA 0.000 0.428
#> DRR006485 2 0.4859 0.545 0.000 0.616 0.324 NA 0.000 0.020
#> DRR006486 6 0.3533 0.386 0.008 0.020 0.196 NA 0.000 0.776
#> DRR006487 3 0.4727 0.784 0.000 0.056 0.576 NA 0.000 0.368
#> DRR006488 5 0.0000 0.974 0.000 0.000 0.000 NA 1.000 0.000
#> DRR006489 1 0.4659 0.697 0.704 0.000 0.020 NA 0.000 0.068
#> DRR006490 3 0.4610 0.795 0.000 0.044 0.568 NA 0.000 0.388
#> DRR006491 3 0.4658 0.798 0.000 0.048 0.568 NA 0.000 0.384
#> DRR006492 3 0.6298 0.460 0.164 0.032 0.468 NA 0.000 0.336
#> DRR006493 3 0.4747 0.794 0.000 0.056 0.568 NA 0.000 0.376
#> DRR006494 1 0.6886 0.439 0.524 0.020 0.124 NA 0.000 0.088
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.882 0.929 0.968 0.4597 0.538 0.538
#> 3 3 0.495 0.679 0.805 0.3685 0.735 0.547
#> 4 4 0.583 0.638 0.779 0.1575 0.846 0.602
#> 5 5 0.786 0.820 0.892 0.0690 0.931 0.745
#> 6 6 0.727 0.646 0.812 0.0529 0.872 0.514
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
#> DRR006374 2 0.0000 0.948 0.000 1.000
#> DRR006375 1 0.0000 0.976 1.000 0.000
#> DRR006376 1 0.0000 0.976 1.000 0.000
#> DRR006377 1 0.0000 0.976 1.000 0.000
#> DRR006378 1 0.0000 0.976 1.000 0.000
#> DRR006379 1 0.0000 0.976 1.000 0.000
#> DRR006380 1 0.6343 0.806 0.840 0.160
#> DRR006381 1 0.0000 0.976 1.000 0.000
#> DRR006382 2 0.0000 0.948 0.000 1.000
#> DRR006383 2 0.0000 0.948 0.000 1.000
#> DRR006384 1 0.7219 0.749 0.800 0.200
#> DRR006385 1 0.0000 0.976 1.000 0.000
#> DRR006386 1 0.5946 0.827 0.856 0.144
#> DRR006387 1 0.0000 0.976 1.000 0.000
#> DRR006388 1 0.0000 0.976 1.000 0.000
#> DRR006389 1 0.0000 0.976 1.000 0.000
#> DRR006390 1 0.0000 0.976 1.000 0.000
#> DRR006391 1 0.0000 0.976 1.000 0.000
#> DRR006392 1 0.0000 0.976 1.000 0.000
#> DRR006393 1 0.0000 0.976 1.000 0.000
#> DRR006394 1 0.0000 0.976 1.000 0.000
#> DRR006395 1 0.0000 0.976 1.000 0.000
#> DRR006396 1 0.0000 0.976 1.000 0.000
#> DRR006397 1 0.0000 0.976 1.000 0.000
#> DRR006398 1 0.0000 0.976 1.000 0.000
#> DRR006399 1 0.0000 0.976 1.000 0.000
#> DRR006400 1 0.0000 0.976 1.000 0.000
#> DRR006401 1 0.0000 0.976 1.000 0.000
#> DRR006402 1 0.0000 0.976 1.000 0.000
#> DRR006403 1 0.0000 0.976 1.000 0.000
#> DRR006404 1 0.0000 0.976 1.000 0.000
#> DRR006405 1 0.0000 0.976 1.000 0.000
#> DRR006406 1 0.0000 0.976 1.000 0.000
#> DRR006407 1 0.0000 0.976 1.000 0.000
#> DRR006408 1 0.0000 0.976 1.000 0.000
#> DRR006409 1 0.0000 0.976 1.000 0.000
#> DRR006410 1 0.0000 0.976 1.000 0.000
#> DRR006411 1 0.0000 0.976 1.000 0.000
#> DRR006412 1 0.0000 0.976 1.000 0.000
#> DRR006413 1 0.0000 0.976 1.000 0.000
#> DRR006414 2 0.2948 0.914 0.052 0.948
#> DRR006415 2 0.0000 0.948 0.000 1.000
#> DRR006416 1 0.0000 0.976 1.000 0.000
#> DRR006417 2 0.0000 0.948 0.000 1.000
#> DRR006418 1 0.0000 0.976 1.000 0.000
#> DRR006419 2 0.6343 0.817 0.160 0.840
#> DRR006420 1 0.9710 0.275 0.600 0.400
#> DRR006421 2 0.2948 0.916 0.052 0.948
#> DRR006422 1 0.0000 0.976 1.000 0.000
#> DRR006423 1 0.4161 0.894 0.916 0.084
#> DRR006424 1 0.0000 0.976 1.000 0.000
#> DRR006425 1 0.0000 0.976 1.000 0.000
#> DRR006426 2 0.6623 0.804 0.172 0.828
#> DRR006427 2 0.0000 0.948 0.000 1.000
#> DRR006428 2 0.0000 0.948 0.000 1.000
#> DRR006429 1 0.1843 0.952 0.972 0.028
#> DRR006430 1 0.0000 0.976 1.000 0.000
#> DRR006431 1 0.0000 0.976 1.000 0.000
#> DRR006432 2 0.7299 0.764 0.204 0.796
#> DRR006433 2 0.0938 0.941 0.012 0.988
#> DRR006434 2 0.0000 0.948 0.000 1.000
#> DRR006435 2 0.0000 0.948 0.000 1.000
#> DRR006436 2 0.0000 0.948 0.000 1.000
#> DRR006437 1 0.0000 0.976 1.000 0.000
#> DRR006438 2 0.3431 0.906 0.064 0.936
#> DRR006439 2 0.8327 0.677 0.264 0.736
#> DRR006440 2 0.0000 0.948 0.000 1.000
#> DRR006441 1 0.0000 0.976 1.000 0.000
#> DRR006442 2 0.0000 0.948 0.000 1.000
#> DRR006443 2 0.0000 0.948 0.000 1.000
#> DRR006444 2 0.0000 0.948 0.000 1.000
#> DRR006445 1 0.0000 0.976 1.000 0.000
#> DRR006446 1 0.0000 0.976 1.000 0.000
#> DRR006447 1 0.0000 0.976 1.000 0.000
#> DRR006448 1 0.0000 0.976 1.000 0.000
#> DRR006449 1 0.0000 0.976 1.000 0.000
#> DRR006450 1 0.0000 0.976 1.000 0.000
#> DRR006451 1 0.0000 0.976 1.000 0.000
#> DRR006452 1 0.0000 0.976 1.000 0.000
#> DRR006453 1 0.0000 0.976 1.000 0.000
#> DRR006454 1 0.0000 0.976 1.000 0.000
#> DRR006455 2 0.9775 0.299 0.412 0.588
#> DRR006456 2 0.0000 0.948 0.000 1.000
#> DRR006457 2 0.0000 0.948 0.000 1.000
#> DRR006458 1 0.0000 0.976 1.000 0.000
#> DRR006459 1 0.0000 0.976 1.000 0.000
#> DRR006460 1 0.3733 0.910 0.928 0.072
#> DRR006461 2 0.0000 0.948 0.000 1.000
#> DRR006462 1 0.0000 0.976 1.000 0.000
#> DRR006463 2 0.0000 0.948 0.000 1.000
#> DRR006464 1 0.8081 0.649 0.752 0.248
#> DRR006465 1 0.0000 0.976 1.000 0.000
#> DRR006466 2 0.0000 0.948 0.000 1.000
#> DRR006467 1 0.0000 0.976 1.000 0.000
#> DRR006468 2 0.0000 0.948 0.000 1.000
#> DRR006469 1 0.0000 0.976 1.000 0.000
#> DRR006470 2 0.4022 0.894 0.080 0.920
#> DRR006471 1 0.0000 0.976 1.000 0.000
#> DRR006472 2 0.0000 0.948 0.000 1.000
#> DRR006473 2 0.0000 0.948 0.000 1.000
#> DRR006474 2 0.0000 0.948 0.000 1.000
#> DRR006475 1 0.2043 0.949 0.968 0.032
#> DRR006476 1 0.2778 0.934 0.952 0.048
#> DRR006477 1 0.8608 0.611 0.716 0.284
#> DRR006478 1 0.0000 0.976 1.000 0.000
#> DRR006479 2 0.9044 0.580 0.320 0.680
#> DRR006480 1 0.0000 0.976 1.000 0.000
#> DRR006481 2 0.0000 0.948 0.000 1.000
#> DRR006482 1 0.0000 0.976 1.000 0.000
#> DRR006483 1 0.0000 0.976 1.000 0.000
#> DRR006484 2 0.0000 0.948 0.000 1.000
#> DRR006485 2 0.0000 0.948 0.000 1.000
#> DRR006486 2 0.9087 0.574 0.324 0.676
#> DRR006487 2 0.0000 0.948 0.000 1.000
#> DRR006488 2 0.0000 0.948 0.000 1.000
#> DRR006489 1 0.0000 0.976 1.000 0.000
#> DRR006490 2 0.0000 0.948 0.000 1.000
#> DRR006491 2 0.0000 0.948 0.000 1.000
#> DRR006492 1 0.0000 0.976 1.000 0.000
#> DRR006493 2 0.0000 0.948 0.000 1.000
#> DRR006494 1 0.0000 0.976 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 3 0.3752 0.83068 0.000 0.144 0.856
#> DRR006375 1 0.3412 0.76315 0.876 0.124 0.000
#> DRR006376 1 0.5760 0.52390 0.672 0.328 0.000
#> DRR006377 1 0.4654 0.58522 0.792 0.208 0.000
#> DRR006378 1 0.2356 0.75235 0.928 0.072 0.000
#> DRR006379 2 0.4887 0.72791 0.228 0.772 0.000
#> DRR006380 2 0.1620 0.69655 0.012 0.964 0.024
#> DRR006381 1 0.6295 0.02921 0.528 0.472 0.000
#> DRR006382 3 0.3272 0.86808 0.016 0.080 0.904
#> DRR006383 3 0.4449 0.85016 0.040 0.100 0.860
#> DRR006384 2 0.2229 0.68838 0.012 0.944 0.044
#> DRR006385 1 0.3038 0.76608 0.896 0.104 0.000
#> DRR006386 2 0.2749 0.68009 0.064 0.924 0.012
#> DRR006387 2 0.4931 0.71946 0.232 0.768 0.000
#> DRR006388 1 0.3038 0.76820 0.896 0.104 0.000
#> DRR006389 1 0.3038 0.76820 0.896 0.104 0.000
#> DRR006390 1 0.2165 0.74705 0.936 0.064 0.000
#> DRR006391 1 0.2261 0.74522 0.932 0.068 0.000
#> DRR006392 1 0.5098 0.63412 0.752 0.248 0.000
#> DRR006393 1 0.6225 0.20997 0.568 0.432 0.000
#> DRR006394 1 0.2066 0.74954 0.940 0.060 0.000
#> DRR006395 2 0.4346 0.76096 0.184 0.816 0.000
#> DRR006396 2 0.6280 0.20718 0.460 0.540 0.000
#> DRR006397 1 0.3267 0.76589 0.884 0.116 0.000
#> DRR006398 1 0.3116 0.76760 0.892 0.108 0.000
#> DRR006399 2 0.4178 0.76530 0.172 0.828 0.000
#> DRR006400 2 0.4178 0.76530 0.172 0.828 0.000
#> DRR006401 2 0.2165 0.73826 0.064 0.936 0.000
#> DRR006402 2 0.2165 0.73826 0.064 0.936 0.000
#> DRR006403 2 0.4062 0.76586 0.164 0.836 0.000
#> DRR006404 2 0.4178 0.76530 0.172 0.828 0.000
#> DRR006405 1 0.4605 0.71859 0.796 0.204 0.000
#> DRR006406 1 0.5497 0.60055 0.708 0.292 0.000
#> DRR006407 1 0.6235 -0.00498 0.564 0.436 0.000
#> DRR006408 2 0.3619 0.76253 0.136 0.864 0.000
#> DRR006409 2 0.4346 0.75913 0.184 0.816 0.000
#> DRR006410 2 0.5016 0.70961 0.240 0.760 0.000
#> DRR006411 1 0.1529 0.75575 0.960 0.040 0.000
#> DRR006412 1 0.1529 0.75377 0.960 0.040 0.000
#> DRR006413 1 0.3784 0.75495 0.864 0.132 0.004
#> DRR006414 3 0.2743 0.86769 0.052 0.020 0.928
#> DRR006415 3 0.1636 0.88184 0.016 0.020 0.964
#> DRR006416 1 0.2625 0.76969 0.916 0.084 0.000
#> DRR006417 3 0.6267 0.17355 0.452 0.000 0.548
#> DRR006418 1 0.1267 0.75940 0.972 0.024 0.004
#> DRR006419 1 0.5138 0.56846 0.748 0.000 0.252
#> DRR006420 1 0.5467 0.63173 0.792 0.032 0.176
#> DRR006421 3 0.2711 0.84070 0.088 0.000 0.912
#> DRR006422 1 0.6309 -0.08262 0.504 0.496 0.000
#> DRR006423 1 0.2280 0.73394 0.940 0.008 0.052
#> DRR006424 1 0.4178 0.72306 0.828 0.172 0.000
#> DRR006425 1 0.6045 0.42330 0.620 0.380 0.000
#> DRR006426 1 0.4796 0.57157 0.780 0.000 0.220
#> DRR006427 3 0.5939 0.79920 0.072 0.140 0.788
#> DRR006428 3 0.1491 0.88529 0.016 0.016 0.968
#> DRR006429 1 0.2383 0.73840 0.940 0.016 0.044
#> DRR006430 1 0.5621 0.52827 0.692 0.308 0.000
#> DRR006431 2 0.6267 0.27465 0.452 0.548 0.000
#> DRR006432 1 0.4702 0.57923 0.788 0.000 0.212
#> DRR006433 3 0.1647 0.88140 0.004 0.036 0.960
#> DRR006434 3 0.0237 0.88668 0.004 0.000 0.996
#> DRR006435 3 0.6696 0.76193 0.076 0.188 0.736
#> DRR006436 3 0.6435 0.77818 0.076 0.168 0.756
#> DRR006437 1 0.5760 0.51883 0.672 0.328 0.000
#> DRR006438 3 0.5591 0.53652 0.304 0.000 0.696
#> DRR006439 3 0.2599 0.87317 0.052 0.016 0.932
#> DRR006440 3 0.1031 0.88639 0.024 0.000 0.976
#> DRR006441 1 0.2356 0.74866 0.928 0.072 0.000
#> DRR006442 3 0.1482 0.88295 0.012 0.020 0.968
#> DRR006443 3 0.0237 0.88668 0.004 0.000 0.996
#> DRR006444 3 0.6511 0.77161 0.072 0.180 0.748
#> DRR006445 1 0.4178 0.73506 0.828 0.172 0.000
#> DRR006446 1 0.1753 0.75587 0.952 0.048 0.000
#> DRR006447 1 0.2537 0.77023 0.920 0.080 0.000
#> DRR006448 2 0.4452 0.75504 0.192 0.808 0.000
#> DRR006449 2 0.6252 0.25892 0.444 0.556 0.000
#> DRR006450 1 0.2878 0.76649 0.904 0.096 0.000
#> DRR006451 2 0.6299 0.11302 0.476 0.524 0.000
#> DRR006452 1 0.4504 0.69964 0.804 0.196 0.000
#> DRR006453 1 0.2711 0.76797 0.912 0.088 0.000
#> DRR006454 1 0.4178 0.74397 0.828 0.172 0.000
#> DRR006455 2 0.7024 0.36382 0.072 0.704 0.224
#> DRR006456 3 0.0237 0.88685 0.000 0.004 0.996
#> DRR006457 3 0.0592 0.88647 0.012 0.000 0.988
#> DRR006458 1 0.6140 0.30736 0.596 0.404 0.000
#> DRR006459 1 0.4605 0.70579 0.796 0.204 0.000
#> DRR006460 2 0.1129 0.71054 0.020 0.976 0.004
#> DRR006461 3 0.1031 0.88479 0.000 0.024 0.976
#> DRR006462 1 0.6111 0.37791 0.604 0.396 0.000
#> DRR006463 3 0.0237 0.88668 0.004 0.000 0.996
#> DRR006464 1 0.2550 0.72996 0.932 0.012 0.056
#> DRR006465 1 0.5431 0.60755 0.716 0.284 0.000
#> DRR006466 3 0.0747 0.88630 0.016 0.000 0.984
#> DRR006467 1 0.2711 0.76797 0.912 0.088 0.000
#> DRR006468 3 0.6696 0.76193 0.076 0.188 0.736
#> DRR006469 1 0.2176 0.74809 0.948 0.032 0.020
#> DRR006470 1 0.4931 0.55404 0.768 0.000 0.232
#> DRR006471 1 0.2537 0.76840 0.920 0.080 0.000
#> DRR006472 3 0.1289 0.88270 0.032 0.000 0.968
#> DRR006473 1 0.4931 0.55691 0.768 0.000 0.232
#> DRR006474 3 0.4755 0.81245 0.008 0.184 0.808
#> DRR006475 1 0.5339 0.70991 0.824 0.080 0.096
#> DRR006476 2 0.7263 0.65065 0.224 0.692 0.084
#> DRR006477 2 0.5156 0.47880 0.008 0.776 0.216
#> DRR006478 1 0.3267 0.76624 0.884 0.116 0.000
#> DRR006479 3 0.7029 0.21969 0.440 0.020 0.540
#> DRR006480 1 0.6451 0.16554 0.560 0.436 0.004
#> DRR006481 3 0.1964 0.86972 0.056 0.000 0.944
#> DRR006482 2 0.6204 0.35215 0.424 0.576 0.000
#> DRR006483 1 0.2448 0.76919 0.924 0.076 0.000
#> DRR006484 3 0.1753 0.87250 0.048 0.000 0.952
#> DRR006485 3 0.0237 0.88668 0.004 0.000 0.996
#> DRR006486 1 0.6294 0.50816 0.692 0.020 0.288
#> DRR006487 3 0.0237 0.88685 0.000 0.004 0.996
#> DRR006488 3 0.6324 0.78378 0.076 0.160 0.764
#> DRR006489 1 0.2625 0.76815 0.916 0.084 0.000
#> DRR006490 3 0.2297 0.87864 0.036 0.020 0.944
#> DRR006491 3 0.2050 0.87958 0.028 0.020 0.952
#> DRR006492 2 0.4805 0.76017 0.176 0.812 0.012
#> DRR006493 3 0.0747 0.88592 0.000 0.016 0.984
#> DRR006494 1 0.5815 0.54563 0.692 0.304 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 3 0.0188 0.8350 0.000 0.000 0.996 0.004
#> DRR006375 2 0.5220 0.1626 0.424 0.568 0.000 0.008
#> DRR006376 2 0.4820 0.5626 0.012 0.692 0.000 0.296
#> DRR006377 2 0.3236 0.7095 0.028 0.880 0.004 0.088
#> DRR006378 2 0.0804 0.7732 0.012 0.980 0.000 0.008
#> DRR006379 4 0.5228 0.4054 0.024 0.312 0.000 0.664
#> DRR006380 4 0.1302 0.7726 0.044 0.000 0.000 0.956
#> DRR006381 1 0.5090 0.6068 0.660 0.016 0.000 0.324
#> DRR006382 3 0.0921 0.8297 0.028 0.000 0.972 0.000
#> DRR006383 3 0.4866 0.2546 0.404 0.000 0.596 0.000
#> DRR006384 4 0.1004 0.7670 0.024 0.000 0.004 0.972
#> DRR006385 1 0.5217 0.4799 0.608 0.380 0.000 0.012
#> DRR006386 4 0.5384 0.5636 0.324 0.028 0.000 0.648
#> DRR006387 4 0.3764 0.6309 0.216 0.000 0.000 0.784
#> DRR006388 2 0.3444 0.6979 0.184 0.816 0.000 0.000
#> DRR006389 2 0.3444 0.6979 0.184 0.816 0.000 0.000
#> DRR006390 2 0.1545 0.7500 0.040 0.952 0.000 0.008
#> DRR006391 2 0.1677 0.7481 0.040 0.948 0.000 0.012
#> DRR006392 1 0.6360 0.7257 0.656 0.180 0.000 0.164
#> DRR006393 1 0.6461 0.6960 0.632 0.128 0.000 0.240
#> DRR006394 2 0.1109 0.7578 0.028 0.968 0.000 0.004
#> DRR006395 4 0.2983 0.7496 0.040 0.068 0.000 0.892
#> DRR006396 1 0.5884 0.5692 0.592 0.044 0.000 0.364
#> DRR006397 2 0.2868 0.7316 0.136 0.864 0.000 0.000
#> DRR006398 2 0.2868 0.7316 0.136 0.864 0.000 0.000
#> DRR006399 4 0.2011 0.7631 0.080 0.000 0.000 0.920
#> DRR006400 4 0.2011 0.7631 0.080 0.000 0.000 0.920
#> DRR006401 4 0.1022 0.7661 0.032 0.000 0.000 0.968
#> DRR006402 4 0.1022 0.7661 0.032 0.000 0.000 0.968
#> DRR006403 4 0.1716 0.7690 0.064 0.000 0.000 0.936
#> DRR006404 4 0.3056 0.7474 0.040 0.072 0.000 0.888
#> DRR006405 2 0.4920 0.6839 0.068 0.768 0.000 0.164
#> DRR006406 2 0.5407 0.5665 0.036 0.668 0.000 0.296
#> DRR006407 2 0.6079 0.2026 0.048 0.544 0.000 0.408
#> DRR006408 4 0.1211 0.7729 0.040 0.000 0.000 0.960
#> DRR006409 4 0.4103 0.5622 0.256 0.000 0.000 0.744
#> DRR006410 4 0.4212 0.6234 0.216 0.012 0.000 0.772
#> DRR006411 2 0.0592 0.7732 0.016 0.984 0.000 0.000
#> DRR006412 2 0.0817 0.7604 0.024 0.976 0.000 0.000
#> DRR006413 1 0.6157 0.7004 0.660 0.232 0.000 0.108
#> DRR006414 3 0.4933 0.1753 0.432 0.000 0.568 0.000
#> DRR006415 3 0.0921 0.8297 0.028 0.000 0.972 0.000
#> DRR006416 2 0.4994 -0.0456 0.480 0.520 0.000 0.000
#> DRR006417 3 0.4795 0.5119 0.012 0.292 0.696 0.000
#> DRR006418 2 0.3074 0.7224 0.152 0.848 0.000 0.000
#> DRR006419 2 0.5723 0.5512 0.244 0.684 0.072 0.000
#> DRR006420 1 0.7353 0.4275 0.516 0.288 0.196 0.000
#> DRR006421 3 0.0657 0.8335 0.004 0.012 0.984 0.000
#> DRR006422 1 0.5038 0.5930 0.652 0.012 0.000 0.336
#> DRR006423 2 0.1697 0.7518 0.028 0.952 0.004 0.016
#> DRR006424 1 0.5953 0.6670 0.656 0.268 0.000 0.076
#> DRR006425 2 0.6423 0.4682 0.084 0.580 0.000 0.336
#> DRR006426 2 0.0779 0.7727 0.016 0.980 0.004 0.000
#> DRR006427 3 0.8316 0.4138 0.340 0.116 0.476 0.068
#> DRR006428 3 0.0469 0.8340 0.012 0.000 0.988 0.000
#> DRR006429 2 0.1305 0.7717 0.036 0.960 0.004 0.000
#> DRR006430 1 0.5614 0.6365 0.652 0.044 0.000 0.304
#> DRR006431 1 0.4761 0.5401 0.628 0.000 0.000 0.372
#> DRR006432 2 0.0592 0.7732 0.016 0.984 0.000 0.000
#> DRR006433 3 0.3441 0.7467 0.024 0.000 0.856 0.120
#> DRR006434 3 0.0657 0.8335 0.012 0.000 0.984 0.004
#> DRR006435 3 0.9318 0.2773 0.340 0.132 0.372 0.156
#> DRR006436 3 0.9266 0.2900 0.340 0.132 0.380 0.148
#> DRR006437 1 0.6613 0.7157 0.628 0.200 0.000 0.172
#> DRR006438 3 0.2363 0.8006 0.024 0.056 0.920 0.000
#> DRR006439 3 0.0921 0.8290 0.028 0.000 0.972 0.000
#> DRR006440 3 0.1082 0.8297 0.020 0.004 0.972 0.004
#> DRR006441 2 0.2943 0.7184 0.032 0.892 0.000 0.076
#> DRR006442 3 0.0817 0.8311 0.024 0.000 0.976 0.000
#> DRR006443 3 0.0524 0.8343 0.008 0.000 0.988 0.004
#> DRR006444 3 0.9288 0.2824 0.340 0.128 0.376 0.156
#> DRR006445 1 0.5530 0.5717 0.632 0.336 0.000 0.032
#> DRR006446 2 0.0592 0.7732 0.016 0.984 0.000 0.000
#> DRR006447 2 0.3688 0.6718 0.208 0.792 0.000 0.000
#> DRR006448 4 0.3123 0.7008 0.156 0.000 0.000 0.844
#> DRR006449 1 0.6910 0.5904 0.548 0.128 0.000 0.324
#> DRR006450 1 0.5423 0.5785 0.640 0.332 0.000 0.028
#> DRR006451 2 0.6009 0.1654 0.040 0.492 0.000 0.468
#> DRR006452 1 0.6267 0.6949 0.648 0.240 0.000 0.112
#> DRR006453 1 0.4920 0.5013 0.628 0.368 0.000 0.004
#> DRR006454 2 0.4864 0.6779 0.172 0.768 0.000 0.060
#> DRR006455 4 0.7590 0.4606 0.340 0.076 0.052 0.532
#> DRR006456 3 0.0000 0.8352 0.000 0.000 1.000 0.000
#> DRR006457 3 0.0188 0.8351 0.004 0.000 0.996 0.000
#> DRR006458 1 0.5193 0.6075 0.656 0.020 0.000 0.324
#> DRR006459 1 0.6775 0.7003 0.628 0.136 0.008 0.228
#> DRR006460 4 0.0921 0.7637 0.028 0.000 0.000 0.972
#> DRR006461 3 0.0188 0.8350 0.000 0.000 0.996 0.004
#> DRR006462 4 0.7564 0.0482 0.208 0.328 0.000 0.464
#> DRR006463 3 0.0376 0.8347 0.004 0.000 0.992 0.004
#> DRR006464 2 0.0592 0.7732 0.016 0.984 0.000 0.000
#> DRR006465 1 0.6719 0.6947 0.608 0.240 0.000 0.152
#> DRR006466 3 0.0469 0.8345 0.012 0.000 0.988 0.000
#> DRR006467 1 0.5432 0.6013 0.652 0.316 0.000 0.032
#> DRR006468 3 0.9318 0.2773 0.340 0.132 0.372 0.156
#> DRR006469 2 0.0592 0.7635 0.016 0.984 0.000 0.000
#> DRR006470 2 0.2124 0.7633 0.068 0.924 0.008 0.000
#> DRR006471 2 0.5161 -0.0324 0.476 0.520 0.004 0.000
#> DRR006472 3 0.0000 0.8352 0.000 0.000 1.000 0.000
#> DRR006473 2 0.0524 0.7708 0.008 0.988 0.004 0.000
#> DRR006474 3 0.1635 0.8213 0.008 0.000 0.948 0.044
#> DRR006475 1 0.6495 0.4026 0.560 0.084 0.356 0.000
#> DRR006476 4 0.6977 0.5284 0.056 0.076 0.216 0.652
#> DRR006477 4 0.3128 0.7505 0.040 0.000 0.076 0.884
#> DRR006478 2 0.3649 0.6778 0.204 0.796 0.000 0.000
#> DRR006479 3 0.4452 0.5572 0.260 0.008 0.732 0.000
#> DRR006480 1 0.6339 0.6183 0.652 0.020 0.060 0.268
#> DRR006481 3 0.0336 0.8345 0.000 0.008 0.992 0.000
#> DRR006482 1 0.6603 0.6197 0.580 0.104 0.000 0.316
#> DRR006483 2 0.4560 0.5299 0.296 0.700 0.004 0.000
#> DRR006484 3 0.0524 0.8346 0.008 0.004 0.988 0.000
#> DRR006485 3 0.0657 0.8334 0.012 0.000 0.984 0.004
#> DRR006486 1 0.5590 0.1673 0.524 0.020 0.456 0.000
#> DRR006487 3 0.0000 0.8352 0.000 0.000 1.000 0.000
#> DRR006488 3 0.9148 0.3132 0.340 0.132 0.396 0.132
#> DRR006489 1 0.4990 0.5376 0.640 0.352 0.000 0.008
#> DRR006490 3 0.1474 0.8162 0.052 0.000 0.948 0.000
#> DRR006491 3 0.1389 0.8188 0.048 0.000 0.952 0.000
#> DRR006492 4 0.6860 0.4730 0.244 0.000 0.164 0.592
#> DRR006493 3 0.0000 0.8352 0.000 0.000 1.000 0.000
#> DRR006494 1 0.6921 0.5947 0.644 0.020 0.152 0.184
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 3 0.0510 0.9453 0.000 0.000 0.984 0.000 0.016
#> DRR006375 2 0.4197 0.7300 0.204 0.760 0.000 0.024 0.012
#> DRR006376 2 0.4375 0.3552 0.000 0.576 0.000 0.420 0.004
#> DRR006377 2 0.4076 0.7545 0.012 0.768 0.000 0.200 0.020
#> DRR006378 2 0.0693 0.8846 0.012 0.980 0.000 0.000 0.008
#> DRR006379 4 0.1329 0.8711 0.004 0.032 0.000 0.956 0.008
#> DRR006380 4 0.2616 0.8436 0.020 0.000 0.000 0.880 0.100
#> DRR006381 1 0.1282 0.8152 0.952 0.000 0.000 0.004 0.044
#> DRR006382 3 0.1331 0.9280 0.040 0.000 0.952 0.000 0.008
#> DRR006383 1 0.2416 0.7845 0.888 0.000 0.100 0.000 0.012
#> DRR006384 4 0.3022 0.8156 0.012 0.000 0.004 0.848 0.136
#> DRR006385 1 0.4802 0.6801 0.720 0.212 0.000 0.008 0.060
#> DRR006386 5 0.1243 0.9018 0.008 0.004 0.000 0.028 0.960
#> DRR006387 4 0.2654 0.8433 0.084 0.000 0.000 0.884 0.032
#> DRR006388 2 0.1768 0.8722 0.072 0.924 0.000 0.000 0.004
#> DRR006389 2 0.1768 0.8722 0.072 0.924 0.000 0.000 0.004
#> DRR006390 2 0.0404 0.8800 0.000 0.988 0.000 0.000 0.012
#> DRR006391 2 0.0404 0.8800 0.000 0.988 0.000 0.000 0.012
#> DRR006392 1 0.1375 0.8256 0.960 0.016 0.008 0.008 0.008
#> DRR006393 1 0.2018 0.8235 0.932 0.012 0.008 0.040 0.008
#> DRR006394 2 0.0451 0.8820 0.000 0.988 0.000 0.004 0.008
#> DRR006395 4 0.0162 0.8773 0.000 0.004 0.000 0.996 0.000
#> DRR006396 1 0.4476 0.7140 0.764 0.016 0.000 0.172 0.048
#> DRR006397 2 0.0703 0.8854 0.024 0.976 0.000 0.000 0.000
#> DRR006398 2 0.0865 0.8850 0.024 0.972 0.000 0.000 0.004
#> DRR006399 4 0.0693 0.8791 0.012 0.000 0.000 0.980 0.008
#> DRR006400 4 0.0693 0.8791 0.012 0.000 0.000 0.980 0.008
#> DRR006401 4 0.0703 0.8761 0.000 0.000 0.000 0.976 0.024
#> DRR006402 4 0.0703 0.8761 0.000 0.000 0.000 0.976 0.024
#> DRR006403 4 0.0579 0.8792 0.008 0.000 0.000 0.984 0.008
#> DRR006404 4 0.0693 0.8757 0.000 0.008 0.000 0.980 0.012
#> DRR006405 2 0.3437 0.7953 0.012 0.808 0.000 0.176 0.004
#> DRR006406 2 0.4044 0.7074 0.012 0.732 0.000 0.252 0.004
#> DRR006407 4 0.3527 0.6918 0.000 0.192 0.000 0.792 0.016
#> DRR006408 4 0.1124 0.8754 0.004 0.000 0.000 0.960 0.036
#> DRR006409 4 0.1628 0.8652 0.056 0.000 0.000 0.936 0.008
#> DRR006410 4 0.1648 0.8736 0.040 0.000 0.000 0.940 0.020
#> DRR006411 2 0.0162 0.8825 0.000 0.996 0.000 0.000 0.004
#> DRR006412 2 0.0566 0.8817 0.004 0.984 0.000 0.000 0.012
#> DRR006413 1 0.0771 0.8222 0.976 0.004 0.000 0.000 0.020
#> DRR006414 1 0.4584 0.5584 0.660 0.000 0.312 0.000 0.028
#> DRR006415 3 0.0693 0.9474 0.012 0.000 0.980 0.000 0.008
#> DRR006416 1 0.4321 0.3226 0.600 0.396 0.004 0.000 0.000
#> DRR006417 3 0.3582 0.6713 0.008 0.224 0.768 0.000 0.000
#> DRR006418 2 0.1121 0.8830 0.044 0.956 0.000 0.000 0.000
#> DRR006419 2 0.3955 0.7768 0.084 0.800 0.116 0.000 0.000
#> DRR006420 1 0.6390 0.4448 0.536 0.260 0.200 0.000 0.004
#> DRR006421 3 0.0324 0.9478 0.004 0.000 0.992 0.000 0.004
#> DRR006422 1 0.1156 0.8234 0.968 0.008 0.008 0.008 0.008
#> DRR006423 2 0.0854 0.8852 0.012 0.976 0.000 0.004 0.008
#> DRR006424 1 0.0992 0.8233 0.968 0.008 0.000 0.000 0.024
#> DRR006425 2 0.4532 0.7378 0.036 0.736 0.000 0.216 0.012
#> DRR006426 2 0.0771 0.8853 0.020 0.976 0.000 0.000 0.004
#> DRR006427 5 0.2629 0.9267 0.004 0.012 0.104 0.000 0.880
#> DRR006428 3 0.0566 0.9472 0.012 0.000 0.984 0.004 0.000
#> DRR006429 2 0.2353 0.8762 0.044 0.916 0.004 0.028 0.008
#> DRR006430 1 0.1473 0.8249 0.956 0.008 0.008 0.020 0.008
#> DRR006431 1 0.3282 0.7664 0.836 0.004 0.008 0.144 0.008
#> DRR006432 2 0.0671 0.8855 0.016 0.980 0.000 0.000 0.004
#> DRR006433 3 0.4183 0.5270 0.000 0.000 0.668 0.324 0.008
#> DRR006434 3 0.0579 0.9454 0.000 0.000 0.984 0.008 0.008
#> DRR006435 5 0.2103 0.9645 0.000 0.020 0.056 0.004 0.920
#> DRR006436 5 0.2171 0.9625 0.000 0.024 0.064 0.000 0.912
#> DRR006437 1 0.5899 0.6896 0.688 0.100 0.000 0.144 0.068
#> DRR006438 3 0.1043 0.9305 0.040 0.000 0.960 0.000 0.000
#> DRR006439 3 0.1121 0.9272 0.044 0.000 0.956 0.000 0.000
#> DRR006440 3 0.1209 0.9372 0.000 0.012 0.964 0.012 0.012
#> DRR006441 2 0.0451 0.8820 0.000 0.988 0.000 0.004 0.008
#> DRR006442 3 0.0290 0.9472 0.008 0.000 0.992 0.000 0.000
#> DRR006443 3 0.0579 0.9454 0.000 0.000 0.984 0.008 0.008
#> DRR006444 5 0.2005 0.9636 0.000 0.016 0.056 0.004 0.924
#> DRR006445 1 0.2938 0.8102 0.880 0.064 0.000 0.008 0.048
#> DRR006446 2 0.0566 0.8851 0.012 0.984 0.000 0.000 0.004
#> DRR006447 2 0.1704 0.8741 0.068 0.928 0.000 0.000 0.004
#> DRR006448 4 0.2914 0.8397 0.076 0.000 0.000 0.872 0.052
#> DRR006449 1 0.6684 0.5375 0.584 0.148 0.000 0.220 0.048
#> DRR006450 1 0.3838 0.7558 0.804 0.148 0.000 0.004 0.044
#> DRR006451 2 0.5636 0.1336 0.012 0.496 0.000 0.444 0.048
#> DRR006452 1 0.1701 0.8184 0.936 0.016 0.000 0.000 0.048
#> DRR006453 1 0.1626 0.8238 0.940 0.044 0.000 0.000 0.016
#> DRR006454 2 0.3226 0.8450 0.056 0.868 0.000 0.016 0.060
#> DRR006455 5 0.1493 0.9346 0.000 0.000 0.028 0.024 0.948
#> DRR006456 3 0.0290 0.9461 0.000 0.000 0.992 0.000 0.008
#> DRR006457 3 0.0579 0.9471 0.008 0.000 0.984 0.008 0.000
#> DRR006458 1 0.2169 0.8192 0.924 0.008 0.012 0.048 0.008
#> DRR006459 4 0.7116 -0.0068 0.412 0.096 0.052 0.432 0.008
#> DRR006460 4 0.0963 0.8745 0.000 0.000 0.000 0.964 0.036
#> DRR006461 3 0.0807 0.9422 0.000 0.000 0.976 0.012 0.012
#> DRR006462 4 0.5823 0.5845 0.064 0.232 0.000 0.656 0.048
#> DRR006463 3 0.0566 0.9449 0.000 0.000 0.984 0.004 0.012
#> DRR006464 2 0.0798 0.8852 0.016 0.976 0.000 0.000 0.008
#> DRR006465 1 0.4028 0.7768 0.816 0.092 0.004 0.080 0.008
#> DRR006466 3 0.0898 0.9404 0.000 0.000 0.972 0.020 0.008
#> DRR006467 1 0.1059 0.8255 0.968 0.020 0.008 0.000 0.004
#> DRR006468 5 0.2103 0.9645 0.000 0.020 0.056 0.004 0.920
#> DRR006469 2 0.0290 0.8817 0.000 0.992 0.000 0.000 0.008
#> DRR006470 2 0.1012 0.8838 0.020 0.968 0.012 0.000 0.000
#> DRR006471 2 0.4683 0.4634 0.356 0.624 0.012 0.000 0.008
#> DRR006472 3 0.1187 0.9362 0.024 0.004 0.964 0.004 0.004
#> DRR006473 2 0.2187 0.8719 0.056 0.920 0.008 0.004 0.012
#> DRR006474 3 0.3413 0.7989 0.136 0.004 0.836 0.012 0.012
#> DRR006475 1 0.4869 0.4965 0.632 0.016 0.340 0.004 0.008
#> DRR006476 4 0.2518 0.8177 0.000 0.016 0.080 0.896 0.008
#> DRR006477 4 0.2388 0.8366 0.000 0.000 0.072 0.900 0.028
#> DRR006478 2 0.3542 0.8443 0.084 0.848 0.004 0.056 0.008
#> DRR006479 3 0.1270 0.9222 0.052 0.000 0.948 0.000 0.000
#> DRR006480 1 0.2242 0.8193 0.920 0.008 0.052 0.012 0.008
#> DRR006481 3 0.0324 0.9483 0.004 0.000 0.992 0.004 0.000
#> DRR006482 1 0.5686 0.6130 0.664 0.028 0.000 0.224 0.084
#> DRR006483 2 0.3515 0.8131 0.144 0.828 0.012 0.008 0.008
#> DRR006484 3 0.0290 0.9472 0.008 0.000 0.992 0.000 0.000
#> DRR006485 3 0.0579 0.9454 0.000 0.000 0.984 0.008 0.008
#> DRR006486 1 0.2116 0.8035 0.912 0.008 0.076 0.000 0.004
#> DRR006487 3 0.0324 0.9478 0.004 0.000 0.992 0.000 0.004
#> DRR006488 5 0.2171 0.9625 0.000 0.024 0.064 0.000 0.912
#> DRR006489 1 0.2050 0.8199 0.920 0.064 0.008 0.000 0.008
#> DRR006490 3 0.0703 0.9420 0.024 0.000 0.976 0.000 0.000
#> DRR006491 3 0.0609 0.9431 0.020 0.000 0.980 0.000 0.000
#> DRR006492 4 0.4534 0.7400 0.052 0.000 0.144 0.776 0.028
#> DRR006493 3 0.0290 0.9461 0.000 0.000 0.992 0.000 0.008
#> DRR006494 1 0.2484 0.8120 0.908 0.008 0.060 0.016 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 3 0.0935 0.9178 0.032 0.000 0.964 0.000 0.004 0.000
#> DRR006375 2 0.6293 0.4313 0.208 0.552 0.000 0.056 0.000 0.184
#> DRR006376 4 0.4851 0.3419 0.056 0.364 0.000 0.576 0.000 0.004
#> DRR006377 4 0.5373 0.2681 0.216 0.196 0.000 0.588 0.000 0.000
#> DRR006378 2 0.1500 0.8302 0.052 0.936 0.000 0.012 0.000 0.000
#> DRR006379 4 0.4950 0.4851 0.012 0.096 0.000 0.664 0.000 0.228
#> DRR006380 6 0.3854 -0.1445 0.000 0.000 0.000 0.464 0.000 0.536
#> DRR006381 6 0.3446 0.4192 0.308 0.000 0.000 0.000 0.000 0.692
#> DRR006382 3 0.1549 0.8983 0.044 0.000 0.936 0.000 0.000 0.020
#> DRR006383 1 0.4040 0.4284 0.688 0.000 0.032 0.000 0.000 0.280
#> DRR006384 4 0.4282 0.2931 0.000 0.000 0.000 0.560 0.020 0.420
#> DRR006385 6 0.3663 0.6309 0.068 0.148 0.000 0.000 0.000 0.784
#> DRR006386 5 0.0000 0.9814 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006387 4 0.3868 0.1889 0.000 0.000 0.000 0.508 0.000 0.492
#> DRR006388 2 0.2333 0.8265 0.024 0.884 0.000 0.000 0.000 0.092
#> DRR006389 2 0.2383 0.8250 0.024 0.880 0.000 0.000 0.000 0.096
#> DRR006390 2 0.0858 0.8406 0.004 0.968 0.000 0.000 0.000 0.028
#> DRR006391 2 0.0858 0.8406 0.004 0.968 0.000 0.000 0.000 0.028
#> DRR006392 1 0.2209 0.6497 0.900 0.024 0.000 0.004 0.000 0.072
#> DRR006393 1 0.2226 0.6745 0.904 0.028 0.000 0.060 0.000 0.008
#> DRR006394 2 0.0972 0.8375 0.028 0.964 0.000 0.008 0.000 0.000
#> DRR006395 4 0.2975 0.6220 0.004 0.004 0.012 0.832 0.000 0.148
#> DRR006396 6 0.2917 0.6081 0.136 0.016 0.000 0.008 0.000 0.840
#> DRR006397 2 0.2482 0.7791 0.004 0.848 0.000 0.000 0.000 0.148
#> DRR006398 2 0.2482 0.7791 0.004 0.848 0.000 0.000 0.000 0.148
#> DRR006399 4 0.3619 0.4983 0.000 0.004 0.000 0.680 0.000 0.316
#> DRR006400 4 0.3584 0.5065 0.000 0.004 0.000 0.688 0.000 0.308
#> DRR006401 4 0.1753 0.6377 0.004 0.000 0.000 0.912 0.000 0.084
#> DRR006402 4 0.1644 0.6384 0.004 0.000 0.000 0.920 0.000 0.076
#> DRR006403 4 0.2340 0.6215 0.000 0.000 0.000 0.852 0.000 0.148
#> DRR006404 4 0.1082 0.6236 0.040 0.004 0.000 0.956 0.000 0.000
#> DRR006405 4 0.5645 0.1164 0.152 0.392 0.000 0.456 0.000 0.000
#> DRR006406 4 0.5164 0.3411 0.116 0.300 0.000 0.584 0.000 0.000
#> DRR006407 4 0.3123 0.5790 0.056 0.112 0.000 0.832 0.000 0.000
#> DRR006408 4 0.3163 0.5665 0.004 0.000 0.000 0.764 0.000 0.232
#> DRR006409 4 0.1588 0.6134 0.072 0.000 0.000 0.924 0.000 0.004
#> DRR006410 4 0.2357 0.6231 0.012 0.000 0.000 0.872 0.000 0.116
#> DRR006411 2 0.2389 0.7875 0.008 0.864 0.000 0.000 0.000 0.128
#> DRR006412 2 0.0914 0.8440 0.016 0.968 0.000 0.000 0.000 0.016
#> DRR006413 1 0.3982 0.0883 0.536 0.004 0.000 0.000 0.000 0.460
#> DRR006414 3 0.5663 0.2527 0.220 0.000 0.532 0.000 0.000 0.248
#> DRR006415 3 0.0622 0.9310 0.008 0.000 0.980 0.000 0.000 0.012
#> DRR006416 1 0.3974 0.6120 0.752 0.188 0.000 0.004 0.000 0.056
#> DRR006417 3 0.4139 0.4221 0.000 0.336 0.640 0.000 0.000 0.024
#> DRR006418 2 0.1471 0.8298 0.064 0.932 0.000 0.000 0.000 0.004
#> DRR006419 2 0.3896 0.6249 0.000 0.744 0.204 0.000 0.000 0.052
#> DRR006420 1 0.4937 0.6025 0.724 0.116 0.064 0.000 0.000 0.096
#> DRR006421 3 0.0146 0.9382 0.000 0.004 0.996 0.000 0.000 0.000
#> DRR006422 1 0.2618 0.6590 0.860 0.000 0.000 0.116 0.000 0.024
#> DRR006423 2 0.3078 0.6957 0.192 0.796 0.000 0.012 0.000 0.000
#> DRR006424 1 0.3717 0.2806 0.616 0.000 0.000 0.000 0.000 0.384
#> DRR006425 4 0.5330 0.2567 0.232 0.176 0.000 0.592 0.000 0.000
#> DRR006426 2 0.2053 0.7995 0.108 0.888 0.000 0.004 0.000 0.000
#> DRR006427 5 0.1814 0.8646 0.100 0.000 0.000 0.000 0.900 0.000
#> DRR006428 3 0.0000 0.9390 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006429 2 0.5387 0.2696 0.316 0.560 0.004 0.120 0.000 0.000
#> DRR006430 1 0.2046 0.6669 0.916 0.008 0.000 0.044 0.000 0.032
#> DRR006431 1 0.4181 0.4779 0.644 0.000 0.000 0.328 0.000 0.028
#> DRR006432 2 0.2100 0.7963 0.112 0.884 0.000 0.004 0.000 0.000
#> DRR006433 4 0.3997 0.0808 0.004 0.000 0.488 0.508 0.000 0.000
#> DRR006434 3 0.0000 0.9390 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006435 5 0.0000 0.9814 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006436 5 0.0000 0.9814 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006437 6 0.1918 0.6339 0.000 0.088 0.000 0.008 0.000 0.904
#> DRR006438 3 0.0000 0.9390 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006439 3 0.0000 0.9390 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006440 3 0.1325 0.9161 0.012 0.012 0.956 0.004 0.000 0.016
#> DRR006441 2 0.2450 0.8002 0.016 0.868 0.000 0.000 0.000 0.116
#> DRR006442 3 0.0458 0.9324 0.016 0.000 0.984 0.000 0.000 0.000
#> DRR006443 3 0.0405 0.9358 0.008 0.004 0.988 0.000 0.000 0.000
#> DRR006444 5 0.0000 0.9814 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006445 6 0.4684 0.3000 0.352 0.056 0.000 0.000 0.000 0.592
#> DRR006446 2 0.1176 0.8446 0.020 0.956 0.000 0.000 0.000 0.024
#> DRR006447 2 0.2357 0.8136 0.012 0.872 0.000 0.000 0.000 0.116
#> DRR006448 6 0.3629 0.3472 0.016 0.000 0.000 0.260 0.000 0.724
#> DRR006449 6 0.4062 0.6163 0.080 0.068 0.000 0.056 0.000 0.796
#> DRR006450 6 0.4823 0.5063 0.216 0.124 0.000 0.000 0.000 0.660
#> DRR006451 6 0.5209 0.1834 0.000 0.416 0.000 0.092 0.000 0.492
#> DRR006452 6 0.3690 0.4523 0.288 0.012 0.000 0.000 0.000 0.700
#> DRR006453 1 0.4829 0.3631 0.612 0.080 0.000 0.000 0.000 0.308
#> DRR006454 6 0.3636 0.4558 0.004 0.320 0.000 0.000 0.000 0.676
#> DRR006455 5 0.0000 0.9814 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006456 3 0.0000 0.9390 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006457 3 0.0146 0.9379 0.000 0.000 0.996 0.004 0.000 0.000
#> DRR006458 1 0.3284 0.6214 0.784 0.020 0.000 0.196 0.000 0.000
#> DRR006459 1 0.4988 0.2117 0.484 0.068 0.000 0.448 0.000 0.000
#> DRR006460 4 0.2092 0.6293 0.000 0.000 0.000 0.876 0.000 0.124
#> DRR006461 3 0.0146 0.9381 0.000 0.000 0.996 0.004 0.000 0.000
#> DRR006462 6 0.4172 0.5419 0.000 0.204 0.000 0.072 0.000 0.724
#> DRR006463 3 0.0520 0.9339 0.008 0.008 0.984 0.000 0.000 0.000
#> DRR006464 2 0.1753 0.8150 0.084 0.912 0.000 0.004 0.000 0.000
#> DRR006465 1 0.4808 0.5031 0.636 0.092 0.000 0.272 0.000 0.000
#> DRR006466 3 0.0405 0.9358 0.008 0.004 0.988 0.000 0.000 0.000
#> DRR006467 1 0.2221 0.6511 0.896 0.032 0.000 0.000 0.000 0.072
#> DRR006468 5 0.0000 0.9814 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006469 2 0.1866 0.8209 0.008 0.908 0.000 0.000 0.000 0.084
#> DRR006470 2 0.1944 0.8353 0.024 0.924 0.036 0.000 0.000 0.016
#> DRR006471 1 0.2400 0.6570 0.872 0.116 0.000 0.004 0.000 0.008
#> DRR006472 3 0.4245 0.4328 0.328 0.024 0.644 0.004 0.000 0.000
#> DRR006473 1 0.4253 0.1001 0.524 0.460 0.000 0.016 0.000 0.000
#> DRR006474 1 0.5570 0.2265 0.476 0.000 0.092 0.420 0.008 0.004
#> DRR006475 1 0.2089 0.6553 0.908 0.012 0.072 0.004 0.000 0.004
#> DRR006476 4 0.2451 0.6355 0.008 0.028 0.036 0.904 0.000 0.024
#> DRR006477 4 0.5054 0.3257 0.000 0.000 0.368 0.548 0.000 0.084
#> DRR006478 1 0.6080 0.2284 0.396 0.288 0.000 0.316 0.000 0.000
#> DRR006479 3 0.0000 0.9390 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006480 1 0.3017 0.6214 0.848 0.000 0.052 0.004 0.000 0.096
#> DRR006481 3 0.0622 0.9272 0.012 0.008 0.980 0.000 0.000 0.000
#> DRR006482 6 0.2265 0.6267 0.004 0.076 0.000 0.024 0.000 0.896
#> DRR006483 1 0.3977 0.5266 0.692 0.284 0.000 0.020 0.000 0.004
#> DRR006484 3 0.0000 0.9390 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006485 3 0.0520 0.9339 0.008 0.008 0.984 0.000 0.000 0.000
#> DRR006486 1 0.2189 0.6447 0.904 0.004 0.032 0.000 0.000 0.060
#> DRR006487 3 0.0000 0.9390 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006488 5 0.0000 0.9814 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006489 1 0.4552 0.4083 0.648 0.064 0.000 0.000 0.000 0.288
#> DRR006490 3 0.0000 0.9390 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006491 3 0.0146 0.9380 0.004 0.000 0.996 0.000 0.000 0.000
#> DRR006492 4 0.5599 0.3981 0.004 0.000 0.216 0.568 0.000 0.212
#> DRR006493 3 0.0000 0.9390 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006494 1 0.2715 0.6659 0.872 0.028 0.012 0.088 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)
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 16187 rows and 121 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 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 0.466 0.749 0.827 0.3804 0.711 0.711
#> 3 3 0.494 0.697 0.845 0.4908 0.722 0.609
#> 4 4 0.492 0.623 0.776 0.1609 0.865 0.696
#> 5 5 0.574 0.662 0.747 0.0987 0.852 0.590
#> 6 6 0.693 0.710 0.781 0.0546 0.947 0.800
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
#> DRR006374 1 0.0000 0.8209 1.000 0.000
#> DRR006375 2 0.0672 0.9340 0.008 0.992
#> DRR006376 1 0.2236 0.8198 0.964 0.036
#> DRR006377 1 0.1843 0.8211 0.972 0.028
#> DRR006378 1 0.0000 0.8209 1.000 0.000
#> DRR006379 1 0.2236 0.8198 0.964 0.036
#> DRR006380 1 0.0000 0.8209 1.000 0.000
#> DRR006381 2 0.3584 0.8792 0.068 0.932
#> DRR006382 1 0.0376 0.8211 0.996 0.004
#> DRR006383 1 0.9460 0.6130 0.636 0.364
#> DRR006384 1 0.0000 0.8209 1.000 0.000
#> DRR006385 1 0.8955 0.6538 0.688 0.312
#> DRR006386 1 0.0000 0.8209 1.000 0.000
#> DRR006387 1 0.9635 0.5673 0.612 0.388
#> DRR006388 1 0.3274 0.8150 0.940 0.060
#> DRR006389 1 0.3274 0.8150 0.940 0.060
#> DRR006390 1 0.0000 0.8209 1.000 0.000
#> DRR006391 1 0.0000 0.8209 1.000 0.000
#> DRR006392 2 0.0000 0.9383 0.000 1.000
#> DRR006393 1 0.8955 0.6669 0.688 0.312
#> DRR006394 1 0.0000 0.8209 1.000 0.000
#> DRR006395 1 0.9944 0.4404 0.544 0.456
#> DRR006396 2 0.6148 0.7798 0.152 0.848
#> DRR006397 1 0.4161 0.8065 0.916 0.084
#> DRR006398 1 0.4161 0.8065 0.916 0.084
#> DRR006399 1 0.2603 0.8187 0.956 0.044
#> DRR006400 1 0.2603 0.8187 0.956 0.044
#> DRR006401 1 0.0000 0.8209 1.000 0.000
#> DRR006402 1 0.0000 0.8209 1.000 0.000
#> DRR006403 1 0.2423 0.8194 0.960 0.040
#> DRR006404 1 0.2603 0.8192 0.956 0.044
#> DRR006405 1 0.1843 0.8211 0.972 0.028
#> DRR006406 1 0.1843 0.8211 0.972 0.028
#> DRR006407 1 0.0000 0.8209 1.000 0.000
#> DRR006408 1 0.0000 0.8209 1.000 0.000
#> DRR006409 1 0.9983 0.4039 0.524 0.476
#> DRR006410 1 0.9635 0.5673 0.612 0.388
#> DRR006411 1 0.3114 0.8168 0.944 0.056
#> DRR006412 1 0.0000 0.8209 1.000 0.000
#> DRR006413 2 0.0000 0.9383 0.000 1.000
#> DRR006414 1 0.9580 0.5928 0.620 0.380
#> DRR006415 1 0.9580 0.5928 0.620 0.380
#> DRR006416 1 0.8909 0.6703 0.692 0.308
#> DRR006417 1 0.9815 0.5295 0.580 0.420
#> DRR006418 1 0.9795 0.5324 0.584 0.416
#> DRR006419 1 0.9580 0.5942 0.620 0.380
#> DRR006420 1 0.9580 0.5942 0.620 0.380
#> DRR006421 1 0.1414 0.8218 0.980 0.020
#> DRR006422 1 0.2948 0.8177 0.948 0.052
#> DRR006423 1 0.0000 0.8209 1.000 0.000
#> DRR006424 2 0.0000 0.9383 0.000 1.000
#> DRR006425 1 0.0000 0.8209 1.000 0.000
#> DRR006426 1 0.9044 0.6588 0.680 0.320
#> DRR006427 1 0.0000 0.8209 1.000 0.000
#> DRR006428 1 0.9944 0.4556 0.544 0.456
#> DRR006429 1 0.0000 0.8209 1.000 0.000
#> DRR006430 2 0.0000 0.9383 0.000 1.000
#> DRR006431 2 0.0000 0.9383 0.000 1.000
#> DRR006432 1 0.9170 0.6484 0.668 0.332
#> DRR006433 1 0.7602 0.7352 0.780 0.220
#> DRR006434 1 0.0000 0.8209 1.000 0.000
#> DRR006435 1 0.0000 0.8209 1.000 0.000
#> DRR006436 1 0.0000 0.8209 1.000 0.000
#> DRR006437 2 0.9983 -0.0984 0.476 0.524
#> DRR006438 1 0.9608 0.5888 0.616 0.384
#> DRR006439 1 0.9209 0.6455 0.664 0.336
#> DRR006440 1 0.0000 0.8209 1.000 0.000
#> DRR006441 1 0.0000 0.8209 1.000 0.000
#> DRR006442 1 0.9732 0.5561 0.596 0.404
#> DRR006443 1 0.0000 0.8209 1.000 0.000
#> DRR006444 1 0.0000 0.8209 1.000 0.000
#> DRR006445 1 0.9850 0.4777 0.572 0.428
#> DRR006446 1 0.0000 0.8209 1.000 0.000
#> DRR006447 1 0.9552 0.5491 0.624 0.376
#> DRR006448 1 0.2603 0.8187 0.956 0.044
#> DRR006449 1 0.9460 0.5628 0.636 0.364
#> DRR006450 1 0.9460 0.5628 0.636 0.364
#> DRR006451 1 0.2236 0.8198 0.964 0.036
#> DRR006452 2 0.4815 0.8397 0.104 0.896
#> DRR006453 1 0.8909 0.6703 0.692 0.308
#> DRR006454 1 0.0376 0.8211 0.996 0.004
#> DRR006455 1 0.0000 0.8209 1.000 0.000
#> DRR006456 1 0.9732 0.5561 0.596 0.404
#> DRR006457 1 0.7602 0.7352 0.780 0.220
#> DRR006458 2 0.0000 0.9383 0.000 1.000
#> DRR006459 2 0.0000 0.9383 0.000 1.000
#> DRR006460 1 0.0000 0.8209 1.000 0.000
#> DRR006461 1 0.1184 0.8218 0.984 0.016
#> DRR006462 2 0.6247 0.7711 0.156 0.844
#> DRR006463 1 0.0000 0.8209 1.000 0.000
#> DRR006464 1 0.0000 0.8209 1.000 0.000
#> DRR006465 1 0.8955 0.6669 0.688 0.312
#> DRR006466 1 0.1184 0.8221 0.984 0.016
#> DRR006467 2 0.0000 0.9383 0.000 1.000
#> DRR006468 1 0.0000 0.8209 1.000 0.000
#> DRR006469 1 0.0000 0.8209 1.000 0.000
#> DRR006470 1 0.9815 0.5295 0.580 0.420
#> DRR006471 2 0.0000 0.9383 0.000 1.000
#> DRR006472 1 0.8955 0.6668 0.688 0.312
#> DRR006473 1 0.0000 0.8209 1.000 0.000
#> DRR006474 1 0.0000 0.8209 1.000 0.000
#> DRR006475 2 0.0000 0.9383 0.000 1.000
#> DRR006476 1 0.1184 0.8218 0.984 0.016
#> DRR006477 1 0.2043 0.8216 0.968 0.032
#> DRR006478 1 0.8955 0.6669 0.688 0.312
#> DRR006479 1 0.9608 0.5888 0.616 0.384
#> DRR006480 2 0.0000 0.9383 0.000 1.000
#> DRR006481 1 0.8813 0.6768 0.700 0.300
#> DRR006482 1 0.8813 0.6631 0.700 0.300
#> DRR006483 2 0.0000 0.9383 0.000 1.000
#> DRR006484 1 0.9552 0.5976 0.624 0.376
#> DRR006485 1 0.0000 0.8209 1.000 0.000
#> DRR006486 2 0.0376 0.9363 0.004 0.996
#> DRR006487 1 0.9775 0.5423 0.588 0.412
#> DRR006488 1 0.0000 0.8209 1.000 0.000
#> DRR006489 2 0.1184 0.9280 0.016 0.984
#> DRR006490 1 0.9775 0.5421 0.588 0.412
#> DRR006491 1 0.9732 0.5561 0.596 0.404
#> DRR006492 1 0.9944 0.4404 0.544 0.456
#> DRR006493 1 0.9710 0.5616 0.600 0.400
#> DRR006494 2 0.0000 0.9383 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006375 1 0.2537 0.8753 0.920 0.000 0.080
#> DRR006376 2 0.3765 0.7998 0.028 0.888 0.084
#> DRR006377 2 0.3370 0.8072 0.024 0.904 0.072
#> DRR006378 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006379 2 0.3765 0.7998 0.028 0.888 0.084
#> DRR006380 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006381 1 0.4605 0.7598 0.796 0.000 0.204
#> DRR006382 2 0.3816 0.7253 0.000 0.852 0.148
#> DRR006383 3 0.5639 0.7260 0.016 0.232 0.752
#> DRR006384 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006385 2 0.9201 0.1035 0.160 0.488 0.352
#> DRR006386 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006387 2 0.9387 0.2122 0.272 0.508 0.220
#> DRR006388 2 0.4840 0.7329 0.016 0.816 0.168
#> DRR006389 2 0.4840 0.7329 0.016 0.816 0.168
#> DRR006390 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006391 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006392 1 0.2066 0.8904 0.940 0.000 0.060
#> DRR006393 2 0.8802 0.3820 0.200 0.584 0.216
#> DRR006394 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006395 3 0.9937 0.3781 0.316 0.296 0.388
#> DRR006396 1 0.6624 0.5904 0.708 0.044 0.248
#> DRR006397 2 0.5891 0.6786 0.036 0.764 0.200
#> DRR006398 2 0.5891 0.6786 0.036 0.764 0.200
#> DRR006399 2 0.3973 0.7968 0.032 0.880 0.088
#> DRR006400 2 0.3973 0.7968 0.032 0.880 0.088
#> DRR006401 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006403 2 0.3850 0.7983 0.028 0.884 0.088
#> DRR006404 2 0.3933 0.7956 0.028 0.880 0.092
#> DRR006405 2 0.3370 0.8072 0.024 0.904 0.072
#> DRR006406 2 0.3370 0.8072 0.024 0.904 0.072
#> DRR006407 2 0.1163 0.8328 0.000 0.972 0.028
#> DRR006408 2 0.0237 0.8374 0.000 0.996 0.004
#> DRR006409 3 0.9340 0.4394 0.308 0.192 0.500
#> DRR006410 2 0.9387 0.2122 0.272 0.508 0.220
#> DRR006411 2 0.6274 0.1041 0.000 0.544 0.456
#> DRR006412 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006413 1 0.1753 0.8789 0.952 0.000 0.048
#> DRR006414 3 0.2599 0.7160 0.016 0.052 0.932
#> DRR006415 3 0.2599 0.7160 0.016 0.052 0.932
#> DRR006416 2 0.8799 0.3856 0.196 0.584 0.220
#> DRR006417 3 0.2031 0.6867 0.032 0.016 0.952
#> DRR006418 3 0.7815 0.6537 0.096 0.260 0.644
#> DRR006419 3 0.6142 0.7336 0.040 0.212 0.748
#> DRR006420 3 0.6232 0.7312 0.040 0.220 0.740
#> DRR006421 2 0.2496 0.8139 0.004 0.928 0.068
#> DRR006422 2 0.4094 0.7896 0.028 0.872 0.100
#> DRR006423 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006424 1 0.2165 0.8894 0.936 0.000 0.064
#> DRR006425 2 0.0424 0.8370 0.000 0.992 0.008
#> DRR006426 3 0.6275 0.5790 0.008 0.348 0.644
#> DRR006427 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006428 3 0.2878 0.6224 0.096 0.000 0.904
#> DRR006429 2 0.0424 0.8370 0.000 0.992 0.008
#> DRR006430 1 0.2066 0.8904 0.940 0.000 0.060
#> DRR006431 1 0.1860 0.8916 0.948 0.000 0.052
#> DRR006432 3 0.4033 0.7297 0.008 0.136 0.856
#> DRR006433 3 0.6168 0.4532 0.000 0.412 0.588
#> DRR006434 2 0.0424 0.8356 0.000 0.992 0.008
#> DRR006435 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006437 1 0.9926 -0.1970 0.376 0.276 0.348
#> DRR006438 3 0.6201 0.7337 0.044 0.208 0.748
#> DRR006439 3 0.6053 0.7058 0.020 0.260 0.720
#> DRR006440 2 0.0237 0.8369 0.000 0.996 0.004
#> DRR006441 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006442 3 0.1919 0.6943 0.024 0.020 0.956
#> DRR006443 2 0.1163 0.8255 0.000 0.972 0.028
#> DRR006444 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006445 2 0.9663 0.0985 0.308 0.456 0.236
#> DRR006446 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006447 2 0.9716 -0.0614 0.228 0.428 0.344
#> DRR006448 2 0.4056 0.7945 0.032 0.876 0.092
#> DRR006449 2 0.9651 -0.0448 0.216 0.436 0.348
#> DRR006450 2 0.9651 -0.0448 0.216 0.436 0.348
#> DRR006451 2 0.3765 0.7998 0.028 0.888 0.084
#> DRR006452 1 0.5945 0.6869 0.740 0.024 0.236
#> DRR006453 2 0.8799 0.3856 0.196 0.584 0.220
#> DRR006454 2 0.1031 0.8350 0.000 0.976 0.024
#> DRR006455 2 0.0892 0.8277 0.000 0.980 0.020
#> DRR006456 3 0.1919 0.6943 0.024 0.020 0.956
#> DRR006457 3 0.6168 0.4532 0.000 0.412 0.588
#> DRR006458 1 0.1860 0.8916 0.948 0.000 0.052
#> DRR006459 1 0.1860 0.8916 0.948 0.000 0.052
#> DRR006460 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006461 2 0.1163 0.8339 0.000 0.972 0.028
#> DRR006462 1 0.6490 0.5780 0.708 0.036 0.256
#> DRR006463 2 0.1163 0.8255 0.000 0.972 0.028
#> DRR006464 2 0.0424 0.8370 0.000 0.992 0.008
#> DRR006465 2 0.8802 0.3820 0.200 0.584 0.216
#> DRR006466 2 0.2261 0.8157 0.000 0.932 0.068
#> DRR006467 1 0.1411 0.8740 0.964 0.000 0.036
#> DRR006468 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006469 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006470 3 0.2031 0.6867 0.032 0.016 0.952
#> DRR006471 1 0.1411 0.8740 0.964 0.000 0.036
#> DRR006472 3 0.6318 0.5650 0.008 0.356 0.636
#> DRR006473 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006474 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006475 1 0.1860 0.8916 0.948 0.000 0.052
#> DRR006476 2 0.1163 0.8339 0.000 0.972 0.028
#> DRR006477 2 0.4504 0.6856 0.000 0.804 0.196
#> DRR006478 2 0.8802 0.3820 0.200 0.584 0.216
#> DRR006479 3 0.6201 0.7337 0.044 0.208 0.748
#> DRR006480 1 0.1860 0.8916 0.948 0.000 0.052
#> DRR006481 3 0.5929 0.6261 0.004 0.320 0.676
#> DRR006482 2 0.9074 0.1343 0.148 0.500 0.352
#> DRR006483 1 0.1860 0.8916 0.948 0.000 0.052
#> DRR006484 3 0.2599 0.7167 0.016 0.052 0.932
#> DRR006485 2 0.1163 0.8255 0.000 0.972 0.028
#> DRR006486 1 0.1860 0.8712 0.948 0.000 0.052
#> DRR006487 3 0.2031 0.6853 0.032 0.016 0.952
#> DRR006488 2 0.0000 0.8377 0.000 1.000 0.000
#> DRR006489 1 0.2625 0.8783 0.916 0.000 0.084
#> DRR006490 3 0.1905 0.6892 0.028 0.016 0.956
#> DRR006491 3 0.1919 0.6943 0.024 0.020 0.956
#> DRR006492 3 0.9937 0.3781 0.316 0.296 0.388
#> DRR006493 3 0.2187 0.7005 0.024 0.028 0.948
#> DRR006494 1 0.1860 0.8916 0.948 0.000 0.052
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.0188 0.79217 0.000 0.996 0.000 0.004
#> DRR006375 1 0.4535 0.83514 0.704 0.000 0.004 0.292
#> DRR006376 2 0.4925 0.27169 0.000 0.572 0.000 0.428
#> DRR006377 2 0.4855 0.32753 0.000 0.600 0.000 0.400
#> DRR006378 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006379 2 0.4933 0.26458 0.000 0.568 0.000 0.432
#> DRR006380 2 0.0188 0.79217 0.000 0.996 0.000 0.004
#> DRR006381 1 0.5600 0.70278 0.596 0.000 0.028 0.376
#> DRR006382 2 0.3351 0.65205 0.000 0.844 0.148 0.008
#> DRR006383 3 0.4635 0.63318 0.000 0.216 0.756 0.028
#> DRR006384 2 0.0188 0.79217 0.000 0.996 0.000 0.004
#> DRR006385 4 0.6397 0.62247 0.004 0.256 0.100 0.640
#> DRR006386 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006387 4 0.6230 0.67581 0.088 0.256 0.004 0.652
#> DRR006388 2 0.6233 0.19845 0.000 0.552 0.060 0.388
#> DRR006389 2 0.6233 0.19845 0.000 0.552 0.060 0.388
#> DRR006390 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006391 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006392 1 0.4088 0.87685 0.764 0.000 0.004 0.232
#> DRR006393 4 0.5266 0.65968 0.020 0.264 0.012 0.704
#> DRR006394 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006395 4 0.7869 0.46759 0.096 0.116 0.184 0.604
#> DRR006396 4 0.6337 -0.35970 0.420 0.020 0.028 0.532
#> DRR006397 2 0.6438 0.00706 0.000 0.496 0.068 0.436
#> DRR006398 2 0.6438 0.00706 0.000 0.496 0.068 0.436
#> DRR006399 2 0.4948 0.24618 0.000 0.560 0.000 0.440
#> DRR006400 2 0.4948 0.24618 0.000 0.560 0.000 0.440
#> DRR006401 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006402 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006403 2 0.4941 0.25535 0.000 0.564 0.000 0.436
#> DRR006404 2 0.4948 0.24385 0.000 0.560 0.000 0.440
#> DRR006405 2 0.4888 0.30599 0.000 0.588 0.000 0.412
#> DRR006406 2 0.4888 0.30599 0.000 0.588 0.000 0.412
#> DRR006407 2 0.4164 0.54316 0.000 0.736 0.000 0.264
#> DRR006408 2 0.0707 0.78659 0.000 0.980 0.000 0.020
#> DRR006409 4 0.8389 0.14202 0.116 0.072 0.348 0.464
#> DRR006410 4 0.6230 0.67581 0.088 0.256 0.004 0.652
#> DRR006411 2 0.7864 -0.19710 0.000 0.392 0.320 0.288
#> DRR006412 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006413 1 0.1661 0.76337 0.944 0.000 0.004 0.052
#> DRR006414 3 0.1820 0.72082 0.000 0.036 0.944 0.020
#> DRR006415 3 0.1820 0.72082 0.000 0.036 0.944 0.020
#> DRR006416 4 0.5216 0.65383 0.016 0.272 0.012 0.700
#> DRR006417 3 0.3774 0.69968 0.008 0.004 0.820 0.168
#> DRR006418 3 0.7887 0.35999 0.036 0.124 0.496 0.344
#> DRR006419 3 0.6240 0.64220 0.004 0.108 0.668 0.220
#> DRR006420 3 0.6343 0.63297 0.004 0.116 0.660 0.220
#> DRR006421 2 0.2675 0.74352 0.000 0.908 0.048 0.044
#> DRR006422 2 0.5126 0.22011 0.000 0.552 0.004 0.444
#> DRR006423 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006424 1 0.4262 0.87428 0.756 0.000 0.008 0.236
#> DRR006425 2 0.0336 0.79150 0.000 0.992 0.000 0.008
#> DRR006426 3 0.7357 0.45113 0.000 0.260 0.524 0.216
#> DRR006427 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006428 3 0.3697 0.66948 0.048 0.000 0.852 0.100
#> DRR006429 2 0.0336 0.79150 0.000 0.992 0.000 0.008
#> DRR006430 1 0.4088 0.87685 0.764 0.000 0.004 0.232
#> DRR006431 1 0.3791 0.88455 0.796 0.000 0.004 0.200
#> DRR006432 3 0.5599 0.66487 0.000 0.072 0.700 0.228
#> DRR006433 3 0.7441 0.38485 0.000 0.320 0.488 0.192
#> DRR006434 2 0.0336 0.79037 0.000 0.992 0.008 0.000
#> DRR006435 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006436 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006437 4 0.8258 0.45828 0.216 0.124 0.100 0.560
#> DRR006438 3 0.6219 0.64531 0.004 0.104 0.668 0.224
#> DRR006439 3 0.6786 0.58194 0.004 0.132 0.608 0.256
#> DRR006440 2 0.0376 0.79033 0.000 0.992 0.004 0.004
#> DRR006441 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006442 3 0.0524 0.71217 0.000 0.008 0.988 0.004
#> DRR006443 2 0.1256 0.77465 0.000 0.964 0.028 0.008
#> DRR006444 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006445 4 0.4978 0.67017 0.056 0.160 0.008 0.776
#> DRR006446 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006447 4 0.7148 0.62897 0.056 0.208 0.092 0.644
#> DRR006448 2 0.4955 0.23677 0.000 0.556 0.000 0.444
#> DRR006449 4 0.7297 0.62409 0.056 0.220 0.096 0.628
#> DRR006450 4 0.7297 0.62409 0.056 0.220 0.096 0.628
#> DRR006451 2 0.4933 0.26458 0.000 0.568 0.000 0.432
#> DRR006452 1 0.6044 0.56361 0.528 0.008 0.028 0.436
#> DRR006453 4 0.5216 0.65383 0.016 0.272 0.012 0.700
#> DRR006454 2 0.1888 0.76563 0.000 0.940 0.016 0.044
#> DRR006455 2 0.0707 0.77901 0.000 0.980 0.000 0.020
#> DRR006456 3 0.0524 0.71217 0.000 0.008 0.988 0.004
#> DRR006457 3 0.7441 0.38485 0.000 0.320 0.488 0.192
#> DRR006458 1 0.3751 0.88508 0.800 0.000 0.004 0.196
#> DRR006459 1 0.3751 0.88508 0.800 0.000 0.004 0.196
#> DRR006460 2 0.0188 0.79217 0.000 0.996 0.000 0.004
#> DRR006461 2 0.1297 0.77981 0.000 0.964 0.020 0.016
#> DRR006462 4 0.5708 -0.34854 0.416 0.000 0.028 0.556
#> DRR006463 2 0.1109 0.77417 0.000 0.968 0.028 0.004
#> DRR006464 2 0.0336 0.79150 0.000 0.992 0.000 0.008
#> DRR006465 4 0.5266 0.65968 0.020 0.264 0.012 0.704
#> DRR006466 2 0.2586 0.74707 0.000 0.912 0.048 0.040
#> DRR006467 1 0.1211 0.75475 0.960 0.000 0.000 0.040
#> DRR006468 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006469 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006470 3 0.3774 0.69968 0.008 0.004 0.820 0.168
#> DRR006471 1 0.1211 0.75475 0.960 0.000 0.000 0.040
#> DRR006472 3 0.7389 0.43618 0.000 0.272 0.516 0.212
#> DRR006473 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006474 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006475 1 0.3751 0.88508 0.800 0.000 0.004 0.196
#> DRR006476 2 0.1297 0.77981 0.000 0.964 0.020 0.016
#> DRR006477 2 0.4798 0.57411 0.000 0.768 0.180 0.052
#> DRR006478 4 0.5266 0.65968 0.020 0.264 0.012 0.704
#> DRR006479 3 0.6219 0.64531 0.004 0.104 0.668 0.224
#> DRR006480 1 0.3751 0.88508 0.800 0.000 0.004 0.196
#> DRR006481 3 0.7093 0.53691 0.000 0.220 0.568 0.212
#> DRR006482 4 0.6473 0.61153 0.004 0.268 0.100 0.628
#> DRR006483 1 0.3751 0.88508 0.800 0.000 0.004 0.196
#> DRR006484 3 0.2882 0.72379 0.000 0.024 0.892 0.084
#> DRR006485 2 0.1109 0.77417 0.000 0.968 0.028 0.004
#> DRR006486 1 0.2611 0.71159 0.896 0.000 0.008 0.096
#> DRR006487 3 0.0376 0.70785 0.000 0.004 0.992 0.004
#> DRR006488 2 0.0000 0.79382 0.000 1.000 0.000 0.000
#> DRR006489 1 0.4511 0.85309 0.724 0.000 0.008 0.268
#> DRR006490 3 0.0524 0.71045 0.000 0.004 0.988 0.008
#> DRR006491 3 0.0524 0.71217 0.000 0.008 0.988 0.004
#> DRR006492 4 0.7869 0.46759 0.096 0.116 0.184 0.604
#> DRR006493 3 0.0779 0.71574 0.000 0.016 0.980 0.004
#> DRR006494 1 0.3751 0.88508 0.800 0.000 0.004 0.196
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.0290 0.9475 0.000 0.992 0.000 0.008 0.000
#> DRR006375 1 0.5044 0.5174 0.504 0.000 0.000 0.032 0.464
#> DRR006376 4 0.3741 0.6889 0.000 0.264 0.000 0.732 0.004
#> DRR006377 4 0.4583 0.6622 0.000 0.296 0.000 0.672 0.032
#> DRR006378 2 0.0290 0.9472 0.000 0.992 0.000 0.008 0.000
#> DRR006379 4 0.3689 0.6911 0.000 0.256 0.000 0.740 0.004
#> DRR006380 2 0.0290 0.9475 0.000 0.992 0.000 0.008 0.000
#> DRR006381 5 0.5799 -0.0302 0.416 0.000 0.000 0.092 0.492
#> DRR006382 2 0.3001 0.7850 0.000 0.844 0.144 0.008 0.004
#> DRR006383 3 0.4703 0.5856 0.000 0.212 0.732 0.032 0.024
#> DRR006384 2 0.0290 0.9475 0.000 0.992 0.000 0.008 0.000
#> DRR006385 4 0.5802 0.4748 0.000 0.092 0.008 0.588 0.312
#> DRR006386 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006387 4 0.6632 0.4501 0.024 0.136 0.000 0.516 0.324
#> DRR006388 4 0.5203 0.6461 0.000 0.264 0.004 0.660 0.072
#> DRR006389 4 0.5203 0.6461 0.000 0.264 0.004 0.660 0.072
#> DRR006390 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006391 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006392 1 0.4597 0.6682 0.564 0.000 0.000 0.012 0.424
#> DRR006393 4 0.6070 0.5588 0.000 0.144 0.008 0.592 0.256
#> DRR006394 2 0.0290 0.9472 0.000 0.992 0.000 0.008 0.000
#> DRR006395 4 0.6832 -0.0593 0.020 0.024 0.084 0.440 0.432
#> DRR006396 5 0.6262 0.5153 0.244 0.004 0.000 0.192 0.560
#> DRR006397 4 0.5163 0.6651 0.000 0.224 0.004 0.684 0.088
#> DRR006398 4 0.5163 0.6651 0.000 0.224 0.004 0.684 0.088
#> DRR006399 4 0.3934 0.6918 0.000 0.244 0.000 0.740 0.016
#> DRR006400 4 0.3934 0.6918 0.000 0.244 0.000 0.740 0.016
#> DRR006401 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006402 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006403 4 0.3961 0.6915 0.000 0.248 0.000 0.736 0.016
#> DRR006404 4 0.3863 0.6927 0.000 0.248 0.000 0.740 0.012
#> DRR006405 4 0.4428 0.6815 0.000 0.268 0.000 0.700 0.032
#> DRR006406 4 0.4428 0.6815 0.000 0.268 0.000 0.700 0.032
#> DRR006407 2 0.5046 -0.2870 0.000 0.500 0.000 0.468 0.032
#> DRR006408 2 0.1571 0.9068 0.000 0.936 0.000 0.060 0.004
#> DRR006409 5 0.7971 0.1158 0.052 0.012 0.328 0.240 0.368
#> DRR006410 4 0.6632 0.4501 0.024 0.136 0.000 0.516 0.324
#> DRR006411 4 0.7126 0.1978 0.000 0.180 0.060 0.536 0.224
#> DRR006412 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006413 1 0.1671 0.4172 0.924 0.000 0.000 0.000 0.076
#> DRR006414 3 0.2082 0.6398 0.000 0.032 0.928 0.024 0.016
#> DRR006415 3 0.2082 0.6398 0.000 0.032 0.928 0.024 0.016
#> DRR006416 4 0.6117 0.5655 0.000 0.152 0.008 0.588 0.252
#> DRR006417 3 0.6506 0.5953 0.004 0.000 0.504 0.196 0.296
#> DRR006418 3 0.8108 0.3936 0.016 0.060 0.356 0.344 0.224
#> DRR006419 3 0.7217 0.6141 0.000 0.048 0.476 0.308 0.168
#> DRR006420 3 0.7316 0.6103 0.000 0.056 0.472 0.304 0.168
#> DRR006421 2 0.3093 0.8563 0.000 0.872 0.032 0.080 0.016
#> DRR006422 4 0.4116 0.6939 0.000 0.248 0.004 0.732 0.016
#> DRR006423 2 0.0290 0.9472 0.000 0.992 0.000 0.008 0.000
#> DRR006424 1 0.4610 0.6570 0.556 0.000 0.000 0.012 0.432
#> DRR006425 2 0.1082 0.9336 0.000 0.964 0.000 0.028 0.008
#> DRR006426 3 0.8217 0.4916 0.000 0.200 0.376 0.284 0.140
#> DRR006427 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006428 3 0.3800 0.5729 0.052 0.000 0.824 0.012 0.112
#> DRR006429 2 0.1082 0.9336 0.000 0.964 0.000 0.028 0.008
#> DRR006430 1 0.4597 0.6682 0.564 0.000 0.000 0.012 0.424
#> DRR006431 1 0.4182 0.7054 0.600 0.000 0.000 0.000 0.400
#> DRR006432 3 0.7408 0.5954 0.000 0.036 0.416 0.296 0.252
#> DRR006433 3 0.8178 0.4384 0.000 0.248 0.356 0.284 0.112
#> DRR006434 2 0.0290 0.9457 0.000 0.992 0.008 0.000 0.000
#> DRR006435 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006436 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006437 4 0.6683 -0.1590 0.156 0.004 0.008 0.460 0.372
#> DRR006438 3 0.7184 0.6146 0.000 0.044 0.476 0.308 0.172
#> DRR006439 3 0.7394 0.5755 0.000 0.060 0.440 0.340 0.160
#> DRR006440 2 0.0324 0.9455 0.000 0.992 0.004 0.004 0.000
#> DRR006441 2 0.0404 0.9463 0.000 0.988 0.000 0.012 0.000
#> DRR006442 3 0.0162 0.6273 0.000 0.004 0.996 0.000 0.000
#> DRR006443 2 0.1195 0.9292 0.000 0.960 0.028 0.012 0.000
#> DRR006444 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006445 4 0.6248 0.3629 0.016 0.080 0.008 0.544 0.352
#> DRR006446 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006447 4 0.6328 0.3640 0.028 0.068 0.008 0.548 0.348
#> DRR006448 4 0.3999 0.6918 0.000 0.240 0.000 0.740 0.020
#> DRR006449 4 0.6305 0.3760 0.028 0.068 0.008 0.556 0.340
#> DRR006450 4 0.6305 0.3760 0.028 0.068 0.008 0.556 0.340
#> DRR006451 4 0.3689 0.6911 0.000 0.256 0.000 0.740 0.004
#> DRR006452 5 0.6075 0.3253 0.356 0.000 0.000 0.132 0.512
#> DRR006453 4 0.6117 0.5655 0.000 0.152 0.008 0.588 0.252
#> DRR006454 2 0.2241 0.8833 0.000 0.908 0.008 0.076 0.008
#> DRR006455 2 0.0798 0.9398 0.000 0.976 0.000 0.008 0.016
#> DRR006456 3 0.0162 0.6273 0.000 0.004 0.996 0.000 0.000
#> DRR006457 3 0.8178 0.4384 0.000 0.248 0.356 0.284 0.112
#> DRR006458 1 0.4171 0.7077 0.604 0.000 0.000 0.000 0.396
#> DRR006459 1 0.4171 0.7077 0.604 0.000 0.000 0.000 0.396
#> DRR006460 2 0.0404 0.9482 0.000 0.988 0.000 0.012 0.000
#> DRR006461 2 0.1836 0.9149 0.000 0.936 0.016 0.040 0.008
#> DRR006462 5 0.6114 0.5192 0.244 0.000 0.000 0.192 0.564
#> DRR006463 2 0.0955 0.9319 0.000 0.968 0.028 0.004 0.000
#> DRR006464 2 0.1082 0.9336 0.000 0.964 0.000 0.028 0.008
#> DRR006465 4 0.6070 0.5588 0.000 0.144 0.008 0.592 0.256
#> DRR006466 2 0.2930 0.8648 0.000 0.880 0.032 0.076 0.012
#> DRR006467 1 0.0000 0.4211 1.000 0.000 0.000 0.000 0.000
#> DRR006468 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006469 2 0.0404 0.9463 0.000 0.988 0.000 0.012 0.000
#> DRR006470 3 0.6506 0.5953 0.004 0.000 0.504 0.196 0.296
#> DRR006471 1 0.0000 0.4211 1.000 0.000 0.000 0.000 0.000
#> DRR006472 3 0.8238 0.4828 0.000 0.204 0.368 0.288 0.140
#> DRR006473 2 0.0290 0.9472 0.000 0.992 0.000 0.008 0.000
#> DRR006474 2 0.0290 0.9472 0.000 0.992 0.000 0.008 0.000
#> DRR006475 1 0.4171 0.7077 0.604 0.000 0.000 0.000 0.396
#> DRR006476 2 0.1836 0.9149 0.000 0.936 0.016 0.040 0.008
#> DRR006477 2 0.5023 0.6418 0.000 0.732 0.168 0.080 0.020
#> DRR006478 4 0.6070 0.5588 0.000 0.144 0.008 0.592 0.256
#> DRR006479 3 0.7184 0.6146 0.000 0.044 0.476 0.308 0.172
#> DRR006480 1 0.4171 0.7077 0.604 0.000 0.000 0.000 0.396
#> DRR006481 3 0.7904 0.5597 0.000 0.160 0.432 0.284 0.124
#> DRR006482 4 0.5735 0.4854 0.000 0.092 0.008 0.604 0.296
#> DRR006483 1 0.4171 0.7077 0.604 0.000 0.000 0.000 0.396
#> DRR006484 3 0.3661 0.6545 0.000 0.012 0.836 0.096 0.056
#> DRR006485 2 0.0955 0.9319 0.000 0.968 0.028 0.004 0.000
#> DRR006486 1 0.2127 0.3224 0.892 0.000 0.000 0.000 0.108
#> DRR006487 3 0.0510 0.6240 0.000 0.000 0.984 0.000 0.016
#> DRR006488 2 0.0162 0.9487 0.000 0.996 0.000 0.004 0.000
#> DRR006489 1 0.4957 0.5811 0.528 0.000 0.000 0.028 0.444
#> DRR006490 3 0.0162 0.6255 0.000 0.000 0.996 0.000 0.004
#> DRR006491 3 0.0162 0.6273 0.000 0.004 0.996 0.000 0.000
#> DRR006492 4 0.6832 -0.0593 0.020 0.024 0.084 0.440 0.432
#> DRR006493 3 0.0404 0.6317 0.000 0.012 0.988 0.000 0.000
#> DRR006494 1 0.4171 0.7077 0.604 0.000 0.000 0.000 0.396
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.0551 0.9548 0.000 0.984 0.008 0.004 0.000 0.004
#> DRR006375 1 0.1871 0.7654 0.928 0.000 0.024 0.032 0.016 0.000
#> DRR006376 4 0.1500 0.6753 0.000 0.052 0.000 0.936 0.000 0.012
#> DRR006377 4 0.2775 0.6199 0.000 0.104 0.000 0.856 0.000 0.040
#> DRR006378 2 0.0458 0.9558 0.000 0.984 0.000 0.016 0.000 0.000
#> DRR006379 4 0.1367 0.6767 0.000 0.044 0.000 0.944 0.000 0.012
#> DRR006380 2 0.0551 0.9548 0.000 0.984 0.008 0.004 0.000 0.004
#> DRR006381 1 0.4855 0.6181 0.760 0.000 0.072 0.064 0.076 0.028
#> DRR006382 2 0.3103 0.8043 0.000 0.836 0.100 0.000 0.000 0.064
#> DRR006383 3 0.6003 0.3232 0.000 0.200 0.520 0.008 0.004 0.268
#> DRR006384 2 0.0551 0.9548 0.000 0.984 0.008 0.004 0.000 0.004
#> DRR006385 4 0.7331 0.5083 0.084 0.020 0.104 0.560 0.056 0.176
#> DRR006386 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006387 4 0.6370 0.5750 0.184 0.012 0.120 0.616 0.048 0.020
#> DRR006388 4 0.4616 0.5665 0.004 0.120 0.004 0.728 0.004 0.140
#> DRR006389 4 0.4616 0.5665 0.004 0.120 0.004 0.728 0.004 0.140
#> DRR006390 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006391 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006392 1 0.0363 0.7989 0.988 0.000 0.000 0.012 0.000 0.000
#> DRR006393 4 0.6162 0.6196 0.112 0.024 0.112 0.672 0.044 0.036
#> DRR006394 2 0.0458 0.9558 0.000 0.984 0.000 0.016 0.000 0.000
#> DRR006395 4 0.8176 0.1270 0.268 0.008 0.100 0.328 0.040 0.256
#> DRR006396 1 0.6631 0.4508 0.592 0.000 0.128 0.164 0.084 0.032
#> DRR006397 4 0.4647 0.6104 0.004 0.076 0.028 0.748 0.004 0.140
#> DRR006398 4 0.4647 0.6104 0.004 0.076 0.028 0.748 0.004 0.140
#> DRR006399 4 0.1268 0.6791 0.008 0.036 0.000 0.952 0.000 0.004
#> DRR006400 4 0.1268 0.6791 0.008 0.036 0.000 0.952 0.000 0.004
#> DRR006401 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006402 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006403 4 0.1155 0.6784 0.004 0.036 0.000 0.956 0.000 0.004
#> DRR006404 4 0.1723 0.6772 0.004 0.048 0.000 0.932 0.004 0.012
#> DRR006405 4 0.2129 0.6583 0.000 0.056 0.000 0.904 0.000 0.040
#> DRR006406 4 0.2129 0.6583 0.000 0.056 0.000 0.904 0.000 0.040
#> DRR006407 4 0.4300 0.3358 0.000 0.324 0.000 0.640 0.000 0.036
#> DRR006408 2 0.1897 0.9066 0.000 0.908 0.004 0.084 0.000 0.004
#> DRR006409 3 0.8289 -0.0573 0.228 0.004 0.360 0.220 0.052 0.136
#> DRR006410 4 0.6370 0.5750 0.184 0.012 0.120 0.616 0.048 0.020
#> DRR006411 6 0.5902 0.0215 0.000 0.144 0.000 0.388 0.012 0.456
#> DRR006412 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006413 5 0.4032 0.6557 0.420 0.000 0.000 0.008 0.572 0.000
#> DRR006414 3 0.3932 0.7079 0.000 0.028 0.720 0.000 0.004 0.248
#> DRR006415 3 0.3932 0.7079 0.000 0.028 0.720 0.000 0.004 0.248
#> DRR006416 4 0.6330 0.6195 0.108 0.036 0.112 0.664 0.044 0.036
#> DRR006417 6 0.3158 0.3076 0.000 0.000 0.164 0.004 0.020 0.812
#> DRR006418 6 0.5972 0.5078 0.088 0.024 0.020 0.252 0.008 0.608
#> DRR006419 6 0.5344 0.6105 0.016 0.036 0.108 0.144 0.000 0.696
#> DRR006420 6 0.5429 0.6136 0.016 0.044 0.104 0.144 0.000 0.692
#> DRR006421 2 0.3359 0.8566 0.004 0.848 0.036 0.068 0.000 0.044
#> DRR006422 4 0.1904 0.6770 0.004 0.048 0.000 0.924 0.004 0.020
#> DRR006423 2 0.0458 0.9558 0.000 0.984 0.000 0.016 0.000 0.000
#> DRR006424 1 0.0653 0.7974 0.980 0.000 0.000 0.012 0.004 0.004
#> DRR006425 2 0.1333 0.9360 0.000 0.944 0.000 0.048 0.000 0.008
#> DRR006426 6 0.5634 0.5784 0.000 0.188 0.032 0.140 0.004 0.636
#> DRR006427 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006428 3 0.4797 0.6134 0.012 0.000 0.640 0.000 0.056 0.292
#> DRR006429 2 0.1333 0.9360 0.000 0.944 0.000 0.048 0.000 0.008
#> DRR006430 1 0.0363 0.7989 0.988 0.000 0.000 0.012 0.000 0.000
#> DRR006431 1 0.0790 0.7968 0.968 0.000 0.000 0.000 0.032 0.000
#> DRR006432 6 0.3653 0.5440 0.000 0.032 0.032 0.096 0.012 0.828
#> DRR006433 6 0.6645 0.4916 0.000 0.232 0.116 0.128 0.000 0.524
#> DRR006434 2 0.0748 0.9521 0.000 0.976 0.016 0.004 0.000 0.004
#> DRR006435 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006436 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006437 4 0.8016 0.2002 0.296 0.000 0.092 0.368 0.068 0.176
#> DRR006438 6 0.5276 0.6071 0.016 0.032 0.108 0.144 0.000 0.700
#> DRR006439 6 0.5558 0.6016 0.016 0.048 0.092 0.168 0.000 0.676
#> DRR006440 2 0.0260 0.9545 0.000 0.992 0.008 0.000 0.000 0.000
#> DRR006441 2 0.0547 0.9552 0.000 0.980 0.000 0.020 0.000 0.000
#> DRR006442 3 0.2996 0.7657 0.000 0.000 0.772 0.000 0.000 0.228
#> DRR006443 2 0.1268 0.9353 0.000 0.952 0.036 0.008 0.000 0.004
#> DRR006444 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006445 4 0.6913 0.5485 0.208 0.016 0.112 0.572 0.056 0.036
#> DRR006446 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006447 4 0.7736 0.4605 0.144 0.016 0.108 0.508 0.052 0.172
#> DRR006448 4 0.1194 0.6787 0.008 0.032 0.000 0.956 0.000 0.004
#> DRR006449 4 0.7674 0.4634 0.140 0.016 0.096 0.520 0.060 0.168
#> DRR006450 4 0.7674 0.4634 0.140 0.016 0.096 0.520 0.060 0.168
#> DRR006451 4 0.1367 0.6767 0.000 0.044 0.000 0.944 0.000 0.012
#> DRR006452 1 0.5703 0.5535 0.692 0.000 0.092 0.104 0.080 0.032
#> DRR006453 4 0.6330 0.6195 0.108 0.036 0.112 0.664 0.044 0.036
#> DRR006454 2 0.2587 0.8608 0.000 0.868 0.004 0.108 0.000 0.020
#> DRR006455 2 0.0951 0.9510 0.000 0.968 0.004 0.008 0.000 0.020
#> DRR006456 3 0.2996 0.7657 0.000 0.000 0.772 0.000 0.000 0.228
#> DRR006457 6 0.6645 0.4916 0.000 0.232 0.116 0.128 0.000 0.524
#> DRR006458 1 0.0865 0.7965 0.964 0.000 0.000 0.000 0.036 0.000
#> DRR006459 1 0.0865 0.7965 0.964 0.000 0.000 0.000 0.036 0.000
#> DRR006460 2 0.0363 0.9573 0.000 0.988 0.000 0.012 0.000 0.000
#> DRR006461 2 0.1642 0.9281 0.000 0.936 0.004 0.032 0.000 0.028
#> DRR006462 1 0.6688 0.4394 0.588 0.000 0.128 0.160 0.092 0.032
#> DRR006463 2 0.1010 0.9388 0.000 0.960 0.036 0.000 0.000 0.004
#> DRR006464 2 0.1333 0.9360 0.000 0.944 0.000 0.048 0.000 0.008
#> DRR006465 4 0.6162 0.6196 0.112 0.024 0.112 0.672 0.044 0.036
#> DRR006466 2 0.3160 0.8654 0.000 0.856 0.036 0.064 0.000 0.044
#> DRR006467 5 0.3563 0.8227 0.336 0.000 0.000 0.000 0.664 0.000
#> DRR006468 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006469 2 0.0547 0.9552 0.000 0.980 0.000 0.020 0.000 0.000
#> DRR006470 6 0.3158 0.3076 0.000 0.000 0.164 0.004 0.020 0.812
#> DRR006471 5 0.3563 0.8227 0.336 0.000 0.000 0.000 0.664 0.000
#> DRR006472 6 0.5559 0.5769 0.000 0.192 0.032 0.144 0.000 0.632
#> DRR006473 2 0.0458 0.9558 0.000 0.984 0.000 0.016 0.000 0.000
#> DRR006474 2 0.0458 0.9558 0.000 0.984 0.000 0.016 0.000 0.000
#> DRR006475 1 0.0865 0.7965 0.964 0.000 0.000 0.000 0.036 0.000
#> DRR006476 2 0.1642 0.9281 0.000 0.936 0.004 0.032 0.000 0.028
#> DRR006477 2 0.4900 0.6551 0.000 0.724 0.112 0.052 0.000 0.112
#> DRR006478 4 0.6162 0.6196 0.112 0.024 0.112 0.672 0.044 0.036
#> DRR006479 6 0.5276 0.6071 0.016 0.032 0.108 0.144 0.000 0.700
#> DRR006480 1 0.0865 0.7965 0.964 0.000 0.000 0.000 0.036 0.000
#> DRR006481 6 0.6062 0.5617 0.000 0.144 0.116 0.124 0.000 0.616
#> DRR006482 4 0.7170 0.5114 0.080 0.020 0.092 0.576 0.056 0.176
#> DRR006483 1 0.0865 0.7965 0.964 0.000 0.000 0.000 0.036 0.000
#> DRR006484 3 0.4366 0.4253 0.000 0.004 0.540 0.016 0.000 0.440
#> DRR006485 2 0.1010 0.9388 0.000 0.960 0.036 0.000 0.000 0.004
#> DRR006486 5 0.2257 0.6950 0.116 0.000 0.000 0.000 0.876 0.008
#> DRR006487 3 0.3052 0.7498 0.000 0.000 0.780 0.000 0.004 0.216
#> DRR006488 2 0.0405 0.9577 0.000 0.988 0.004 0.008 0.000 0.000
#> DRR006489 1 0.1716 0.7747 0.932 0.000 0.000 0.036 0.028 0.004
#> DRR006490 3 0.3050 0.7632 0.000 0.000 0.764 0.000 0.000 0.236
#> DRR006491 3 0.2996 0.7657 0.000 0.000 0.772 0.000 0.000 0.228
#> DRR006492 4 0.8176 0.1270 0.268 0.008 0.100 0.328 0.040 0.256
#> DRR006493 3 0.3163 0.7623 0.000 0.004 0.764 0.000 0.000 0.232
#> DRR006494 1 0.0865 0.7965 0.964 0.000 0.000 0.000 0.036 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)
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 16187 rows and 121 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 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 0.595 0.816 0.911 0.4927 0.496 0.496
#> 3 3 0.510 0.676 0.782 0.3080 0.680 0.454
#> 4 4 0.920 0.945 0.960 0.1417 0.889 0.696
#> 5 5 0.788 0.708 0.845 0.0625 0.997 0.987
#> 6 6 0.768 0.637 0.745 0.0437 0.889 0.595
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
#> DRR006374 2 0.0000 0.890 0.000 1.000
#> DRR006375 1 0.0376 0.915 0.996 0.004
#> DRR006376 2 0.9944 0.309 0.456 0.544
#> DRR006377 2 0.6343 0.765 0.160 0.840
#> DRR006378 2 0.0938 0.893 0.012 0.988
#> DRR006379 2 0.7950 0.694 0.240 0.760
#> DRR006380 2 0.0000 0.890 0.000 1.000
#> DRR006381 1 0.0376 0.915 0.996 0.004
#> DRR006382 2 0.0376 0.888 0.004 0.996
#> DRR006383 1 0.8081 0.722 0.752 0.248
#> DRR006384 2 0.0000 0.890 0.000 1.000
#> DRR006385 1 0.0376 0.915 0.996 0.004
#> DRR006386 2 0.0938 0.893 0.012 0.988
#> DRR006387 1 0.0376 0.915 0.996 0.004
#> DRR006388 2 0.8813 0.578 0.300 0.700
#> DRR006389 2 0.8813 0.578 0.300 0.700
#> DRR006390 2 0.0938 0.893 0.012 0.988
#> DRR006391 2 0.0938 0.893 0.012 0.988
#> DRR006392 1 0.0376 0.915 0.996 0.004
#> DRR006393 1 0.0376 0.915 0.996 0.004
#> DRR006394 2 0.0938 0.893 0.012 0.988
#> DRR006395 1 0.1633 0.909 0.976 0.024
#> DRR006396 1 0.0376 0.915 0.996 0.004
#> DRR006397 2 0.9522 0.472 0.372 0.628
#> DRR006398 2 0.9522 0.472 0.372 0.628
#> DRR006399 2 0.9998 0.213 0.492 0.508
#> DRR006400 2 0.9998 0.213 0.492 0.508
#> DRR006401 2 0.0938 0.893 0.012 0.988
#> DRR006402 2 0.0938 0.893 0.012 0.988
#> DRR006403 2 0.9933 0.301 0.452 0.548
#> DRR006404 2 0.9850 0.371 0.428 0.572
#> DRR006405 1 0.9129 0.499 0.672 0.328
#> DRR006406 1 0.9170 0.489 0.668 0.332
#> DRR006407 2 0.0938 0.893 0.012 0.988
#> DRR006408 2 0.0938 0.893 0.012 0.988
#> DRR006409 1 0.0376 0.912 0.996 0.004
#> DRR006410 1 0.0376 0.915 0.996 0.004
#> DRR006411 2 0.2423 0.875 0.040 0.960
#> DRR006412 2 0.0938 0.893 0.012 0.988
#> DRR006413 1 0.0376 0.915 0.996 0.004
#> DRR006414 1 0.6048 0.834 0.852 0.148
#> DRR006415 1 0.7139 0.787 0.804 0.196
#> DRR006416 1 0.8499 0.665 0.724 0.276
#> DRR006417 1 0.4939 0.865 0.892 0.108
#> DRR006418 1 0.1184 0.912 0.984 0.016
#> DRR006419 1 0.4815 0.868 0.896 0.104
#> DRR006420 1 0.7139 0.787 0.804 0.196
#> DRR006421 2 0.0376 0.888 0.004 0.996
#> DRR006422 2 0.9954 0.276 0.460 0.540
#> DRR006423 2 0.0938 0.893 0.012 0.988
#> DRR006424 1 0.0376 0.915 0.996 0.004
#> DRR006425 2 0.0938 0.893 0.012 0.988
#> DRR006426 1 0.9754 0.364 0.592 0.408
#> DRR006427 2 0.0938 0.893 0.012 0.988
#> DRR006428 1 0.0938 0.908 0.988 0.012
#> DRR006429 2 0.0938 0.893 0.012 0.988
#> DRR006430 1 0.0376 0.915 0.996 0.004
#> DRR006431 1 0.0376 0.915 0.996 0.004
#> DRR006432 1 0.5737 0.840 0.864 0.136
#> DRR006433 2 0.0376 0.888 0.004 0.996
#> DRR006434 2 0.0000 0.890 0.000 1.000
#> DRR006435 2 0.0938 0.893 0.012 0.988
#> DRR006436 2 0.0938 0.893 0.012 0.988
#> DRR006437 1 0.0376 0.915 0.996 0.004
#> DRR006438 1 0.2603 0.902 0.956 0.044
#> DRR006439 1 0.2603 0.902 0.956 0.044
#> DRR006440 2 0.0376 0.888 0.004 0.996
#> DRR006441 2 0.0938 0.893 0.012 0.988
#> DRR006442 1 0.7139 0.787 0.804 0.196
#> DRR006443 2 0.0376 0.888 0.004 0.996
#> DRR006444 2 0.0938 0.893 0.012 0.988
#> DRR006445 1 0.0376 0.915 0.996 0.004
#> DRR006446 2 0.0938 0.893 0.012 0.988
#> DRR006447 1 0.0376 0.915 0.996 0.004
#> DRR006448 1 0.0376 0.915 0.996 0.004
#> DRR006449 1 0.0376 0.915 0.996 0.004
#> DRR006450 1 0.0376 0.915 0.996 0.004
#> DRR006451 2 0.6438 0.775 0.164 0.836
#> DRR006452 1 0.0376 0.915 0.996 0.004
#> DRR006453 1 0.1184 0.912 0.984 0.016
#> DRR006454 2 0.0938 0.893 0.012 0.988
#> DRR006455 2 0.0000 0.890 0.000 1.000
#> DRR006456 1 0.7139 0.787 0.804 0.196
#> DRR006457 2 0.9000 0.513 0.316 0.684
#> DRR006458 1 0.0376 0.915 0.996 0.004
#> DRR006459 1 0.0000 0.913 1.000 0.000
#> DRR006460 2 0.0000 0.890 0.000 1.000
#> DRR006461 2 0.0376 0.888 0.004 0.996
#> DRR006462 1 0.0376 0.915 0.996 0.004
#> DRR006463 2 0.0376 0.888 0.004 0.996
#> DRR006464 2 0.0938 0.893 0.012 0.988
#> DRR006465 1 0.0376 0.915 0.996 0.004
#> DRR006466 2 0.0376 0.888 0.004 0.996
#> DRR006467 1 0.0376 0.915 0.996 0.004
#> DRR006468 2 0.0938 0.893 0.012 0.988
#> DRR006469 2 0.0938 0.893 0.012 0.988
#> DRR006470 1 0.0938 0.908 0.988 0.012
#> DRR006471 1 0.0376 0.915 0.996 0.004
#> DRR006472 1 0.9754 0.388 0.592 0.408
#> DRR006473 2 0.0938 0.893 0.012 0.988
#> DRR006474 2 0.0938 0.893 0.012 0.988
#> DRR006475 1 0.0000 0.913 1.000 0.000
#> DRR006476 2 0.0000 0.890 0.000 1.000
#> DRR006477 2 0.0376 0.888 0.004 0.996
#> DRR006478 1 0.1184 0.912 0.984 0.016
#> DRR006479 1 0.2603 0.902 0.956 0.044
#> DRR006480 1 0.0000 0.913 1.000 0.000
#> DRR006481 1 0.9248 0.558 0.660 0.340
#> DRR006482 2 0.9850 0.357 0.428 0.572
#> DRR006483 1 0.0376 0.915 0.996 0.004
#> DRR006484 1 0.7139 0.787 0.804 0.196
#> DRR006485 2 0.0376 0.888 0.004 0.996
#> DRR006486 1 0.0938 0.908 0.988 0.012
#> DRR006487 1 0.5737 0.844 0.864 0.136
#> DRR006488 2 0.0938 0.893 0.012 0.988
#> DRR006489 1 0.0376 0.915 0.996 0.004
#> DRR006490 1 0.4161 0.880 0.916 0.084
#> DRR006491 1 0.5737 0.844 0.864 0.136
#> DRR006492 1 0.2043 0.906 0.968 0.032
#> DRR006493 1 0.8327 0.698 0.736 0.264
#> DRR006494 1 0.0000 0.913 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006375 1 0.5988 0.535 0.632 0.000 0.368
#> DRR006376 1 0.4346 0.505 0.816 0.184 0.000
#> DRR006377 1 0.6476 0.157 0.548 0.448 0.004
#> DRR006378 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006379 1 0.4974 0.455 0.764 0.236 0.000
#> DRR006380 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006381 1 0.6215 0.513 0.572 0.000 0.428
#> DRR006382 2 0.3551 0.837 0.000 0.868 0.132
#> DRR006383 3 0.6632 0.853 0.272 0.036 0.692
#> DRR006384 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006385 1 0.0237 0.585 0.996 0.000 0.004
#> DRR006386 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006387 1 0.0000 0.587 1.000 0.000 0.000
#> DRR006388 1 0.5873 0.355 0.684 0.312 0.004
#> DRR006389 1 0.5873 0.355 0.684 0.312 0.004
#> DRR006390 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006391 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006392 1 0.6215 0.513 0.572 0.000 0.428
#> DRR006393 1 0.2537 0.581 0.920 0.000 0.080
#> DRR006394 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006395 1 0.3038 0.485 0.896 0.000 0.104
#> DRR006396 1 0.5138 0.566 0.748 0.000 0.252
#> DRR006397 1 0.5244 0.445 0.756 0.240 0.004
#> DRR006398 1 0.5244 0.445 0.756 0.240 0.004
#> DRR006399 1 0.3412 0.543 0.876 0.124 0.000
#> DRR006400 1 0.3412 0.543 0.876 0.124 0.000
#> DRR006401 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006403 1 0.4121 0.518 0.832 0.168 0.000
#> DRR006404 1 0.4346 0.505 0.816 0.184 0.000
#> DRR006405 1 0.4235 0.513 0.824 0.176 0.000
#> DRR006406 1 0.4235 0.513 0.824 0.176 0.000
#> DRR006407 2 0.0747 0.942 0.016 0.984 0.000
#> DRR006408 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006409 1 0.5216 0.432 0.740 0.000 0.260
#> DRR006410 1 0.0000 0.587 1.000 0.000 0.000
#> DRR006411 2 0.5859 0.418 0.344 0.656 0.000
#> DRR006412 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006413 1 0.6308 0.467 0.508 0.000 0.492
#> DRR006414 3 0.6522 0.853 0.272 0.032 0.696
#> DRR006415 3 0.6632 0.853 0.272 0.036 0.692
#> DRR006416 1 0.4575 0.503 0.828 0.160 0.012
#> DRR006417 3 0.6113 0.843 0.300 0.012 0.688
#> DRR006418 1 0.1751 0.577 0.960 0.028 0.012
#> DRR006419 3 0.6497 0.820 0.336 0.016 0.648
#> DRR006420 3 0.7013 0.825 0.324 0.036 0.640
#> DRR006421 3 0.9497 0.634 0.332 0.200 0.468
#> DRR006422 1 0.4784 0.487 0.796 0.200 0.004
#> DRR006423 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006424 1 0.6168 0.520 0.588 0.000 0.412
#> DRR006425 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006426 1 0.9531 -0.367 0.468 0.208 0.324
#> DRR006427 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006428 3 0.5016 0.801 0.240 0.000 0.760
#> DRR006429 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006430 1 0.6215 0.513 0.572 0.000 0.428
#> DRR006431 1 0.6215 0.513 0.572 0.000 0.428
#> DRR006432 1 0.8457 -0.424 0.512 0.092 0.396
#> DRR006433 3 0.9895 0.526 0.332 0.272 0.396
#> DRR006434 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006435 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006437 1 0.1643 0.583 0.956 0.000 0.044
#> DRR006438 3 0.5986 0.845 0.284 0.012 0.704
#> DRR006439 3 0.6333 0.822 0.332 0.012 0.656
#> DRR006440 2 0.4002 0.807 0.000 0.840 0.160
#> DRR006441 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006442 3 0.6335 0.828 0.240 0.036 0.724
#> DRR006443 2 0.4002 0.807 0.000 0.840 0.160
#> DRR006444 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006445 1 0.0000 0.587 1.000 0.000 0.000
#> DRR006446 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006447 1 0.0237 0.585 0.996 0.000 0.004
#> DRR006448 1 0.0237 0.589 0.996 0.000 0.004
#> DRR006449 1 0.1031 0.588 0.976 0.000 0.024
#> DRR006450 1 0.1411 0.587 0.964 0.000 0.036
#> DRR006451 1 0.5926 0.290 0.644 0.356 0.000
#> DRR006452 1 0.5785 0.545 0.668 0.000 0.332
#> DRR006453 1 0.1163 0.584 0.972 0.028 0.000
#> DRR006454 2 0.2448 0.882 0.076 0.924 0.000
#> DRR006455 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006456 3 0.6632 0.853 0.272 0.036 0.692
#> DRR006457 3 0.7622 0.800 0.332 0.060 0.608
#> DRR006458 1 0.6308 0.467 0.508 0.000 0.492
#> DRR006459 1 0.6308 0.467 0.508 0.000 0.492
#> DRR006460 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006461 2 0.4002 0.807 0.000 0.840 0.160
#> DRR006462 1 0.2356 0.596 0.928 0.000 0.072
#> DRR006463 2 0.4002 0.807 0.000 0.840 0.160
#> DRR006464 2 0.2448 0.882 0.076 0.924 0.000
#> DRR006465 1 0.3619 0.589 0.864 0.000 0.136
#> DRR006466 2 0.0747 0.946 0.000 0.984 0.016
#> DRR006467 1 0.6308 0.467 0.508 0.000 0.492
#> DRR006468 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006469 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006470 3 0.5327 0.831 0.272 0.000 0.728
#> DRR006471 1 0.6308 0.467 0.508 0.000 0.492
#> DRR006472 3 0.7389 0.716 0.408 0.036 0.556
#> DRR006473 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006474 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006475 1 0.6308 0.467 0.508 0.000 0.492
#> DRR006476 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006477 3 0.9927 0.514 0.316 0.292 0.392
#> DRR006478 1 0.0892 0.586 0.980 0.020 0.000
#> DRR006479 3 0.5986 0.845 0.284 0.012 0.704
#> DRR006480 1 0.6308 0.467 0.508 0.000 0.492
#> DRR006481 3 0.7061 0.818 0.332 0.036 0.632
#> DRR006482 1 0.8310 -0.226 0.584 0.104 0.312
#> DRR006483 1 0.6308 0.467 0.508 0.000 0.492
#> DRR006484 3 0.6632 0.853 0.272 0.036 0.692
#> DRR006485 2 0.4002 0.807 0.000 0.840 0.160
#> DRR006486 3 0.6299 -0.460 0.476 0.000 0.524
#> DRR006487 3 0.6407 0.851 0.272 0.028 0.700
#> DRR006488 2 0.0000 0.957 0.000 1.000 0.000
#> DRR006489 1 0.6215 0.513 0.572 0.000 0.428
#> DRR006490 3 0.5858 0.821 0.240 0.020 0.740
#> DRR006491 3 0.6264 0.842 0.256 0.028 0.716
#> DRR006492 3 0.5706 0.751 0.320 0.000 0.680
#> DRR006493 3 0.6632 0.853 0.272 0.036 0.692
#> DRR006494 1 0.6308 0.467 0.508 0.000 0.492
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.0336 0.952 0.000 0.992 0.000 0.008
#> DRR006375 1 0.0779 0.983 0.980 0.000 0.004 0.016
#> DRR006376 4 0.1042 0.957 0.008 0.020 0.000 0.972
#> DRR006377 4 0.2345 0.893 0.000 0.100 0.000 0.900
#> DRR006378 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006379 4 0.1118 0.952 0.000 0.036 0.000 0.964
#> DRR006380 2 0.0657 0.948 0.000 0.984 0.004 0.012
#> DRR006381 1 0.0779 0.983 0.980 0.000 0.004 0.016
#> DRR006382 2 0.3443 0.846 0.000 0.848 0.136 0.016
#> DRR006383 3 0.0657 0.974 0.000 0.012 0.984 0.004
#> DRR006384 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> DRR006385 4 0.1305 0.952 0.036 0.000 0.004 0.960
#> DRR006386 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006387 4 0.1022 0.954 0.032 0.000 0.000 0.968
#> DRR006388 4 0.1118 0.952 0.000 0.036 0.000 0.964
#> DRR006389 4 0.1118 0.952 0.000 0.036 0.000 0.964
#> DRR006390 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006391 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006392 1 0.0592 0.984 0.984 0.000 0.000 0.016
#> DRR006393 4 0.1022 0.954 0.032 0.000 0.000 0.968
#> DRR006394 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006395 4 0.1394 0.948 0.012 0.008 0.016 0.964
#> DRR006396 4 0.3052 0.870 0.136 0.000 0.004 0.860
#> DRR006397 4 0.1118 0.952 0.000 0.036 0.000 0.964
#> DRR006398 4 0.1118 0.952 0.000 0.036 0.000 0.964
#> DRR006399 4 0.1443 0.955 0.028 0.008 0.004 0.960
#> DRR006400 4 0.1443 0.955 0.028 0.008 0.004 0.960
#> DRR006401 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006402 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006403 4 0.0779 0.957 0.004 0.016 0.000 0.980
#> DRR006404 4 0.1004 0.956 0.004 0.024 0.000 0.972
#> DRR006405 4 0.1118 0.952 0.000 0.036 0.000 0.964
#> DRR006406 4 0.1118 0.952 0.000 0.036 0.000 0.964
#> DRR006407 2 0.2469 0.872 0.000 0.892 0.000 0.108
#> DRR006408 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006409 3 0.4136 0.727 0.196 0.000 0.788 0.016
#> DRR006410 4 0.1118 0.953 0.036 0.000 0.000 0.964
#> DRR006411 4 0.0921 0.952 0.000 0.028 0.000 0.972
#> DRR006412 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006413 1 0.0657 0.983 0.984 0.000 0.004 0.012
#> DRR006414 3 0.0376 0.976 0.004 0.004 0.992 0.000
#> DRR006415 3 0.0376 0.976 0.004 0.004 0.992 0.000
#> DRR006416 4 0.1256 0.955 0.008 0.028 0.000 0.964
#> DRR006417 3 0.1247 0.968 0.016 0.004 0.968 0.012
#> DRR006418 4 0.0779 0.957 0.016 0.004 0.000 0.980
#> DRR006419 3 0.1124 0.970 0.012 0.004 0.972 0.012
#> DRR006420 3 0.0804 0.973 0.000 0.012 0.980 0.008
#> DRR006421 3 0.1059 0.969 0.000 0.012 0.972 0.016
#> DRR006422 4 0.1004 0.956 0.004 0.024 0.000 0.972
#> DRR006423 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006424 1 0.0779 0.983 0.980 0.000 0.004 0.016
#> DRR006425 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006426 4 0.2255 0.915 0.000 0.068 0.012 0.920
#> DRR006427 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006428 3 0.1042 0.968 0.020 0.000 0.972 0.008
#> DRR006429 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006430 1 0.0592 0.984 0.984 0.000 0.000 0.016
#> DRR006431 1 0.0592 0.984 0.984 0.000 0.000 0.016
#> DRR006432 4 0.0992 0.949 0.012 0.004 0.008 0.976
#> DRR006433 3 0.1059 0.969 0.000 0.012 0.972 0.016
#> DRR006434 2 0.0336 0.952 0.000 0.992 0.000 0.008
#> DRR006435 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006436 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006437 4 0.3052 0.864 0.136 0.000 0.004 0.860
#> DRR006438 3 0.1124 0.970 0.012 0.004 0.972 0.012
#> DRR006439 3 0.0564 0.976 0.004 0.004 0.988 0.004
#> DRR006440 2 0.4139 0.797 0.000 0.800 0.176 0.024
#> DRR006441 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006442 3 0.0188 0.976 0.000 0.004 0.996 0.000
#> DRR006443 2 0.4012 0.795 0.000 0.800 0.184 0.016
#> DRR006444 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006445 4 0.1118 0.953 0.036 0.000 0.000 0.964
#> DRR006446 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006447 4 0.1109 0.953 0.028 0.000 0.004 0.968
#> DRR006448 4 0.1305 0.952 0.036 0.000 0.004 0.960
#> DRR006449 4 0.3157 0.861 0.144 0.000 0.004 0.852
#> DRR006450 4 0.3052 0.864 0.136 0.000 0.004 0.860
#> DRR006451 4 0.1118 0.952 0.000 0.036 0.000 0.964
#> DRR006452 1 0.3791 0.750 0.796 0.000 0.004 0.200
#> DRR006453 4 0.1004 0.956 0.024 0.004 0.000 0.972
#> DRR006454 2 0.0469 0.957 0.000 0.988 0.000 0.012
#> DRR006455 2 0.0592 0.956 0.000 0.984 0.000 0.016
#> DRR006456 3 0.0188 0.976 0.000 0.004 0.996 0.000
#> DRR006457 3 0.0804 0.973 0.000 0.012 0.980 0.008
#> DRR006458 1 0.0592 0.984 0.984 0.000 0.000 0.016
#> DRR006459 1 0.0592 0.984 0.984 0.000 0.000 0.016
#> DRR006460 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> DRR006461 2 0.4012 0.795 0.000 0.800 0.184 0.016
#> DRR006462 4 0.3052 0.870 0.136 0.000 0.004 0.860
#> DRR006463 2 0.4012 0.795 0.000 0.800 0.184 0.016
#> DRR006464 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006465 4 0.1118 0.953 0.036 0.000 0.000 0.964
#> DRR006466 2 0.3224 0.861 0.000 0.864 0.120 0.016
#> DRR006467 1 0.0469 0.983 0.988 0.000 0.000 0.012
#> DRR006468 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006469 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006470 3 0.2542 0.901 0.084 0.000 0.904 0.012
#> DRR006471 1 0.0469 0.983 0.988 0.000 0.000 0.012
#> DRR006472 3 0.0804 0.973 0.000 0.012 0.980 0.008
#> DRR006473 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006474 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006475 1 0.0469 0.983 0.988 0.000 0.000 0.012
#> DRR006476 2 0.0779 0.946 0.000 0.980 0.004 0.016
#> DRR006477 3 0.1059 0.969 0.000 0.012 0.972 0.016
#> DRR006478 4 0.0921 0.956 0.028 0.000 0.000 0.972
#> DRR006479 3 0.1124 0.970 0.012 0.004 0.972 0.012
#> DRR006480 1 0.0469 0.983 0.988 0.000 0.000 0.012
#> DRR006481 3 0.0524 0.975 0.000 0.004 0.988 0.008
#> DRR006482 4 0.0927 0.944 0.000 0.008 0.016 0.976
#> DRR006483 1 0.0469 0.983 0.988 0.000 0.000 0.012
#> DRR006484 3 0.0188 0.976 0.000 0.004 0.996 0.000
#> DRR006485 2 0.4012 0.795 0.000 0.800 0.184 0.016
#> DRR006486 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> DRR006487 3 0.0524 0.975 0.008 0.004 0.988 0.000
#> DRR006488 2 0.0336 0.960 0.000 0.992 0.000 0.008
#> DRR006489 1 0.0779 0.983 0.980 0.000 0.004 0.016
#> DRR006490 3 0.0657 0.974 0.012 0.004 0.984 0.000
#> DRR006491 3 0.0376 0.976 0.004 0.004 0.992 0.000
#> DRR006492 3 0.0804 0.972 0.000 0.008 0.980 0.012
#> DRR006493 3 0.0188 0.976 0.000 0.004 0.996 0.000
#> DRR006494 1 0.0592 0.984 0.984 0.000 0.000 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006375 1 0.1197 0.9158 0.952 0.000 0.000 0.000 0.048
#> DRR006376 4 0.1410 0.7756 0.000 0.000 0.000 0.940 0.060
#> DRR006377 4 0.4152 0.6773 0.000 0.060 0.000 0.772 0.168
#> DRR006378 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006379 4 0.1608 0.7747 0.000 0.000 0.000 0.928 0.072
#> DRR006380 2 0.3452 0.6941 0.000 0.756 0.000 0.000 0.244
#> DRR006381 1 0.3727 0.7436 0.768 0.000 0.000 0.016 0.216
#> DRR006382 2 0.5875 0.4861 0.000 0.592 0.152 0.000 0.256
#> DRR006383 3 0.0703 0.7532 0.000 0.000 0.976 0.000 0.024
#> DRR006384 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006385 4 0.4218 0.7200 0.008 0.000 0.000 0.660 0.332
#> DRR006386 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006387 4 0.2377 0.7900 0.000 0.000 0.000 0.872 0.128
#> DRR006388 4 0.2516 0.7731 0.000 0.000 0.000 0.860 0.140
#> DRR006389 4 0.2516 0.7731 0.000 0.000 0.000 0.860 0.140
#> DRR006390 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006391 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006392 1 0.0162 0.9300 0.996 0.000 0.000 0.000 0.004
#> DRR006393 4 0.3551 0.7858 0.008 0.000 0.000 0.772 0.220
#> DRR006394 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006395 4 0.1270 0.7821 0.000 0.000 0.000 0.948 0.052
#> DRR006396 4 0.5028 0.6936 0.072 0.000 0.000 0.668 0.260
#> DRR006397 4 0.1851 0.7918 0.000 0.000 0.000 0.912 0.088
#> DRR006398 4 0.1851 0.7918 0.000 0.000 0.000 0.912 0.088
#> DRR006399 4 0.2825 0.7630 0.016 0.000 0.000 0.860 0.124
#> DRR006400 4 0.2825 0.7630 0.016 0.000 0.000 0.860 0.124
#> DRR006401 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006403 4 0.1478 0.7747 0.000 0.000 0.000 0.936 0.064
#> DRR006404 4 0.1341 0.7760 0.000 0.000 0.000 0.944 0.056
#> DRR006405 4 0.2179 0.7700 0.000 0.000 0.000 0.888 0.112
#> DRR006406 4 0.2179 0.7700 0.000 0.000 0.000 0.888 0.112
#> DRR006407 2 0.6816 -0.0768 0.000 0.360 0.000 0.320 0.320
#> DRR006408 2 0.0963 0.8363 0.000 0.964 0.000 0.000 0.036
#> DRR006409 3 0.4168 0.4911 0.200 0.000 0.756 0.000 0.044
#> DRR006410 4 0.2424 0.7897 0.000 0.000 0.000 0.868 0.132
#> DRR006411 4 0.4074 0.5823 0.000 0.000 0.000 0.636 0.364
#> DRR006412 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006413 1 0.2280 0.8868 0.880 0.000 0.000 0.000 0.120
#> DRR006414 3 0.0000 0.7677 0.000 0.000 1.000 0.000 0.000
#> DRR006415 3 0.0000 0.7677 0.000 0.000 1.000 0.000 0.000
#> DRR006416 4 0.3452 0.7816 0.000 0.000 0.000 0.756 0.244
#> DRR006417 3 0.3752 0.5412 0.000 0.000 0.708 0.000 0.292
#> DRR006418 4 0.4210 0.6661 0.000 0.000 0.000 0.588 0.412
#> DRR006419 3 0.3730 0.5292 0.000 0.000 0.712 0.000 0.288
#> DRR006420 3 0.2561 0.6985 0.000 0.000 0.856 0.000 0.144
#> DRR006421 3 0.5396 -0.4001 0.000 0.000 0.588 0.072 0.340
#> DRR006422 4 0.2516 0.7879 0.000 0.000 0.000 0.860 0.140
#> DRR006423 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006424 1 0.1197 0.9158 0.952 0.000 0.000 0.000 0.048
#> DRR006425 2 0.0794 0.8400 0.000 0.972 0.000 0.000 0.028
#> DRR006426 4 0.5088 0.5140 0.000 0.032 0.008 0.608 0.352
#> DRR006427 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006428 3 0.2020 0.7178 0.000 0.000 0.900 0.000 0.100
#> DRR006429 2 0.5187 0.5865 0.000 0.656 0.000 0.084 0.260
#> DRR006430 1 0.0290 0.9296 0.992 0.000 0.000 0.000 0.008
#> DRR006431 1 0.0609 0.9300 0.980 0.000 0.000 0.000 0.020
#> DRR006432 4 0.4489 0.4907 0.000 0.000 0.008 0.572 0.420
#> DRR006433 3 0.5546 -0.4491 0.000 0.000 0.576 0.084 0.340
#> DRR006434 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006435 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006437 4 0.5467 0.6050 0.064 0.000 0.000 0.524 0.412
#> DRR006438 3 0.2773 0.6986 0.000 0.000 0.836 0.000 0.164
#> DRR006439 3 0.1851 0.7477 0.000 0.000 0.912 0.000 0.088
#> DRR006440 2 0.5940 0.4509 0.000 0.568 0.140 0.000 0.292
#> DRR006441 2 0.0880 0.8381 0.000 0.968 0.000 0.000 0.032
#> DRR006442 3 0.0000 0.7677 0.000 0.000 1.000 0.000 0.000
#> DRR006443 2 0.6063 0.4393 0.000 0.568 0.176 0.000 0.256
#> DRR006444 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006445 4 0.3242 0.7876 0.000 0.000 0.000 0.784 0.216
#> DRR006446 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006447 4 0.4126 0.7216 0.000 0.000 0.000 0.620 0.380
#> DRR006448 4 0.4014 0.7397 0.016 0.000 0.000 0.728 0.256
#> DRR006449 4 0.5393 0.6734 0.080 0.000 0.000 0.608 0.312
#> DRR006450 4 0.5605 0.6174 0.076 0.000 0.000 0.520 0.404
#> DRR006451 4 0.1478 0.7756 0.000 0.000 0.000 0.936 0.064
#> DRR006452 1 0.6494 0.2274 0.492 0.000 0.000 0.252 0.256
#> DRR006453 4 0.3242 0.7870 0.000 0.000 0.000 0.784 0.216
#> DRR006454 2 0.5287 0.5761 0.000 0.648 0.000 0.092 0.260
#> DRR006455 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006456 3 0.0000 0.7677 0.000 0.000 1.000 0.000 0.000
#> DRR006457 3 0.2852 0.5915 0.000 0.000 0.828 0.000 0.172
#> DRR006458 1 0.0404 0.9295 0.988 0.000 0.000 0.000 0.012
#> DRR006459 1 0.0510 0.9289 0.984 0.000 0.000 0.000 0.016
#> DRR006460 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006461 2 0.6083 0.4325 0.000 0.564 0.176 0.000 0.260
#> DRR006462 4 0.4907 0.6944 0.056 0.000 0.000 0.664 0.280
#> DRR006463 2 0.6063 0.4393 0.000 0.568 0.176 0.000 0.256
#> DRR006464 2 0.5551 0.5157 0.000 0.612 0.000 0.104 0.284
#> DRR006465 4 0.3487 0.7869 0.008 0.000 0.000 0.780 0.212
#> DRR006466 2 0.7158 0.3042 0.000 0.500 0.160 0.052 0.288
#> DRR006467 1 0.1043 0.9176 0.960 0.000 0.000 0.000 0.040
#> DRR006468 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006469 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006470 3 0.4218 0.4552 0.008 0.000 0.660 0.000 0.332
#> DRR006471 1 0.1043 0.9176 0.960 0.000 0.000 0.000 0.040
#> DRR006472 5 0.6121 0.0000 0.000 0.000 0.380 0.132 0.488
#> DRR006473 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006474 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006475 1 0.0404 0.9299 0.988 0.000 0.000 0.000 0.012
#> DRR006476 2 0.5355 0.5501 0.000 0.624 0.000 0.084 0.292
#> DRR006477 3 0.5179 -0.3299 0.000 0.000 0.640 0.072 0.288
#> DRR006478 4 0.3242 0.7870 0.000 0.000 0.000 0.784 0.216
#> DRR006479 3 0.2773 0.6986 0.000 0.000 0.836 0.000 0.164
#> DRR006480 1 0.0510 0.9295 0.984 0.000 0.000 0.000 0.016
#> DRR006481 3 0.1965 0.7346 0.000 0.000 0.904 0.000 0.096
#> DRR006482 4 0.3895 0.6800 0.000 0.000 0.000 0.680 0.320
#> DRR006483 1 0.0162 0.9299 0.996 0.000 0.000 0.000 0.004
#> DRR006484 3 0.0510 0.7663 0.000 0.000 0.984 0.000 0.016
#> DRR006485 2 0.6063 0.4393 0.000 0.568 0.176 0.000 0.256
#> DRR006486 1 0.1197 0.9170 0.952 0.000 0.000 0.000 0.048
#> DRR006487 3 0.0162 0.7675 0.000 0.000 0.996 0.000 0.004
#> DRR006488 2 0.0000 0.8519 0.000 1.000 0.000 0.000 0.000
#> DRR006489 1 0.0880 0.9227 0.968 0.000 0.000 0.000 0.032
#> DRR006490 3 0.0880 0.7600 0.000 0.000 0.968 0.000 0.032
#> DRR006491 3 0.0000 0.7677 0.000 0.000 1.000 0.000 0.000
#> DRR006492 3 0.1410 0.7501 0.000 0.000 0.940 0.000 0.060
#> DRR006493 3 0.0000 0.7677 0.000 0.000 1.000 0.000 0.000
#> DRR006494 1 0.0510 0.9289 0.984 0.000 0.000 0.000 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.0458 0.9672 0.000 0.984 0.000 0.000 0.016 0.000
#> DRR006375 1 0.2053 0.8759 0.888 0.000 0.000 0.000 0.004 0.108
#> DRR006376 4 0.1320 0.5021 0.000 0.000 0.000 0.948 0.016 0.036
#> DRR006377 4 0.5184 0.3973 0.000 0.024 0.000 0.672 0.144 0.160
#> DRR006378 2 0.0993 0.9543 0.000 0.964 0.000 0.000 0.012 0.024
#> DRR006379 4 0.0972 0.4793 0.000 0.000 0.000 0.964 0.008 0.028
#> DRR006380 5 0.3995 0.4552 0.000 0.480 0.000 0.000 0.516 0.004
#> DRR006381 1 0.3966 0.3281 0.552 0.000 0.000 0.000 0.004 0.444
#> DRR006382 5 0.5650 0.6956 0.000 0.332 0.148 0.000 0.516 0.004
#> DRR006383 3 0.1411 0.7781 0.000 0.000 0.936 0.000 0.060 0.004
#> DRR006384 2 0.0146 0.9721 0.000 0.996 0.000 0.000 0.000 0.004
#> DRR006385 6 0.3890 0.4060 0.004 0.000 0.000 0.400 0.000 0.596
#> DRR006386 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006387 4 0.3445 0.2540 0.000 0.000 0.000 0.732 0.008 0.260
#> DRR006388 4 0.4871 0.4157 0.000 0.000 0.000 0.644 0.112 0.244
#> DRR006389 4 0.4871 0.4157 0.000 0.000 0.000 0.644 0.112 0.244
#> DRR006390 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006391 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006392 1 0.0260 0.9230 0.992 0.000 0.000 0.000 0.000 0.008
#> DRR006393 4 0.5284 0.2455 0.020 0.000 0.000 0.516 0.056 0.408
#> DRR006394 2 0.1088 0.9514 0.000 0.960 0.000 0.000 0.016 0.024
#> DRR006395 4 0.1700 0.4969 0.000 0.000 0.000 0.928 0.048 0.024
#> DRR006396 6 0.4722 0.3305 0.036 0.000 0.000 0.468 0.004 0.492
#> DRR006397 4 0.4311 0.4217 0.000 0.000 0.000 0.716 0.088 0.196
#> DRR006398 4 0.4311 0.4217 0.000 0.000 0.000 0.716 0.088 0.196
#> DRR006399 4 0.2488 0.3669 0.008 0.000 0.000 0.864 0.004 0.124
#> DRR006400 4 0.2488 0.3669 0.008 0.000 0.000 0.864 0.004 0.124
#> DRR006401 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006403 4 0.0405 0.4928 0.000 0.000 0.000 0.988 0.008 0.004
#> DRR006404 4 0.1074 0.5019 0.000 0.000 0.000 0.960 0.012 0.028
#> DRR006405 4 0.3426 0.4880 0.000 0.000 0.000 0.808 0.068 0.124
#> DRR006406 4 0.3426 0.4880 0.000 0.000 0.000 0.808 0.068 0.124
#> DRR006407 4 0.6631 0.0848 0.000 0.080 0.000 0.424 0.376 0.120
#> DRR006408 2 0.2558 0.8149 0.000 0.868 0.000 0.000 0.104 0.028
#> DRR006409 3 0.5358 0.5900 0.268 0.000 0.628 0.004 0.068 0.032
#> DRR006410 4 0.3534 0.2525 0.000 0.000 0.000 0.716 0.008 0.276
#> DRR006411 4 0.6191 0.0827 0.000 0.008 0.000 0.424 0.236 0.332
#> DRR006412 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006413 1 0.3907 0.8004 0.756 0.000 0.000 0.000 0.068 0.176
#> DRR006414 3 0.0146 0.7997 0.000 0.000 0.996 0.000 0.000 0.004
#> DRR006415 3 0.0146 0.7997 0.000 0.000 0.996 0.000 0.000 0.004
#> DRR006416 4 0.5313 0.2506 0.000 0.000 0.000 0.484 0.104 0.412
#> DRR006417 3 0.5796 0.6361 0.000 0.000 0.500 0.000 0.268 0.232
#> DRR006418 6 0.5440 0.0335 0.000 0.000 0.000 0.296 0.152 0.552
#> DRR006419 3 0.5911 0.5960 0.000 0.000 0.468 0.000 0.252 0.280
#> DRR006420 3 0.5351 0.6522 0.000 0.000 0.588 0.000 0.236 0.176
#> DRR006421 5 0.3684 0.2953 0.000 0.000 0.300 0.004 0.692 0.004
#> DRR006422 4 0.5230 0.3260 0.000 0.000 0.000 0.548 0.108 0.344
#> DRR006423 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006424 1 0.1753 0.8920 0.912 0.000 0.000 0.000 0.004 0.084
#> DRR006425 2 0.1700 0.9126 0.000 0.928 0.000 0.000 0.048 0.024
#> DRR006426 6 0.6398 -0.0887 0.000 0.000 0.012 0.324 0.296 0.368
#> DRR006427 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006428 3 0.2790 0.7615 0.000 0.000 0.844 0.000 0.132 0.024
#> DRR006429 5 0.5404 0.4139 0.000 0.460 0.000 0.028 0.460 0.052
#> DRR006430 1 0.0363 0.9230 0.988 0.000 0.000 0.000 0.000 0.012
#> DRR006431 1 0.0508 0.9229 0.984 0.000 0.000 0.000 0.004 0.012
#> DRR006432 6 0.6169 0.0384 0.000 0.000 0.012 0.240 0.276 0.472
#> DRR006433 5 0.4122 0.2906 0.000 0.000 0.316 0.020 0.660 0.004
#> DRR006434 2 0.0458 0.9672 0.000 0.984 0.000 0.000 0.016 0.000
#> DRR006435 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006437 6 0.4391 0.4684 0.020 0.000 0.000 0.312 0.016 0.652
#> DRR006438 3 0.5022 0.7273 0.000 0.000 0.640 0.000 0.204 0.156
#> DRR006439 3 0.3458 0.7806 0.000 0.000 0.808 0.000 0.112 0.080
#> DRR006440 5 0.5377 0.6872 0.000 0.336 0.112 0.000 0.548 0.004
#> DRR006441 2 0.1765 0.9068 0.000 0.924 0.000 0.000 0.052 0.024
#> DRR006442 3 0.0000 0.7997 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006443 5 0.5529 0.6942 0.000 0.336 0.148 0.000 0.516 0.000
#> DRR006444 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006445 4 0.4591 0.1834 0.000 0.000 0.000 0.500 0.036 0.464
#> DRR006446 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006447 6 0.3819 0.3585 0.000 0.000 0.000 0.280 0.020 0.700
#> DRR006448 4 0.3536 0.1477 0.008 0.000 0.000 0.736 0.004 0.252
#> DRR006449 6 0.4860 0.4332 0.040 0.000 0.000 0.380 0.012 0.568
#> DRR006450 6 0.4354 0.4704 0.028 0.000 0.000 0.272 0.016 0.684
#> DRR006451 4 0.0806 0.4898 0.000 0.000 0.000 0.972 0.008 0.020
#> DRR006452 6 0.5766 0.3184 0.292 0.000 0.000 0.184 0.004 0.520
#> DRR006453 4 0.4837 0.2410 0.000 0.000 0.000 0.512 0.056 0.432
#> DRR006454 5 0.5657 0.4731 0.000 0.416 0.000 0.060 0.484 0.040
#> DRR006455 2 0.0146 0.9714 0.000 0.996 0.000 0.000 0.000 0.004
#> DRR006456 3 0.0146 0.7992 0.000 0.000 0.996 0.000 0.004 0.000
#> DRR006457 3 0.4234 0.5582 0.000 0.000 0.644 0.000 0.324 0.032
#> DRR006458 1 0.0405 0.9229 0.988 0.000 0.000 0.000 0.004 0.008
#> DRR006459 1 0.0508 0.9226 0.984 0.000 0.000 0.000 0.004 0.012
#> DRR006460 2 0.0405 0.9705 0.000 0.988 0.000 0.000 0.008 0.004
#> DRR006461 5 0.5421 0.6965 0.000 0.328 0.136 0.000 0.536 0.000
#> DRR006462 6 0.4453 0.3628 0.020 0.000 0.000 0.452 0.004 0.524
#> DRR006463 5 0.5529 0.6942 0.000 0.336 0.148 0.000 0.516 0.000
#> DRR006464 5 0.6827 0.3797 0.000 0.376 0.000 0.080 0.392 0.152
#> DRR006465 4 0.5284 0.2455 0.020 0.000 0.000 0.516 0.056 0.408
#> DRR006466 5 0.5173 0.7020 0.000 0.276 0.128 0.000 0.596 0.000
#> DRR006467 1 0.2308 0.8841 0.892 0.000 0.000 0.000 0.068 0.040
#> DRR006468 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006469 2 0.1176 0.9475 0.000 0.956 0.000 0.000 0.020 0.024
#> DRR006470 3 0.6288 0.5217 0.008 0.000 0.396 0.000 0.288 0.308
#> DRR006471 1 0.2308 0.8841 0.892 0.000 0.000 0.000 0.068 0.040
#> DRR006472 5 0.6643 0.1039 0.000 0.000 0.172 0.072 0.496 0.260
#> DRR006473 2 0.1088 0.9514 0.000 0.960 0.000 0.000 0.016 0.024
#> DRR006474 2 0.0458 0.9672 0.000 0.984 0.000 0.000 0.016 0.000
#> DRR006475 1 0.0000 0.9222 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006476 5 0.4710 0.6042 0.000 0.360 0.000 0.020 0.596 0.024
#> DRR006477 5 0.3986 0.2489 0.000 0.000 0.384 0.004 0.608 0.004
#> DRR006478 4 0.4837 0.2490 0.000 0.000 0.000 0.512 0.056 0.432
#> DRR006479 3 0.5022 0.7273 0.000 0.000 0.640 0.000 0.204 0.156
#> DRR006480 1 0.0146 0.9222 0.996 0.000 0.000 0.000 0.004 0.000
#> DRR006481 3 0.4344 0.7308 0.000 0.000 0.716 0.000 0.188 0.096
#> DRR006482 4 0.6044 -0.1541 0.000 0.000 0.000 0.396 0.256 0.348
#> DRR006483 1 0.0291 0.9210 0.992 0.000 0.000 0.000 0.004 0.004
#> DRR006484 3 0.1349 0.8000 0.000 0.000 0.940 0.000 0.056 0.004
#> DRR006485 5 0.5529 0.6942 0.000 0.336 0.148 0.000 0.516 0.000
#> DRR006486 1 0.2365 0.8823 0.888 0.000 0.000 0.000 0.072 0.040
#> DRR006487 3 0.0777 0.7988 0.000 0.000 0.972 0.000 0.024 0.004
#> DRR006488 2 0.0000 0.9740 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006489 1 0.1411 0.9053 0.936 0.000 0.000 0.000 0.004 0.060
#> DRR006490 3 0.1584 0.7907 0.000 0.000 0.928 0.000 0.064 0.008
#> DRR006491 3 0.0146 0.7997 0.000 0.000 0.996 0.000 0.004 0.000
#> DRR006492 3 0.3493 0.7655 0.000 0.000 0.796 0.000 0.148 0.056
#> DRR006493 3 0.0146 0.7992 0.000 0.000 0.996 0.000 0.004 0.000
#> DRR006494 1 0.0508 0.9226 0.984 0.000 0.000 0.000 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)
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 16187 rows and 121 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 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 0.660 0.866 0.938 0.5044 0.496 0.496
#> 3 3 0.827 0.886 0.951 0.2950 0.725 0.509
#> 4 4 0.841 0.884 0.945 0.1244 0.905 0.736
#> 5 5 0.794 0.751 0.881 0.0496 0.954 0.836
#> 6 6 0.795 0.666 0.802 0.0436 0.916 0.671
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
#> DRR006374 2 0.0000 0.932 0.000 1.000
#> DRR006375 1 0.0000 0.929 1.000 0.000
#> DRR006376 2 0.9686 0.441 0.396 0.604
#> DRR006377 2 0.0000 0.932 0.000 1.000
#> DRR006378 2 0.0000 0.932 0.000 1.000
#> DRR006379 2 0.7219 0.763 0.200 0.800
#> DRR006380 2 0.0000 0.932 0.000 1.000
#> DRR006381 1 0.0000 0.929 1.000 0.000
#> DRR006382 2 0.0000 0.932 0.000 1.000
#> DRR006383 1 0.9686 0.446 0.604 0.396
#> DRR006384 2 0.0000 0.932 0.000 1.000
#> DRR006385 1 0.0000 0.929 1.000 0.000
#> DRR006386 2 0.0000 0.932 0.000 1.000
#> DRR006387 1 0.0000 0.929 1.000 0.000
#> DRR006388 2 0.4298 0.869 0.088 0.912
#> DRR006389 2 0.4298 0.869 0.088 0.912
#> DRR006390 2 0.0000 0.932 0.000 1.000
#> DRR006391 2 0.0000 0.932 0.000 1.000
#> DRR006392 1 0.0000 0.929 1.000 0.000
#> DRR006393 1 0.0000 0.929 1.000 0.000
#> DRR006394 2 0.0000 0.932 0.000 1.000
#> DRR006395 1 0.0000 0.929 1.000 0.000
#> DRR006396 1 0.0000 0.929 1.000 0.000
#> DRR006397 2 0.7219 0.763 0.200 0.800
#> DRR006398 2 0.7219 0.763 0.200 0.800
#> DRR006399 2 0.9686 0.441 0.396 0.604
#> DRR006400 2 0.9686 0.441 0.396 0.604
#> DRR006401 2 0.0000 0.932 0.000 1.000
#> DRR006402 2 0.0000 0.932 0.000 1.000
#> DRR006403 2 0.9686 0.441 0.396 0.604
#> DRR006404 2 0.8909 0.613 0.308 0.692
#> DRR006405 1 0.4161 0.857 0.916 0.084
#> DRR006406 1 0.7453 0.685 0.788 0.212
#> DRR006407 2 0.0000 0.932 0.000 1.000
#> DRR006408 2 0.0000 0.932 0.000 1.000
#> DRR006409 1 0.0000 0.929 1.000 0.000
#> DRR006410 1 0.0000 0.929 1.000 0.000
#> DRR006411 2 0.5294 0.842 0.120 0.880
#> DRR006412 2 0.0000 0.932 0.000 1.000
#> DRR006413 1 0.0000 0.929 1.000 0.000
#> DRR006414 1 0.6247 0.811 0.844 0.156
#> DRR006415 1 0.7219 0.765 0.800 0.200
#> DRR006416 1 0.5629 0.832 0.868 0.132
#> DRR006417 1 0.1184 0.919 0.984 0.016
#> DRR006418 1 0.0000 0.929 1.000 0.000
#> DRR006419 1 0.0000 0.929 1.000 0.000
#> DRR006420 1 0.7219 0.765 0.800 0.200
#> DRR006421 2 0.0000 0.932 0.000 1.000
#> DRR006422 2 0.8713 0.639 0.292 0.708
#> DRR006423 2 0.0000 0.932 0.000 1.000
#> DRR006424 1 0.0000 0.929 1.000 0.000
#> DRR006425 2 0.0000 0.932 0.000 1.000
#> DRR006426 1 0.9732 0.428 0.596 0.404
#> DRR006427 2 0.0000 0.932 0.000 1.000
#> DRR006428 1 0.0000 0.929 1.000 0.000
#> DRR006429 2 0.0000 0.932 0.000 1.000
#> DRR006430 1 0.0000 0.929 1.000 0.000
#> DRR006431 1 0.0000 0.929 1.000 0.000
#> DRR006432 1 0.0938 0.922 0.988 0.012
#> DRR006433 2 0.0000 0.932 0.000 1.000
#> DRR006434 2 0.0000 0.932 0.000 1.000
#> DRR006435 2 0.0000 0.932 0.000 1.000
#> DRR006436 2 0.0000 0.932 0.000 1.000
#> DRR006437 1 0.0000 0.929 1.000 0.000
#> DRR006438 1 0.0000 0.929 1.000 0.000
#> DRR006439 1 0.0000 0.929 1.000 0.000
#> DRR006440 2 0.0000 0.932 0.000 1.000
#> DRR006441 2 0.0000 0.932 0.000 1.000
#> DRR006442 1 0.7219 0.765 0.800 0.200
#> DRR006443 2 0.0000 0.932 0.000 1.000
#> DRR006444 2 0.0000 0.932 0.000 1.000
#> DRR006445 1 0.0000 0.929 1.000 0.000
#> DRR006446 2 0.0000 0.932 0.000 1.000
#> DRR006447 1 0.0000 0.929 1.000 0.000
#> DRR006448 1 0.0000 0.929 1.000 0.000
#> DRR006449 1 0.0000 0.929 1.000 0.000
#> DRR006450 1 0.0000 0.929 1.000 0.000
#> DRR006451 2 0.7219 0.763 0.200 0.800
#> DRR006452 1 0.0000 0.929 1.000 0.000
#> DRR006453 1 0.0000 0.929 1.000 0.000
#> DRR006454 2 0.0000 0.932 0.000 1.000
#> DRR006455 2 0.0000 0.932 0.000 1.000
#> DRR006456 1 0.8327 0.683 0.736 0.264
#> DRR006457 2 0.4939 0.830 0.108 0.892
#> DRR006458 1 0.0000 0.929 1.000 0.000
#> DRR006459 1 0.0000 0.929 1.000 0.000
#> DRR006460 2 0.0000 0.932 0.000 1.000
#> DRR006461 2 0.0000 0.932 0.000 1.000
#> DRR006462 1 0.0000 0.929 1.000 0.000
#> DRR006463 2 0.0000 0.932 0.000 1.000
#> DRR006464 2 0.0000 0.932 0.000 1.000
#> DRR006465 1 0.0000 0.929 1.000 0.000
#> DRR006466 2 0.0000 0.932 0.000 1.000
#> DRR006467 1 0.0000 0.929 1.000 0.000
#> DRR006468 2 0.0000 0.932 0.000 1.000
#> DRR006469 2 0.0000 0.932 0.000 1.000
#> DRR006470 1 0.0000 0.929 1.000 0.000
#> DRR006471 1 0.0000 0.929 1.000 0.000
#> DRR006472 1 0.9686 0.446 0.604 0.396
#> DRR006473 2 0.0000 0.932 0.000 1.000
#> DRR006474 2 0.0000 0.932 0.000 1.000
#> DRR006475 1 0.0000 0.929 1.000 0.000
#> DRR006476 2 0.0000 0.932 0.000 1.000
#> DRR006477 2 0.0000 0.932 0.000 1.000
#> DRR006478 1 0.0000 0.929 1.000 0.000
#> DRR006479 1 0.0000 0.929 1.000 0.000
#> DRR006480 1 0.0000 0.929 1.000 0.000
#> DRR006481 1 0.9427 0.521 0.640 0.360
#> DRR006482 2 0.7219 0.763 0.200 0.800
#> DRR006483 1 0.0000 0.929 1.000 0.000
#> DRR006484 1 0.7219 0.765 0.800 0.200
#> DRR006485 2 0.0000 0.932 0.000 1.000
#> DRR006486 1 0.0000 0.929 1.000 0.000
#> DRR006487 1 0.6247 0.811 0.844 0.156
#> DRR006488 2 0.0000 0.932 0.000 1.000
#> DRR006489 1 0.0000 0.929 1.000 0.000
#> DRR006490 1 0.0938 0.922 0.988 0.012
#> DRR006491 1 0.6247 0.811 0.844 0.156
#> DRR006492 1 0.0000 0.929 1.000 0.000
#> DRR006493 1 0.9686 0.446 0.604 0.396
#> DRR006494 1 0.0000 0.929 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006375 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006376 1 0.0237 0.955 0.996 0.004 0.000
#> DRR006377 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006378 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006379 1 0.4842 0.704 0.776 0.224 0.000
#> DRR006380 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006381 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006382 2 0.4235 0.794 0.000 0.824 0.176
#> DRR006383 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006384 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006385 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006386 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006387 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006388 1 0.4887 0.717 0.772 0.228 0.000
#> DRR006389 1 0.4887 0.717 0.772 0.228 0.000
#> DRR006390 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006391 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006392 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006393 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006394 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006395 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006396 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006397 1 0.0237 0.955 0.996 0.004 0.000
#> DRR006398 1 0.0237 0.955 0.996 0.004 0.000
#> DRR006399 1 0.0237 0.955 0.996 0.004 0.000
#> DRR006400 1 0.0237 0.955 0.996 0.004 0.000
#> DRR006401 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006403 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006404 1 0.4605 0.735 0.796 0.204 0.000
#> DRR006405 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006406 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006407 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006408 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006409 1 0.6225 0.210 0.568 0.000 0.432
#> DRR006410 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006411 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006412 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006413 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006414 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006415 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006416 1 0.4555 0.750 0.800 0.200 0.000
#> DRR006417 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006418 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006419 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006420 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006421 3 0.6204 0.145 0.000 0.424 0.576
#> DRR006422 1 0.2796 0.868 0.908 0.092 0.000
#> DRR006423 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006424 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006425 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006426 3 0.5678 0.550 0.000 0.316 0.684
#> DRR006427 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006428 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006429 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006430 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006431 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006432 3 0.5816 0.744 0.056 0.156 0.788
#> DRR006433 2 0.5650 0.603 0.000 0.688 0.312
#> DRR006434 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006435 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006437 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006438 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006439 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006440 2 0.4555 0.769 0.000 0.800 0.200
#> DRR006441 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006442 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006443 2 0.4555 0.769 0.000 0.800 0.200
#> DRR006444 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006445 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006446 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006447 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006448 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006449 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006450 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006451 2 0.6280 0.160 0.460 0.540 0.000
#> DRR006452 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006453 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006454 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006455 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006456 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006457 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006458 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006459 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006460 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006461 2 0.4555 0.769 0.000 0.800 0.200
#> DRR006462 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006463 2 0.4555 0.769 0.000 0.800 0.200
#> DRR006464 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006465 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006466 2 0.3116 0.856 0.000 0.892 0.108
#> DRR006467 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006468 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006469 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006470 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006471 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006472 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006473 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006474 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006475 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006476 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006477 2 0.5397 0.657 0.000 0.720 0.280
#> DRR006478 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006479 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006480 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006481 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006482 2 0.7133 0.675 0.096 0.712 0.192
#> DRR006483 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006484 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006485 2 0.4555 0.769 0.000 0.800 0.200
#> DRR006486 3 0.6180 0.276 0.416 0.000 0.584
#> DRR006487 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006488 2 0.0000 0.936 0.000 1.000 0.000
#> DRR006489 1 0.0000 0.958 1.000 0.000 0.000
#> DRR006490 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006491 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006492 3 0.3686 0.802 0.140 0.000 0.860
#> DRR006493 3 0.0000 0.935 0.000 0.000 1.000
#> DRR006494 1 0.3816 0.802 0.852 0.000 0.148
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006375 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006376 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006377 4 0.4331 0.586 0.000 0.288 0.000 0.712
#> DRR006378 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006379 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006380 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006381 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006382 2 0.3400 0.795 0.000 0.820 0.180 0.000
#> DRR006383 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006384 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006385 1 0.3219 0.814 0.836 0.000 0.000 0.164
#> DRR006386 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006387 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006388 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006389 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006390 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006391 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006392 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006393 1 0.3219 0.825 0.836 0.000 0.000 0.164
#> DRR006394 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006395 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006396 1 0.3311 0.806 0.828 0.000 0.000 0.172
#> DRR006397 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006398 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006399 4 0.1211 0.939 0.040 0.000 0.000 0.960
#> DRR006400 4 0.1211 0.939 0.040 0.000 0.000 0.960
#> DRR006401 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006402 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006403 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006404 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006405 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006406 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006407 2 0.2868 0.816 0.000 0.864 0.000 0.136
#> DRR006408 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006409 1 0.0336 0.936 0.992 0.000 0.008 0.000
#> DRR006410 1 0.4804 0.490 0.616 0.000 0.000 0.384
#> DRR006411 2 0.4164 0.646 0.000 0.736 0.000 0.264
#> DRR006412 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006413 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006414 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006415 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006416 1 0.4332 0.796 0.800 0.040 0.000 0.160
#> DRR006417 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006418 1 0.1211 0.918 0.960 0.000 0.000 0.040
#> DRR006419 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006420 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006421 3 0.2011 0.846 0.000 0.080 0.920 0.000
#> DRR006422 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006423 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006424 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006425 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006426 3 0.5577 0.475 0.000 0.328 0.636 0.036
#> DRR006427 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006428 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006429 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006430 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006432 3 0.6008 0.490 0.320 0.008 0.628 0.044
#> DRR006433 2 0.4855 0.435 0.000 0.600 0.400 0.000
#> DRR006434 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006435 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006436 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006437 1 0.2814 0.844 0.868 0.000 0.000 0.132
#> DRR006438 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006439 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006440 2 0.3610 0.774 0.000 0.800 0.200 0.000
#> DRR006441 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006442 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006443 2 0.3610 0.774 0.000 0.800 0.200 0.000
#> DRR006444 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006445 1 0.2973 0.842 0.856 0.000 0.000 0.144
#> DRR006446 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006447 1 0.0336 0.937 0.992 0.000 0.000 0.008
#> DRR006448 4 0.1211 0.939 0.040 0.000 0.000 0.960
#> DRR006449 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006450 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006451 4 0.0000 0.969 0.000 0.000 0.000 1.000
#> DRR006452 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006453 1 0.3444 0.807 0.816 0.000 0.000 0.184
#> DRR006454 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006455 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006456 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006457 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006458 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006460 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006461 2 0.3610 0.774 0.000 0.800 0.200 0.000
#> DRR006462 1 0.3311 0.805 0.828 0.000 0.000 0.172
#> DRR006463 2 0.3610 0.774 0.000 0.800 0.200 0.000
#> DRR006464 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006465 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006466 2 0.2345 0.864 0.000 0.900 0.100 0.000
#> DRR006467 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006468 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006469 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006470 3 0.4008 0.672 0.244 0.000 0.756 0.000
#> DRR006471 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006472 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006473 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006474 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006475 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006476 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006477 2 0.4817 0.462 0.000 0.612 0.388 0.000
#> DRR006478 1 0.3400 0.810 0.820 0.000 0.000 0.180
#> DRR006479 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006480 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006481 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006482 2 0.7805 0.479 0.116 0.588 0.068 0.228
#> DRR006483 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006484 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006485 2 0.3610 0.774 0.000 0.800 0.200 0.000
#> DRR006486 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006487 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006488 2 0.0000 0.930 0.000 1.000 0.000 0.000
#> DRR006489 1 0.0000 0.941 1.000 0.000 0.000 0.000
#> DRR006490 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006491 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006492 3 0.4730 0.395 0.364 0.000 0.636 0.000
#> DRR006493 3 0.0000 0.930 0.000 0.000 1.000 0.000
#> DRR006494 1 0.0000 0.941 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
#> DRR006374 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006375 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006376 4 0.0404 0.801 0.000 0.000 0.000 0.988 0.012
#> DRR006377 4 0.5489 0.475 0.000 0.136 0.000 0.648 0.216
#> DRR006378 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006379 4 0.0000 0.801 0.000 0.000 0.000 1.000 0.000
#> DRR006380 2 0.1956 0.870 0.000 0.916 0.008 0.000 0.076
#> DRR006381 1 0.0703 0.847 0.976 0.000 0.000 0.000 0.024
#> DRR006382 2 0.4612 0.677 0.000 0.736 0.180 0.000 0.084
#> DRR006383 3 0.0162 0.825 0.000 0.000 0.996 0.000 0.004
#> DRR006384 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006385 1 0.6114 0.394 0.564 0.000 0.000 0.244 0.192
#> DRR006386 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006387 4 0.2519 0.720 0.100 0.000 0.000 0.884 0.016
#> DRR006388 4 0.4304 0.423 0.000 0.000 0.000 0.516 0.484
#> DRR006389 4 0.4304 0.423 0.000 0.000 0.000 0.516 0.484
#> DRR006390 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006391 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006392 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006393 1 0.3339 0.750 0.840 0.000 0.000 0.112 0.048
#> DRR006394 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006395 4 0.1197 0.780 0.048 0.000 0.000 0.952 0.000
#> DRR006396 1 0.4937 0.565 0.672 0.000 0.000 0.264 0.064
#> DRR006397 4 0.3796 0.591 0.000 0.000 0.000 0.700 0.300
#> DRR006398 4 0.3796 0.591 0.000 0.000 0.000 0.700 0.300
#> DRR006399 4 0.0609 0.797 0.020 0.000 0.000 0.980 0.000
#> DRR006400 4 0.0609 0.797 0.020 0.000 0.000 0.980 0.000
#> DRR006401 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006403 4 0.0000 0.801 0.000 0.000 0.000 1.000 0.000
#> DRR006404 4 0.0162 0.801 0.004 0.000 0.000 0.996 0.000
#> DRR006405 4 0.3452 0.647 0.000 0.000 0.000 0.756 0.244
#> DRR006406 4 0.3452 0.647 0.000 0.000 0.000 0.756 0.244
#> DRR006407 2 0.2864 0.766 0.000 0.852 0.000 0.136 0.012
#> DRR006408 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006409 1 0.0162 0.854 0.996 0.000 0.004 0.000 0.000
#> DRR006410 1 0.4114 0.469 0.624 0.000 0.000 0.376 0.000
#> DRR006411 5 0.5554 0.400 0.000 0.316 0.000 0.092 0.592
#> DRR006412 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006413 1 0.0290 0.853 0.992 0.000 0.000 0.000 0.008
#> DRR006414 3 0.0162 0.827 0.000 0.000 0.996 0.000 0.004
#> DRR006415 3 0.0162 0.827 0.000 0.000 0.996 0.000 0.004
#> DRR006416 1 0.6678 0.088 0.488 0.044 0.000 0.092 0.376
#> DRR006417 3 0.4138 0.515 0.000 0.000 0.616 0.000 0.384
#> DRR006418 5 0.2930 0.472 0.164 0.000 0.000 0.004 0.832
#> DRR006419 3 0.4182 0.487 0.000 0.000 0.600 0.000 0.400
#> DRR006420 3 0.2773 0.753 0.000 0.000 0.836 0.000 0.164
#> DRR006421 3 0.2077 0.763 0.000 0.008 0.908 0.000 0.084
#> DRR006422 1 0.1854 0.821 0.936 0.020 0.000 0.036 0.008
#> DRR006423 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006424 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006425 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006426 5 0.6659 0.464 0.032 0.152 0.172 0.020 0.624
#> DRR006427 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006428 3 0.1732 0.796 0.000 0.000 0.920 0.000 0.080
#> DRR006429 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006430 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006432 5 0.3320 0.453 0.012 0.000 0.164 0.004 0.820
#> DRR006433 3 0.5164 0.377 0.000 0.256 0.660 0.000 0.084
#> DRR006434 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006435 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006437 1 0.6337 0.255 0.512 0.000 0.000 0.192 0.296
#> DRR006438 3 0.3109 0.728 0.000 0.000 0.800 0.000 0.200
#> DRR006439 3 0.1732 0.796 0.000 0.000 0.920 0.000 0.080
#> DRR006440 2 0.4879 0.659 0.000 0.720 0.124 0.000 0.156
#> DRR006441 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006442 3 0.0000 0.827 0.000 0.000 1.000 0.000 0.000
#> DRR006443 2 0.4747 0.655 0.000 0.720 0.196 0.000 0.084
#> DRR006444 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006445 1 0.4349 0.698 0.756 0.000 0.000 0.068 0.176
#> DRR006446 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006447 5 0.4047 0.295 0.320 0.000 0.000 0.004 0.676
#> DRR006448 4 0.0609 0.797 0.020 0.000 0.000 0.980 0.000
#> DRR006449 1 0.3649 0.718 0.808 0.000 0.000 0.040 0.152
#> DRR006450 1 0.3366 0.667 0.768 0.000 0.000 0.000 0.232
#> DRR006451 4 0.0162 0.802 0.000 0.000 0.000 0.996 0.004
#> DRR006452 1 0.0963 0.840 0.964 0.000 0.000 0.000 0.036
#> DRR006453 1 0.4545 0.666 0.752 0.000 0.000 0.132 0.116
#> DRR006454 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006455 2 0.0162 0.929 0.000 0.996 0.000 0.000 0.004
#> DRR006456 3 0.0000 0.827 0.000 0.000 1.000 0.000 0.000
#> DRR006457 3 0.0290 0.824 0.000 0.000 0.992 0.000 0.008
#> DRR006458 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006460 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006461 2 0.4747 0.655 0.000 0.720 0.196 0.000 0.084
#> DRR006462 1 0.6071 0.382 0.556 0.000 0.000 0.284 0.160
#> DRR006463 2 0.4747 0.655 0.000 0.720 0.196 0.000 0.084
#> DRR006464 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006465 1 0.0703 0.846 0.976 0.000 0.000 0.000 0.024
#> DRR006466 2 0.4390 0.708 0.000 0.760 0.156 0.000 0.084
#> DRR006467 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006468 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006469 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006470 3 0.5594 0.263 0.072 0.000 0.492 0.000 0.436
#> DRR006471 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006472 3 0.2891 0.742 0.000 0.000 0.824 0.000 0.176
#> DRR006473 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006474 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006475 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006476 2 0.1430 0.892 0.000 0.944 0.004 0.000 0.052
#> DRR006477 3 0.5254 0.347 0.000 0.272 0.644 0.000 0.084
#> DRR006478 1 0.4025 0.708 0.792 0.000 0.000 0.132 0.076
#> DRR006479 3 0.3109 0.728 0.000 0.000 0.800 0.000 0.200
#> DRR006480 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006481 3 0.0162 0.827 0.000 0.000 0.996 0.000 0.004
#> DRR006482 5 0.7726 0.253 0.020 0.248 0.032 0.256 0.444
#> DRR006483 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006484 3 0.0162 0.827 0.000 0.000 0.996 0.000 0.004
#> DRR006485 2 0.4747 0.655 0.000 0.720 0.196 0.000 0.084
#> DRR006486 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006487 3 0.0290 0.827 0.000 0.000 0.992 0.000 0.008
#> DRR006488 2 0.0000 0.932 0.000 1.000 0.000 0.000 0.000
#> DRR006489 1 0.0000 0.856 1.000 0.000 0.000 0.000 0.000
#> DRR006490 3 0.0404 0.826 0.000 0.000 0.988 0.000 0.012
#> DRR006491 3 0.0162 0.827 0.000 0.000 0.996 0.000 0.004
#> DRR006492 3 0.4288 0.264 0.384 0.000 0.612 0.000 0.004
#> DRR006493 3 0.0000 0.827 0.000 0.000 1.000 0.000 0.000
#> DRR006494 1 0.0000 0.856 1.000 0.000 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
#> DRR006374 2 0.0260 0.9573 0.000 0.992 0.000 0.000 0.008 0.000
#> DRR006375 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006376 4 0.1092 0.7812 0.000 0.000 0.000 0.960 0.020 0.020
#> DRR006377 4 0.5567 0.4796 0.000 0.096 0.000 0.636 0.052 0.216
#> DRR006378 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006379 4 0.0260 0.7912 0.000 0.000 0.000 0.992 0.008 0.000
#> DRR006380 2 0.3672 0.2831 0.000 0.632 0.000 0.000 0.368 0.000
#> DRR006381 1 0.3248 0.7523 0.804 0.000 0.000 0.000 0.164 0.032
#> DRR006382 5 0.5487 0.4994 0.000 0.356 0.136 0.000 0.508 0.000
#> DRR006383 3 0.1204 0.8249 0.000 0.000 0.944 0.000 0.056 0.000
#> DRR006384 2 0.0146 0.9608 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006385 5 0.7214 -0.1864 0.320 0.000 0.000 0.224 0.360 0.096
#> DRR006386 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006387 4 0.3219 0.6584 0.132 0.000 0.000 0.828 0.028 0.012
#> DRR006388 6 0.5803 -0.1613 0.000 0.000 0.000 0.372 0.184 0.444
#> DRR006389 6 0.5803 -0.1613 0.000 0.000 0.000 0.372 0.184 0.444
#> DRR006390 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006391 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006392 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006393 1 0.2589 0.8014 0.888 0.000 0.000 0.024 0.028 0.060
#> DRR006394 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006395 4 0.1858 0.7466 0.076 0.000 0.000 0.912 0.012 0.000
#> DRR006396 1 0.7013 0.1870 0.392 0.000 0.000 0.260 0.280 0.068
#> DRR006397 4 0.5771 0.3012 0.000 0.000 0.000 0.508 0.248 0.244
#> DRR006398 4 0.5771 0.3012 0.000 0.000 0.000 0.508 0.248 0.244
#> DRR006399 4 0.0653 0.7900 0.004 0.000 0.000 0.980 0.012 0.004
#> DRR006400 4 0.0653 0.7900 0.004 0.000 0.000 0.980 0.012 0.004
#> DRR006401 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006403 4 0.0000 0.7919 0.000 0.000 0.000 1.000 0.000 0.000
#> DRR006404 4 0.0363 0.7914 0.000 0.000 0.000 0.988 0.012 0.000
#> DRR006405 4 0.4364 0.5451 0.004 0.000 0.000 0.688 0.052 0.256
#> DRR006406 4 0.4364 0.5451 0.004 0.000 0.000 0.688 0.052 0.256
#> DRR006407 2 0.3593 0.7095 0.000 0.808 0.000 0.136 0.028 0.028
#> DRR006408 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006409 1 0.0713 0.8455 0.972 0.000 0.028 0.000 0.000 0.000
#> DRR006410 1 0.3930 0.4501 0.628 0.000 0.000 0.364 0.004 0.004
#> DRR006411 6 0.6476 0.2129 0.000 0.276 0.000 0.036 0.216 0.472
#> DRR006412 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006413 1 0.2624 0.7922 0.856 0.000 0.000 0.000 0.124 0.020
#> DRR006414 3 0.0146 0.8492 0.000 0.000 0.996 0.000 0.004 0.000
#> DRR006415 3 0.0291 0.8491 0.000 0.000 0.992 0.000 0.004 0.004
#> DRR006416 6 0.6172 -0.0409 0.412 0.012 0.000 0.056 0.060 0.460
#> DRR006417 6 0.5034 -0.1298 0.000 0.000 0.456 0.000 0.072 0.472
#> DRR006418 6 0.1390 0.4369 0.016 0.000 0.000 0.004 0.032 0.948
#> DRR006419 6 0.5033 -0.1198 0.000 0.000 0.452 0.000 0.072 0.476
#> DRR006420 3 0.3834 0.6191 0.000 0.000 0.732 0.000 0.036 0.232
#> DRR006421 5 0.3868 -0.0484 0.000 0.000 0.496 0.000 0.504 0.000
#> DRR006422 1 0.3615 0.7259 0.824 0.108 0.000 0.008 0.036 0.024
#> DRR006423 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006424 1 0.1245 0.8455 0.952 0.000 0.000 0.000 0.032 0.016
#> DRR006425 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006426 6 0.4095 0.4288 0.008 0.076 0.048 0.008 0.048 0.812
#> DRR006427 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006428 3 0.2046 0.8021 0.000 0.000 0.908 0.000 0.060 0.032
#> DRR006429 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006430 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006432 6 0.2461 0.4600 0.000 0.000 0.064 0.004 0.044 0.888
#> DRR006433 5 0.4794 0.1342 0.000 0.052 0.440 0.000 0.508 0.000
#> DRR006434 2 0.0790 0.9317 0.000 0.968 0.000 0.000 0.032 0.000
#> DRR006435 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006437 5 0.7008 -0.2418 0.360 0.000 0.000 0.140 0.388 0.112
#> DRR006438 3 0.3860 0.6693 0.000 0.000 0.764 0.000 0.072 0.164
#> DRR006439 3 0.2106 0.7998 0.000 0.000 0.904 0.000 0.064 0.032
#> DRR006440 5 0.5667 0.4423 0.000 0.352 0.056 0.000 0.540 0.052
#> DRR006441 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006442 3 0.0547 0.8456 0.000 0.000 0.980 0.000 0.020 0.000
#> DRR006443 5 0.5536 0.5057 0.000 0.352 0.144 0.000 0.504 0.000
#> DRR006444 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006445 1 0.6118 0.5055 0.552 0.000 0.000 0.036 0.228 0.184
#> DRR006446 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006447 6 0.5378 0.2935 0.140 0.000 0.000 0.000 0.304 0.556
#> DRR006448 4 0.1232 0.7777 0.004 0.000 0.000 0.956 0.024 0.016
#> DRR006449 1 0.5006 0.5803 0.644 0.000 0.000 0.016 0.264 0.076
#> DRR006450 1 0.4914 0.5613 0.628 0.000 0.000 0.000 0.268 0.104
#> DRR006451 4 0.0622 0.7917 0.000 0.000 0.000 0.980 0.012 0.008
#> DRR006452 1 0.3455 0.7356 0.784 0.000 0.000 0.000 0.180 0.036
#> DRR006453 1 0.4606 0.6505 0.728 0.000 0.000 0.060 0.036 0.176
#> DRR006454 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006455 2 0.0405 0.9535 0.000 0.988 0.000 0.000 0.004 0.008
#> DRR006456 3 0.0547 0.8456 0.000 0.000 0.980 0.000 0.020 0.000
#> DRR006457 3 0.2003 0.7630 0.000 0.000 0.884 0.000 0.116 0.000
#> DRR006458 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006460 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006461 5 0.5478 0.5036 0.000 0.352 0.136 0.000 0.512 0.000
#> DRR006462 5 0.7191 -0.1504 0.288 0.000 0.000 0.272 0.356 0.084
#> DRR006463 5 0.5536 0.5057 0.000 0.352 0.144 0.000 0.504 0.000
#> DRR006464 2 0.0405 0.9522 0.000 0.988 0.000 0.000 0.004 0.008
#> DRR006465 1 0.1257 0.8413 0.952 0.000 0.000 0.000 0.020 0.028
#> DRR006466 5 0.5400 0.4649 0.000 0.376 0.120 0.000 0.504 0.000
#> DRR006467 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006468 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006469 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006470 6 0.5403 -0.0487 0.008 0.000 0.420 0.000 0.088 0.484
#> DRR006471 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006472 3 0.5490 0.2976 0.000 0.000 0.516 0.000 0.140 0.344
#> DRR006473 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006474 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006475 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006476 2 0.3330 0.5116 0.000 0.716 0.000 0.000 0.284 0.000
#> DRR006477 5 0.4837 0.1496 0.000 0.056 0.432 0.000 0.512 0.000
#> DRR006478 1 0.3784 0.7363 0.808 0.000 0.000 0.048 0.036 0.108
#> DRR006479 3 0.3754 0.6833 0.000 0.000 0.776 0.000 0.072 0.152
#> DRR006480 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006481 3 0.0909 0.8475 0.000 0.000 0.968 0.000 0.020 0.012
#> DRR006482 5 0.5063 -0.1071 0.004 0.000 0.004 0.244 0.644 0.104
#> DRR006483 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006484 3 0.0146 0.8495 0.000 0.000 0.996 0.000 0.004 0.000
#> DRR006485 5 0.5536 0.5057 0.000 0.352 0.144 0.000 0.504 0.000
#> DRR006486 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006487 3 0.0692 0.8458 0.000 0.000 0.976 0.000 0.020 0.004
#> DRR006488 2 0.0000 0.9641 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006489 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006490 3 0.1092 0.8365 0.000 0.000 0.960 0.000 0.020 0.020
#> DRR006491 3 0.0146 0.8492 0.000 0.000 0.996 0.000 0.004 0.000
#> DRR006492 3 0.4321 0.2213 0.400 0.000 0.580 0.000 0.008 0.012
#> DRR006493 3 0.0713 0.8417 0.000 0.000 0.972 0.000 0.028 0.000
#> DRR006494 1 0.0000 0.8627 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.428 0.788 0.892 0.4754 0.498 0.498
#> 3 3 0.579 0.685 0.785 0.3816 0.708 0.484
#> 4 4 0.850 0.865 0.940 0.1170 0.834 0.575
#> 5 5 0.795 0.816 0.892 0.0566 0.915 0.705
#> 6 6 0.836 0.812 0.902 0.0355 0.963 0.837
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
#> DRR006374 2 0.0000 0.9095 0.000 1.000
#> DRR006375 1 0.0000 0.8309 1.000 0.000
#> DRR006376 1 0.9933 0.3815 0.548 0.452
#> DRR006377 2 0.8861 0.4234 0.304 0.696
#> DRR006378 2 0.0000 0.9095 0.000 1.000
#> DRR006379 2 0.3879 0.8467 0.076 0.924
#> DRR006380 2 0.0000 0.9095 0.000 1.000
#> DRR006381 1 0.0000 0.8309 1.000 0.000
#> DRR006382 2 0.0000 0.9095 0.000 1.000
#> DRR006383 1 0.9323 0.6216 0.652 0.348
#> DRR006384 2 0.0000 0.9095 0.000 1.000
#> DRR006385 1 0.6343 0.8263 0.840 0.160
#> DRR006386 2 0.0000 0.9095 0.000 1.000
#> DRR006387 1 0.6887 0.8207 0.816 0.184
#> DRR006388 2 0.6712 0.7299 0.176 0.824
#> DRR006389 2 0.6623 0.7349 0.172 0.828
#> DRR006390 2 0.0000 0.9095 0.000 1.000
#> DRR006391 2 0.0000 0.9095 0.000 1.000
#> DRR006392 1 0.0000 0.8309 1.000 0.000
#> DRR006393 1 0.6887 0.8207 0.816 0.184
#> DRR006394 2 0.0000 0.9095 0.000 1.000
#> DRR006395 1 0.6712 0.8234 0.824 0.176
#> DRR006396 1 0.0000 0.8309 1.000 0.000
#> DRR006397 2 0.6973 0.7150 0.188 0.812
#> DRR006398 2 0.6887 0.7203 0.184 0.816
#> DRR006399 1 0.0376 0.8315 0.996 0.004
#> DRR006400 1 0.0376 0.8315 0.996 0.004
#> DRR006401 2 0.0000 0.9095 0.000 1.000
#> DRR006402 2 0.0000 0.9095 0.000 1.000
#> DRR006403 1 0.6887 0.8207 0.816 0.184
#> DRR006404 1 0.9922 0.3951 0.552 0.448
#> DRR006405 1 0.9933 0.3815 0.548 0.452
#> DRR006406 1 0.9933 0.3815 0.548 0.452
#> DRR006407 2 0.0000 0.9095 0.000 1.000
#> DRR006408 2 0.0000 0.9095 0.000 1.000
#> DRR006409 1 0.0376 0.8315 0.996 0.004
#> DRR006410 1 0.6712 0.8234 0.824 0.176
#> DRR006411 2 0.0376 0.9065 0.004 0.996
#> DRR006412 2 0.0000 0.9095 0.000 1.000
#> DRR006413 1 0.0000 0.8309 1.000 0.000
#> DRR006414 1 0.7056 0.8166 0.808 0.192
#> DRR006415 1 0.8661 0.7149 0.712 0.288
#> DRR006416 1 0.9954 0.3648 0.540 0.460
#> DRR006417 1 0.8555 0.7281 0.720 0.280
#> DRR006418 1 0.6887 0.8207 0.816 0.184
#> DRR006419 1 0.7219 0.8114 0.800 0.200
#> DRR006420 1 0.7139 0.8142 0.804 0.196
#> DRR006421 2 0.8386 0.5634 0.268 0.732
#> DRR006422 2 0.9815 0.0183 0.420 0.580
#> DRR006423 2 0.0000 0.9095 0.000 1.000
#> DRR006424 1 0.0000 0.8309 1.000 0.000
#> DRR006425 2 0.0000 0.9095 0.000 1.000
#> DRR006426 2 0.9850 -0.0214 0.428 0.572
#> DRR006427 2 0.0000 0.9095 0.000 1.000
#> DRR006428 1 0.0938 0.8303 0.988 0.012
#> DRR006429 2 0.0000 0.9095 0.000 1.000
#> DRR006430 1 0.0000 0.8309 1.000 0.000
#> DRR006431 1 0.0000 0.8309 1.000 0.000
#> DRR006432 1 0.7056 0.8168 0.808 0.192
#> DRR006433 2 0.8499 0.5495 0.276 0.724
#> DRR006434 2 0.0000 0.9095 0.000 1.000
#> DRR006435 2 0.0000 0.9095 0.000 1.000
#> DRR006436 2 0.0000 0.9095 0.000 1.000
#> DRR006437 1 0.0000 0.8309 1.000 0.000
#> DRR006438 1 0.6712 0.8234 0.824 0.176
#> DRR006439 1 0.7056 0.8166 0.808 0.192
#> DRR006440 2 0.0000 0.9095 0.000 1.000
#> DRR006441 2 0.0000 0.9095 0.000 1.000
#> DRR006442 1 0.7299 0.8082 0.796 0.204
#> DRR006443 2 0.0000 0.9095 0.000 1.000
#> DRR006444 2 0.0000 0.9095 0.000 1.000
#> DRR006445 1 0.8861 0.6863 0.696 0.304
#> DRR006446 2 0.0000 0.9095 0.000 1.000
#> DRR006447 1 0.6712 0.8234 0.824 0.176
#> DRR006448 1 0.0000 0.8309 1.000 0.000
#> DRR006449 1 0.0376 0.8315 0.996 0.004
#> DRR006450 1 0.0000 0.8309 1.000 0.000
#> DRR006451 2 0.6531 0.7425 0.168 0.832
#> DRR006452 1 0.0000 0.8309 1.000 0.000
#> DRR006453 1 0.9248 0.6267 0.660 0.340
#> DRR006454 2 0.0000 0.9095 0.000 1.000
#> DRR006455 2 0.0000 0.9095 0.000 1.000
#> DRR006456 1 0.9393 0.6034 0.644 0.356
#> DRR006457 2 0.8499 0.5495 0.276 0.724
#> DRR006458 1 0.0000 0.8309 1.000 0.000
#> DRR006459 1 0.0000 0.8309 1.000 0.000
#> DRR006460 2 0.0000 0.9095 0.000 1.000
#> DRR006461 2 0.0000 0.9095 0.000 1.000
#> DRR006462 1 0.0000 0.8309 1.000 0.000
#> DRR006463 2 0.0000 0.9095 0.000 1.000
#> DRR006464 2 0.0000 0.9095 0.000 1.000
#> DRR006465 1 0.6887 0.8207 0.816 0.184
#> DRR006466 2 0.0000 0.9095 0.000 1.000
#> DRR006467 1 0.0000 0.8309 1.000 0.000
#> DRR006468 2 0.0000 0.9095 0.000 1.000
#> DRR006469 2 0.0000 0.9095 0.000 1.000
#> DRR006470 1 0.6712 0.8234 0.824 0.176
#> DRR006471 1 0.0000 0.8309 1.000 0.000
#> DRR006472 1 0.9944 0.3983 0.544 0.456
#> DRR006473 2 0.0000 0.9095 0.000 1.000
#> DRR006474 2 0.0000 0.9095 0.000 1.000
#> DRR006475 1 0.0000 0.8309 1.000 0.000
#> DRR006476 2 0.0000 0.9095 0.000 1.000
#> DRR006477 2 0.4161 0.8345 0.084 0.916
#> DRR006478 1 0.6887 0.8207 0.816 0.184
#> DRR006479 1 0.6712 0.8234 0.824 0.176
#> DRR006480 1 0.0000 0.8309 1.000 0.000
#> DRR006481 2 0.8555 0.5430 0.280 0.720
#> DRR006482 2 0.9000 0.4767 0.316 0.684
#> DRR006483 1 0.0000 0.8309 1.000 0.000
#> DRR006484 1 0.7950 0.7750 0.760 0.240
#> DRR006485 2 0.0000 0.9095 0.000 1.000
#> DRR006486 1 0.0000 0.8309 1.000 0.000
#> DRR006487 1 0.7674 0.7906 0.776 0.224
#> DRR006488 2 0.0000 0.9095 0.000 1.000
#> DRR006489 1 0.0000 0.8309 1.000 0.000
#> DRR006490 1 0.7056 0.8166 0.808 0.192
#> DRR006491 1 0.7056 0.8166 0.808 0.192
#> DRR006492 1 0.6712 0.8234 0.824 0.176
#> DRR006493 2 0.8661 0.5268 0.288 0.712
#> DRR006494 1 0.0000 0.8309 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006375 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006376 1 0.1529 0.6525 0.960 0.000 0.040
#> DRR006377 1 0.7236 0.2633 0.576 0.392 0.032
#> DRR006378 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006379 1 0.7661 0.0674 0.504 0.452 0.044
#> DRR006380 2 0.1964 0.9009 0.000 0.944 0.056
#> DRR006381 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006382 2 0.4291 0.7870 0.000 0.820 0.180
#> DRR006383 3 0.5327 0.6034 0.000 0.272 0.728
#> DRR006384 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006385 1 0.1031 0.6651 0.976 0.000 0.024
#> DRR006386 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006387 1 0.1031 0.6651 0.976 0.000 0.024
#> DRR006388 1 0.7571 0.0788 0.508 0.452 0.040
#> DRR006389 2 0.7480 0.0543 0.456 0.508 0.036
#> DRR006390 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006391 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006392 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006393 1 0.0983 0.6683 0.980 0.004 0.016
#> DRR006394 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006395 1 0.3619 0.5496 0.864 0.000 0.136
#> DRR006396 1 0.0000 0.6703 1.000 0.000 0.000
#> DRR006397 1 0.7400 0.1933 0.552 0.412 0.036
#> DRR006398 1 0.7619 0.1531 0.532 0.424 0.044
#> DRR006399 1 0.6154 -0.2488 0.592 0.000 0.408
#> DRR006400 1 0.6154 -0.2488 0.592 0.000 0.408
#> DRR006401 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006403 1 0.5650 0.1460 0.688 0.000 0.312
#> DRR006404 1 0.1267 0.6648 0.972 0.004 0.024
#> DRR006405 1 0.1182 0.6677 0.976 0.012 0.012
#> DRR006406 1 0.1182 0.6677 0.976 0.012 0.012
#> DRR006407 2 0.0424 0.9327 0.000 0.992 0.008
#> DRR006408 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006409 3 0.1529 0.5487 0.040 0.000 0.960
#> DRR006410 1 0.0424 0.6698 0.992 0.000 0.008
#> DRR006411 2 0.5692 0.5366 0.268 0.724 0.008
#> DRR006412 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006413 1 0.5529 0.6419 0.704 0.000 0.296
#> DRR006414 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006415 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006416 1 0.1170 0.6673 0.976 0.008 0.016
#> DRR006417 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006418 1 0.1031 0.6651 0.976 0.000 0.024
#> DRR006419 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006420 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006421 3 0.5327 0.6034 0.000 0.272 0.728
#> DRR006422 1 0.6584 0.3689 0.608 0.380 0.012
#> DRR006423 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006424 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006425 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006426 1 0.7759 0.3074 0.676 0.144 0.180
#> DRR006427 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006428 3 0.0424 0.5834 0.008 0.000 0.992
#> DRR006429 2 0.0592 0.9308 0.000 0.988 0.012
#> DRR006430 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006431 1 0.5948 0.6026 0.640 0.000 0.360
#> DRR006432 3 0.6540 0.6260 0.408 0.008 0.584
#> DRR006433 3 0.5480 0.6152 0.004 0.264 0.732
#> DRR006434 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006435 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006437 3 0.6215 0.5868 0.428 0.000 0.572
#> DRR006438 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006439 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006440 2 0.4605 0.7549 0.000 0.796 0.204
#> DRR006441 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006442 3 0.3192 0.7071 0.112 0.000 0.888
#> DRR006443 2 0.4346 0.7833 0.000 0.816 0.184
#> DRR006444 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006445 1 0.1129 0.6666 0.976 0.004 0.020
#> DRR006446 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006447 1 0.1031 0.6651 0.976 0.000 0.024
#> DRR006448 1 0.0000 0.6703 1.000 0.000 0.000
#> DRR006449 3 0.6295 0.1819 0.472 0.000 0.528
#> DRR006450 1 0.6215 -0.2915 0.572 0.000 0.428
#> DRR006451 1 0.7213 0.1819 0.552 0.420 0.028
#> DRR006452 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006453 1 0.1129 0.6666 0.976 0.004 0.020
#> DRR006454 2 0.1289 0.9182 0.000 0.968 0.032
#> DRR006455 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006456 3 0.6949 0.7527 0.156 0.112 0.732
#> DRR006457 3 0.6588 0.6800 0.060 0.208 0.732
#> DRR006458 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006459 1 0.5968 0.5996 0.636 0.000 0.364
#> DRR006460 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006461 2 0.4346 0.7833 0.000 0.816 0.184
#> DRR006462 1 0.0592 0.6682 0.988 0.000 0.012
#> DRR006463 2 0.4346 0.7833 0.000 0.816 0.184
#> DRR006464 2 0.0424 0.9327 0.000 0.992 0.008
#> DRR006465 1 0.0592 0.6710 0.988 0.000 0.012
#> DRR006466 2 0.4346 0.7833 0.000 0.816 0.184
#> DRR006467 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006468 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006469 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006470 3 0.5621 0.7704 0.308 0.000 0.692
#> DRR006471 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006472 3 0.7465 0.7592 0.272 0.072 0.656
#> DRR006473 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006474 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006475 1 0.6140 0.5650 0.596 0.000 0.404
#> DRR006476 2 0.1753 0.9082 0.000 0.952 0.048
#> DRR006477 3 0.5926 0.4549 0.000 0.356 0.644
#> DRR006478 1 0.1129 0.6666 0.976 0.004 0.020
#> DRR006479 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006480 1 0.6168 0.5568 0.588 0.000 0.412
#> DRR006481 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006482 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006483 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006484 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006485 2 0.4178 0.7952 0.000 0.828 0.172
#> DRR006486 1 0.6168 0.5568 0.588 0.000 0.412
#> DRR006487 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006488 2 0.0000 0.9370 0.000 1.000 0.000
#> DRR006489 1 0.5291 0.6530 0.732 0.000 0.268
#> DRR006490 3 0.4346 0.7665 0.184 0.000 0.816
#> DRR006491 3 0.5291 0.8099 0.268 0.000 0.732
#> DRR006492 3 0.5465 0.7944 0.288 0.000 0.712
#> DRR006493 3 0.5480 0.6152 0.004 0.264 0.732
#> DRR006494 1 0.6168 0.5568 0.588 0.000 0.412
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006375 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006376 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006377 4 0.1022 0.87397 0.000 0.032 0.000 0.968
#> DRR006378 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006379 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006380 2 0.1557 0.91217 0.000 0.944 0.056 0.000
#> DRR006381 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006382 2 0.3610 0.78338 0.000 0.800 0.200 0.000
#> DRR006383 3 0.2408 0.83924 0.000 0.104 0.896 0.000
#> DRR006384 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006385 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006386 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006387 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006388 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006389 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006390 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006391 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006392 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006393 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006394 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006395 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006396 1 0.4277 0.58701 0.720 0.000 0.000 0.280
#> DRR006397 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006398 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006399 4 0.2589 0.81026 0.116 0.000 0.000 0.884
#> DRR006400 4 0.3024 0.77935 0.148 0.000 0.000 0.852
#> DRR006401 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006402 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006403 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006404 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006405 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006406 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006407 2 0.2011 0.88342 0.000 0.920 0.000 0.080
#> DRR006408 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006409 3 0.4454 0.52005 0.308 0.000 0.692 0.000
#> DRR006410 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006411 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006412 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006413 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006414 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006415 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006416 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006417 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006418 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006419 4 0.4925 0.32995 0.000 0.000 0.428 0.572
#> DRR006420 4 0.4967 0.27106 0.000 0.000 0.452 0.548
#> DRR006421 3 0.5793 0.27099 0.000 0.384 0.580 0.036
#> DRR006422 4 0.4998 -0.00118 0.000 0.488 0.000 0.512
#> DRR006423 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006424 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006425 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006426 4 0.1211 0.87094 0.000 0.000 0.040 0.960
#> DRR006427 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006428 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006429 2 0.0524 0.94102 0.000 0.988 0.004 0.008
#> DRR006430 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006432 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006433 3 0.0592 0.93160 0.000 0.016 0.984 0.000
#> DRR006434 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006435 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006436 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006437 4 0.7380 0.36623 0.200 0.000 0.288 0.512
#> DRR006438 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006439 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006440 2 0.3610 0.78338 0.000 0.800 0.200 0.000
#> DRR006441 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006442 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006443 2 0.3610 0.78338 0.000 0.800 0.200 0.000
#> DRR006444 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006445 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006446 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006447 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006448 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006449 4 0.6634 0.47026 0.292 0.000 0.116 0.592
#> DRR006450 4 0.3390 0.79098 0.016 0.000 0.132 0.852
#> DRR006451 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006452 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006453 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006454 2 0.0657 0.93961 0.000 0.984 0.012 0.004
#> DRR006455 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006456 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006457 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006458 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006460 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006461 2 0.3610 0.78338 0.000 0.800 0.200 0.000
#> DRR006462 4 0.4431 0.55224 0.304 0.000 0.000 0.696
#> DRR006463 2 0.3610 0.78338 0.000 0.800 0.200 0.000
#> DRR006464 2 0.0188 0.94412 0.000 0.996 0.000 0.004
#> DRR006465 4 0.0336 0.88963 0.008 0.000 0.000 0.992
#> DRR006466 2 0.3610 0.78338 0.000 0.800 0.200 0.000
#> DRR006467 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006468 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006469 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006470 3 0.1867 0.87393 0.000 0.000 0.928 0.072
#> DRR006471 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006472 4 0.3569 0.72260 0.000 0.000 0.196 0.804
#> DRR006473 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006474 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006475 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006476 2 0.1284 0.92893 0.000 0.964 0.024 0.012
#> DRR006477 2 0.4804 0.43714 0.000 0.616 0.384 0.000
#> DRR006478 4 0.0000 0.89417 0.000 0.000 0.000 1.000
#> DRR006479 3 0.0817 0.92336 0.000 0.000 0.976 0.024
#> DRR006480 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006481 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006482 4 0.4679 0.50756 0.000 0.000 0.352 0.648
#> DRR006483 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006484 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006485 2 0.3610 0.78338 0.000 0.800 0.200 0.000
#> DRR006486 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006487 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006488 2 0.0000 0.94674 0.000 1.000 0.000 0.000
#> DRR006489 1 0.0000 0.97264 1.000 0.000 0.000 0.000
#> DRR006490 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006491 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006492 4 0.4431 0.57980 0.000 0.000 0.304 0.696
#> DRR006493 3 0.0000 0.94445 0.000 0.000 1.000 0.000
#> DRR006494 1 0.2921 0.80516 0.860 0.000 0.140 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006375 1 0.0162 0.9737 0.996 0.000 0.000 0.000 0.004
#> DRR006376 4 0.0162 0.8889 0.000 0.000 0.000 0.996 0.004
#> DRR006377 4 0.1041 0.8729 0.000 0.032 0.000 0.964 0.004
#> DRR006378 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006379 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006380 5 0.3305 0.7783 0.000 0.224 0.000 0.000 0.776
#> DRR006381 1 0.0162 0.9737 0.996 0.000 0.000 0.000 0.004
#> DRR006382 5 0.3300 0.7875 0.000 0.204 0.004 0.000 0.792
#> DRR006383 3 0.3521 0.7388 0.000 0.004 0.764 0.000 0.232
#> DRR006384 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006385 4 0.0162 0.8886 0.000 0.000 0.000 0.996 0.004
#> DRR006386 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006387 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006388 4 0.0162 0.8891 0.000 0.000 0.000 0.996 0.004
#> DRR006389 4 0.0162 0.8891 0.000 0.000 0.000 0.996 0.004
#> DRR006390 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006391 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006392 1 0.0000 0.9744 1.000 0.000 0.000 0.000 0.000
#> DRR006393 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006394 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006395 4 0.0162 0.8891 0.000 0.000 0.000 0.996 0.004
#> DRR006396 1 0.3928 0.5479 0.700 0.000 0.000 0.296 0.004
#> DRR006397 4 0.0162 0.8891 0.000 0.000 0.000 0.996 0.004
#> DRR006398 4 0.0162 0.8891 0.000 0.000 0.000 0.996 0.004
#> DRR006399 4 0.4844 0.7253 0.108 0.000 0.000 0.720 0.172
#> DRR006400 4 0.5224 0.6883 0.140 0.000 0.000 0.684 0.176
#> DRR006401 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006403 4 0.2852 0.8086 0.000 0.000 0.000 0.828 0.172
#> DRR006404 4 0.2852 0.8086 0.000 0.000 0.000 0.828 0.172
#> DRR006405 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006406 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006407 5 0.5390 0.6302 0.000 0.324 0.000 0.076 0.600
#> DRR006408 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006409 3 0.4524 0.4936 0.336 0.000 0.644 0.000 0.020
#> DRR006410 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006411 4 0.0162 0.8891 0.000 0.000 0.000 0.996 0.004
#> DRR006412 2 0.0880 0.9488 0.000 0.968 0.000 0.000 0.032
#> DRR006413 1 0.0162 0.9737 0.996 0.000 0.000 0.000 0.004
#> DRR006414 3 0.3336 0.7449 0.000 0.000 0.772 0.000 0.228
#> DRR006415 3 0.2074 0.8051 0.000 0.000 0.896 0.000 0.104
#> DRR006416 4 0.0162 0.8891 0.000 0.000 0.000 0.996 0.004
#> DRR006417 3 0.6542 0.0395 0.000 0.000 0.428 0.200 0.372
#> DRR006418 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006419 4 0.5066 0.4922 0.000 0.000 0.344 0.608 0.048
#> DRR006420 4 0.4545 0.6980 0.000 0.000 0.132 0.752 0.116
#> DRR006421 5 0.4600 0.6812 0.000 0.104 0.136 0.004 0.756
#> DRR006422 4 0.3840 0.7334 0.000 0.116 0.000 0.808 0.076
#> DRR006423 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006424 1 0.0162 0.9737 0.996 0.000 0.000 0.000 0.004
#> DRR006425 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006426 4 0.2377 0.8091 0.000 0.000 0.000 0.872 0.128
#> DRR006427 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006428 3 0.0703 0.8078 0.000 0.000 0.976 0.000 0.024
#> DRR006429 5 0.4375 0.5117 0.000 0.420 0.000 0.004 0.576
#> DRR006430 1 0.0162 0.9737 0.996 0.000 0.000 0.000 0.004
#> DRR006431 1 0.0000 0.9744 1.000 0.000 0.000 0.000 0.000
#> DRR006432 4 0.0162 0.8891 0.000 0.000 0.000 0.996 0.004
#> DRR006433 5 0.4042 0.5668 0.000 0.032 0.212 0.000 0.756
#> DRR006434 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006435 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006437 4 0.5531 0.5816 0.080 0.000 0.264 0.644 0.012
#> DRR006438 3 0.1121 0.8047 0.000 0.000 0.956 0.000 0.044
#> DRR006439 5 0.4210 0.2545 0.000 0.000 0.412 0.000 0.588
#> DRR006440 5 0.3266 0.7883 0.000 0.200 0.004 0.000 0.796
#> DRR006441 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006442 3 0.3003 0.7694 0.000 0.000 0.812 0.000 0.188
#> DRR006443 5 0.3388 0.7877 0.000 0.200 0.008 0.000 0.792
#> DRR006444 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006445 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006446 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006447 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006448 4 0.2852 0.8065 0.000 0.000 0.000 0.828 0.172
#> DRR006449 4 0.4503 0.5581 0.312 0.000 0.024 0.664 0.000
#> DRR006450 4 0.0912 0.8803 0.012 0.000 0.016 0.972 0.000
#> DRR006451 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006452 1 0.0324 0.9708 0.992 0.000 0.000 0.004 0.004
#> DRR006453 4 0.0000 0.8893 0.000 0.000 0.000 1.000 0.000
#> DRR006454 5 0.4531 0.5030 0.000 0.424 0.004 0.004 0.568
#> DRR006455 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006456 3 0.3242 0.7500 0.000 0.000 0.784 0.000 0.216
#> DRR006457 5 0.3452 0.5081 0.000 0.000 0.244 0.000 0.756
#> DRR006458 1 0.0000 0.9744 1.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.9744 1.000 0.000 0.000 0.000 0.000
#> DRR006460 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006461 5 0.3266 0.7883 0.000 0.200 0.004 0.000 0.796
#> DRR006462 4 0.3861 0.5924 0.284 0.000 0.000 0.712 0.004
#> DRR006463 5 0.3922 0.7756 0.000 0.180 0.040 0.000 0.780
#> DRR006464 2 0.5211 0.1321 0.000 0.524 0.000 0.432 0.044
#> DRR006465 4 0.0404 0.8860 0.012 0.000 0.000 0.988 0.000
#> DRR006466 5 0.3266 0.7883 0.000 0.200 0.004 0.000 0.796
#> DRR006467 1 0.0000 0.9744 1.000 0.000 0.000 0.000 0.000
#> DRR006468 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006469 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006470 4 0.5267 0.3005 0.000 0.000 0.428 0.524 0.048
#> DRR006471 1 0.0000 0.9744 1.000 0.000 0.000 0.000 0.000
#> DRR006472 4 0.4310 0.3974 0.000 0.000 0.004 0.604 0.392
#> DRR006473 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006474 2 0.1043 0.9478 0.000 0.960 0.000 0.000 0.040
#> DRR006475 1 0.0000 0.9744 1.000 0.000 0.000 0.000 0.000
#> DRR006476 5 0.4196 0.6339 0.000 0.356 0.000 0.004 0.640
#> DRR006477 5 0.4493 0.7167 0.000 0.136 0.108 0.000 0.756
#> DRR006478 4 0.0162 0.8891 0.000 0.000 0.000 0.996 0.004
#> DRR006479 3 0.1197 0.8035 0.000 0.000 0.952 0.000 0.048
#> DRR006480 1 0.0000 0.9744 1.000 0.000 0.000 0.000 0.000
#> DRR006481 5 0.3305 0.5106 0.000 0.000 0.224 0.000 0.776
#> DRR006482 5 0.4180 0.4968 0.000 0.000 0.036 0.220 0.744
#> DRR006483 1 0.0000 0.9744 1.000 0.000 0.000 0.000 0.000
#> DRR006484 3 0.1410 0.8165 0.000 0.000 0.940 0.000 0.060
#> DRR006485 5 0.3596 0.7859 0.000 0.200 0.016 0.000 0.784
#> DRR006486 1 0.0000 0.9744 1.000 0.000 0.000 0.000 0.000
#> DRR006487 3 0.0290 0.8145 0.000 0.000 0.992 0.000 0.008
#> DRR006488 2 0.0000 0.9508 0.000 1.000 0.000 0.000 0.000
#> DRR006489 1 0.0162 0.9737 0.996 0.000 0.000 0.000 0.004
#> DRR006490 3 0.0000 0.8133 0.000 0.000 1.000 0.000 0.000
#> DRR006491 3 0.0000 0.8133 0.000 0.000 1.000 0.000 0.000
#> DRR006492 4 0.3759 0.6974 0.000 0.000 0.220 0.764 0.016
#> DRR006493 3 0.3242 0.7500 0.000 0.000 0.784 0.000 0.216
#> DRR006494 1 0.0703 0.9480 0.976 0.000 0.024 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006375 1 0.2838 0.837 0.808 0.000 0.000 0.188 0.004 0.000
#> DRR006376 6 0.0146 0.866 0.000 0.000 0.000 0.004 0.000 0.996
#> DRR006377 6 0.0935 0.845 0.000 0.032 0.000 0.000 0.004 0.964
#> DRR006378 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006379 6 0.0000 0.867 0.000 0.000 0.000 0.000 0.000 1.000
#> DRR006380 5 0.1141 0.826 0.000 0.052 0.000 0.000 0.948 0.000
#> DRR006381 1 0.2738 0.844 0.820 0.000 0.000 0.176 0.004 0.000
#> DRR006382 5 0.1075 0.828 0.000 0.048 0.000 0.000 0.952 0.000
#> DRR006383 3 0.3076 0.769 0.000 0.000 0.760 0.000 0.240 0.000
#> DRR006384 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006385 6 0.2697 0.700 0.000 0.000 0.000 0.188 0.000 0.812
#> DRR006386 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006387 6 0.0000 0.867 0.000 0.000 0.000 0.000 0.000 1.000
#> DRR006388 6 0.0146 0.867 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006389 6 0.0146 0.867 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006390 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006391 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006392 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006393 6 0.0146 0.866 0.004 0.000 0.000 0.000 0.000 0.996
#> DRR006394 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006395 6 0.0146 0.867 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006396 1 0.5140 0.570 0.640 0.000 0.000 0.192 0.004 0.164
#> DRR006397 6 0.0146 0.867 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006398 6 0.0146 0.867 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006399 4 0.0363 0.768 0.000 0.000 0.000 0.988 0.000 0.012
#> DRR006400 4 0.0363 0.768 0.000 0.000 0.000 0.988 0.000 0.012
#> DRR006401 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006403 4 0.3601 0.654 0.000 0.000 0.000 0.684 0.004 0.312
#> DRR006404 4 0.3668 0.630 0.000 0.000 0.000 0.668 0.004 0.328
#> DRR006405 6 0.0000 0.867 0.000 0.000 0.000 0.000 0.000 1.000
#> DRR006406 6 0.0000 0.867 0.000 0.000 0.000 0.000 0.000 1.000
#> DRR006407 5 0.3979 0.675 0.000 0.172 0.000 0.000 0.752 0.076
#> DRR006408 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006409 3 0.3887 0.500 0.360 0.000 0.632 0.000 0.008 0.000
#> DRR006410 6 0.0000 0.867 0.000 0.000 0.000 0.000 0.000 1.000
#> DRR006411 6 0.0146 0.867 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006412 2 0.1327 0.954 0.000 0.936 0.000 0.000 0.064 0.000
#> DRR006413 1 0.2838 0.837 0.808 0.000 0.000 0.188 0.004 0.000
#> DRR006414 3 0.3050 0.772 0.000 0.000 0.764 0.000 0.236 0.000
#> DRR006415 3 0.2003 0.816 0.000 0.000 0.884 0.000 0.116 0.000
#> DRR006416 6 0.0146 0.867 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006417 5 0.6355 0.104 0.000 0.000 0.320 0.012 0.396 0.272
#> DRR006418 6 0.0000 0.867 0.000 0.000 0.000 0.000 0.000 1.000
#> DRR006419 6 0.4582 0.515 0.000 0.000 0.296 0.012 0.040 0.652
#> DRR006420 6 0.3285 0.729 0.000 0.000 0.064 0.000 0.116 0.820
#> DRR006421 5 0.2451 0.794 0.000 0.040 0.068 0.000 0.888 0.004
#> DRR006422 6 0.2679 0.750 0.000 0.040 0.000 0.000 0.096 0.864
#> DRR006423 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006424 1 0.2668 0.848 0.828 0.000 0.000 0.168 0.004 0.000
#> DRR006425 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006426 6 0.2260 0.758 0.000 0.000 0.000 0.000 0.140 0.860
#> DRR006427 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006428 3 0.1151 0.804 0.000 0.000 0.956 0.012 0.032 0.000
#> DRR006429 5 0.3383 0.626 0.000 0.268 0.000 0.000 0.728 0.004
#> DRR006430 1 0.2668 0.848 0.828 0.000 0.000 0.168 0.004 0.000
#> DRR006431 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006432 6 0.0146 0.867 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006433 5 0.2404 0.785 0.000 0.036 0.080 0.000 0.884 0.000
#> DRR006434 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006435 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006437 6 0.4966 0.588 0.020 0.000 0.072 0.196 0.012 0.700
#> DRR006438 3 0.1297 0.800 0.000 0.000 0.948 0.012 0.040 0.000
#> DRR006439 5 0.3766 0.523 0.000 0.000 0.304 0.012 0.684 0.000
#> DRR006440 5 0.1007 0.829 0.000 0.044 0.000 0.000 0.956 0.000
#> DRR006441 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006442 3 0.2823 0.789 0.000 0.000 0.796 0.000 0.204 0.000
#> DRR006443 5 0.1152 0.828 0.000 0.044 0.004 0.000 0.952 0.000
#> DRR006444 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006445 6 0.0000 0.867 0.000 0.000 0.000 0.000 0.000 1.000
#> DRR006446 2 0.0146 0.957 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006447 6 0.0000 0.867 0.000 0.000 0.000 0.000 0.000 1.000
#> DRR006448 4 0.0363 0.768 0.000 0.000 0.000 0.988 0.000 0.012
#> DRR006449 6 0.3134 0.678 0.168 0.000 0.000 0.024 0.000 0.808
#> DRR006450 6 0.0291 0.864 0.004 0.000 0.000 0.004 0.000 0.992
#> DRR006451 6 0.0000 0.867 0.000 0.000 0.000 0.000 0.000 1.000
#> DRR006452 1 0.2979 0.834 0.804 0.000 0.000 0.188 0.004 0.004
#> DRR006453 6 0.0000 0.867 0.000 0.000 0.000 0.000 0.000 1.000
#> DRR006454 5 0.3426 0.616 0.000 0.276 0.000 0.000 0.720 0.004
#> DRR006455 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006456 3 0.3050 0.772 0.000 0.000 0.764 0.000 0.236 0.000
#> DRR006457 5 0.2048 0.742 0.000 0.000 0.120 0.000 0.880 0.000
#> DRR006458 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006460 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006461 5 0.1007 0.829 0.000 0.044 0.000 0.000 0.956 0.000
#> DRR006462 6 0.5579 0.192 0.264 0.000 0.000 0.192 0.000 0.544
#> DRR006463 5 0.1257 0.817 0.000 0.020 0.028 0.000 0.952 0.000
#> DRR006464 6 0.5029 0.229 0.000 0.376 0.000 0.000 0.080 0.544
#> DRR006465 6 0.2597 0.722 0.176 0.000 0.000 0.000 0.000 0.824
#> DRR006466 5 0.1007 0.829 0.000 0.044 0.000 0.000 0.956 0.000
#> DRR006467 1 0.0146 0.895 0.996 0.000 0.000 0.000 0.004 0.000
#> DRR006468 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006469 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006470 6 0.4680 0.474 0.000 0.000 0.320 0.012 0.040 0.628
#> DRR006471 1 0.0146 0.895 0.996 0.000 0.000 0.000 0.004 0.000
#> DRR006472 6 0.3782 0.349 0.000 0.000 0.000 0.000 0.412 0.588
#> DRR006473 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006474 2 0.1501 0.953 0.000 0.924 0.000 0.000 0.076 0.000
#> DRR006475 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006476 5 0.2933 0.703 0.000 0.200 0.000 0.000 0.796 0.004
#> DRR006477 5 0.2190 0.801 0.000 0.040 0.060 0.000 0.900 0.000
#> DRR006478 6 0.0146 0.867 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006479 3 0.1297 0.800 0.000 0.000 0.948 0.012 0.040 0.000
#> DRR006480 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006481 5 0.1908 0.744 0.000 0.000 0.096 0.004 0.900 0.000
#> DRR006482 5 0.2219 0.703 0.000 0.000 0.000 0.000 0.864 0.136
#> DRR006483 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006484 3 0.1584 0.823 0.000 0.000 0.928 0.008 0.064 0.000
#> DRR006485 5 0.1152 0.828 0.000 0.044 0.004 0.000 0.952 0.000
#> DRR006486 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006487 3 0.0260 0.818 0.000 0.000 0.992 0.000 0.008 0.000
#> DRR006488 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006489 1 0.2838 0.837 0.808 0.000 0.000 0.188 0.004 0.000
#> DRR006490 3 0.0291 0.815 0.000 0.000 0.992 0.004 0.004 0.000
#> DRR006491 3 0.0000 0.816 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006492 6 0.3037 0.707 0.004 0.000 0.160 0.000 0.016 0.820
#> DRR006493 3 0.3050 0.772 0.000 0.000 0.764 0.000 0.236 0.000
#> DRR006494 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.947 0.951 0.979 0.3197 0.690 0.690
#> 3 3 0.741 0.836 0.918 0.9758 0.657 0.507
#> 4 4 0.653 0.566 0.778 0.1434 0.901 0.728
#> 5 5 0.619 0.578 0.717 0.0498 0.829 0.503
#> 6 6 0.650 0.556 0.733 0.0431 0.880 0.562
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
#> DRR006374 1 0.595 0.825 0.856 0.144
#> DRR006375 1 0.000 0.981 1.000 0.000
#> DRR006376 1 0.000 0.981 1.000 0.000
#> DRR006377 1 0.000 0.981 1.000 0.000
#> DRR006378 2 0.000 0.960 0.000 1.000
#> DRR006379 1 0.000 0.981 1.000 0.000
#> DRR006380 1 0.000 0.981 1.000 0.000
#> DRR006381 1 0.000 0.981 1.000 0.000
#> DRR006382 1 0.000 0.981 1.000 0.000
#> DRR006383 1 0.000 0.981 1.000 0.000
#> DRR006384 2 0.855 0.643 0.280 0.720
#> DRR006385 1 0.000 0.981 1.000 0.000
#> DRR006386 2 0.000 0.960 0.000 1.000
#> DRR006387 1 0.000 0.981 1.000 0.000
#> DRR006388 1 0.443 0.894 0.908 0.092
#> DRR006389 1 0.443 0.894 0.908 0.092
#> DRR006390 2 0.000 0.960 0.000 1.000
#> DRR006391 2 0.000 0.960 0.000 1.000
#> DRR006392 1 0.000 0.981 1.000 0.000
#> DRR006393 1 0.000 0.981 1.000 0.000
#> DRR006394 2 0.000 0.960 0.000 1.000
#> DRR006395 1 0.000 0.981 1.000 0.000
#> DRR006396 1 0.000 0.981 1.000 0.000
#> DRR006397 1 0.416 0.902 0.916 0.084
#> DRR006398 1 0.416 0.902 0.916 0.084
#> DRR006399 1 0.000 0.981 1.000 0.000
#> DRR006400 1 0.000 0.981 1.000 0.000
#> DRR006401 2 0.000 0.960 0.000 1.000
#> DRR006402 2 0.000 0.960 0.000 1.000
#> DRR006403 1 0.000 0.981 1.000 0.000
#> DRR006404 1 0.000 0.981 1.000 0.000
#> DRR006405 1 0.000 0.981 1.000 0.000
#> DRR006406 1 0.000 0.981 1.000 0.000
#> DRR006407 1 0.118 0.968 0.984 0.016
#> DRR006408 1 0.224 0.951 0.964 0.036
#> DRR006409 1 0.000 0.981 1.000 0.000
#> DRR006410 1 0.000 0.981 1.000 0.000
#> DRR006411 1 0.000 0.981 1.000 0.000
#> DRR006412 2 0.000 0.960 0.000 1.000
#> DRR006413 1 0.000 0.981 1.000 0.000
#> DRR006414 1 0.000 0.981 1.000 0.000
#> DRR006415 1 0.000 0.981 1.000 0.000
#> DRR006416 1 0.416 0.902 0.916 0.084
#> DRR006417 1 0.000 0.981 1.000 0.000
#> DRR006418 1 0.000 0.981 1.000 0.000
#> DRR006419 1 0.000 0.981 1.000 0.000
#> DRR006420 1 0.000 0.981 1.000 0.000
#> DRR006421 1 0.000 0.981 1.000 0.000
#> DRR006422 1 0.000 0.981 1.000 0.000
#> DRR006423 2 0.000 0.960 0.000 1.000
#> DRR006424 1 0.000 0.981 1.000 0.000
#> DRR006425 1 0.943 0.438 0.640 0.360
#> DRR006426 1 0.000 0.981 1.000 0.000
#> DRR006427 2 0.000 0.960 0.000 1.000
#> DRR006428 1 0.000 0.981 1.000 0.000
#> DRR006429 1 0.260 0.942 0.956 0.044
#> DRR006430 1 0.000 0.981 1.000 0.000
#> DRR006431 1 0.000 0.981 1.000 0.000
#> DRR006432 1 0.000 0.981 1.000 0.000
#> DRR006433 1 0.000 0.981 1.000 0.000
#> DRR006434 1 0.605 0.819 0.852 0.148
#> DRR006435 2 0.000 0.960 0.000 1.000
#> DRR006436 2 0.000 0.960 0.000 1.000
#> DRR006437 1 0.000 0.981 1.000 0.000
#> DRR006438 1 0.000 0.981 1.000 0.000
#> DRR006439 1 0.000 0.981 1.000 0.000
#> DRR006440 1 0.000 0.981 1.000 0.000
#> DRR006441 2 0.000 0.960 0.000 1.000
#> DRR006442 1 0.000 0.981 1.000 0.000
#> DRR006443 1 0.000 0.981 1.000 0.000
#> DRR006444 2 0.000 0.960 0.000 1.000
#> DRR006445 1 0.000 0.981 1.000 0.000
#> DRR006446 2 0.000 0.960 0.000 1.000
#> DRR006447 1 0.000 0.981 1.000 0.000
#> DRR006448 1 0.000 0.981 1.000 0.000
#> DRR006449 1 0.000 0.981 1.000 0.000
#> DRR006450 1 0.000 0.981 1.000 0.000
#> DRR006451 1 0.000 0.981 1.000 0.000
#> DRR006452 1 0.000 0.981 1.000 0.000
#> DRR006453 1 0.000 0.981 1.000 0.000
#> DRR006454 2 0.802 0.703 0.244 0.756
#> DRR006455 1 0.494 0.871 0.892 0.108
#> DRR006456 1 0.000 0.981 1.000 0.000
#> DRR006457 1 0.000 0.981 1.000 0.000
#> DRR006458 1 0.000 0.981 1.000 0.000
#> DRR006459 1 0.000 0.981 1.000 0.000
#> DRR006460 2 0.730 0.764 0.204 0.796
#> DRR006461 1 0.000 0.981 1.000 0.000
#> DRR006462 1 0.000 0.981 1.000 0.000
#> DRR006463 1 0.000 0.981 1.000 0.000
#> DRR006464 1 0.991 0.191 0.556 0.444
#> DRR006465 1 0.000 0.981 1.000 0.000
#> DRR006466 1 0.000 0.981 1.000 0.000
#> DRR006467 1 0.000 0.981 1.000 0.000
#> DRR006468 2 0.000 0.960 0.000 1.000
#> DRR006469 2 0.000 0.960 0.000 1.000
#> DRR006470 1 0.000 0.981 1.000 0.000
#> DRR006471 1 0.000 0.981 1.000 0.000
#> DRR006472 1 0.000 0.981 1.000 0.000
#> DRR006473 2 0.000 0.960 0.000 1.000
#> DRR006474 2 0.574 0.844 0.136 0.864
#> DRR006475 1 0.000 0.981 1.000 0.000
#> DRR006476 1 0.000 0.981 1.000 0.000
#> DRR006477 1 0.000 0.981 1.000 0.000
#> DRR006478 1 0.000 0.981 1.000 0.000
#> DRR006479 1 0.000 0.981 1.000 0.000
#> DRR006480 1 0.000 0.981 1.000 0.000
#> DRR006481 1 0.000 0.981 1.000 0.000
#> DRR006482 1 0.000 0.981 1.000 0.000
#> DRR006483 1 0.000 0.981 1.000 0.000
#> DRR006484 1 0.000 0.981 1.000 0.000
#> DRR006485 1 0.000 0.981 1.000 0.000
#> DRR006486 1 0.000 0.981 1.000 0.000
#> DRR006487 1 0.000 0.981 1.000 0.000
#> DRR006488 2 0.000 0.960 0.000 1.000
#> DRR006489 1 0.000 0.981 1.000 0.000
#> DRR006490 1 0.000 0.981 1.000 0.000
#> DRR006491 1 0.000 0.981 1.000 0.000
#> DRR006492 1 0.000 0.981 1.000 0.000
#> DRR006493 1 0.000 0.981 1.000 0.000
#> DRR006494 1 0.000 0.981 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 3 0.4748 0.801 0.024 0.144 0.832
#> DRR006375 1 0.0892 0.879 0.980 0.000 0.020
#> DRR006376 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006377 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006378 2 0.0237 0.952 0.004 0.996 0.000
#> DRR006379 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006380 3 0.7176 0.617 0.068 0.248 0.684
#> DRR006381 1 0.0747 0.873 0.984 0.000 0.016
#> DRR006382 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006383 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006384 2 0.4473 0.771 0.008 0.828 0.164
#> DRR006385 1 0.0424 0.874 0.992 0.000 0.008
#> DRR006386 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006387 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006388 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006389 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006390 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006391 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006392 1 0.0892 0.879 0.980 0.000 0.020
#> DRR006393 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006394 2 0.0237 0.952 0.004 0.996 0.000
#> DRR006395 1 0.4654 0.752 0.792 0.000 0.208
#> DRR006396 1 0.0892 0.879 0.980 0.000 0.020
#> DRR006397 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006398 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006399 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006400 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006401 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006403 1 0.0892 0.878 0.980 0.000 0.020
#> DRR006404 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006405 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006406 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006407 1 0.1337 0.875 0.972 0.012 0.016
#> DRR006408 1 0.6161 0.607 0.708 0.272 0.020
#> DRR006409 3 0.5138 0.619 0.252 0.000 0.748
#> DRR006410 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006411 1 0.5810 0.576 0.664 0.000 0.336
#> DRR006412 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006413 1 0.5905 0.548 0.648 0.000 0.352
#> DRR006414 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006415 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006416 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006417 3 0.1289 0.927 0.032 0.000 0.968
#> DRR006418 1 0.4121 0.778 0.832 0.000 0.168
#> DRR006419 3 0.1289 0.927 0.032 0.000 0.968
#> DRR006420 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006421 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006422 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006423 2 0.0237 0.952 0.004 0.996 0.000
#> DRR006424 1 0.0424 0.874 0.992 0.000 0.008
#> DRR006425 2 0.3207 0.873 0.084 0.904 0.012
#> DRR006426 1 0.5948 0.551 0.640 0.000 0.360
#> DRR006427 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006428 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006429 2 0.9355 0.313 0.252 0.516 0.232
#> DRR006430 1 0.0892 0.879 0.980 0.000 0.020
#> DRR006431 1 0.0892 0.879 0.980 0.000 0.020
#> DRR006432 1 0.5968 0.523 0.636 0.000 0.364
#> DRR006433 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006434 3 0.1620 0.925 0.024 0.012 0.964
#> DRR006435 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006437 1 0.5363 0.664 0.724 0.000 0.276
#> DRR006438 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006439 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006440 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006441 2 0.0237 0.952 0.004 0.996 0.000
#> DRR006442 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006443 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006444 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006445 1 0.0424 0.877 0.992 0.000 0.008
#> DRR006446 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006447 1 0.3752 0.796 0.856 0.000 0.144
#> DRR006448 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006449 1 0.0892 0.879 0.980 0.000 0.020
#> DRR006450 1 0.4702 0.736 0.788 0.000 0.212
#> DRR006451 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006452 1 0.0424 0.874 0.992 0.000 0.008
#> DRR006453 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006454 2 0.3888 0.868 0.048 0.888 0.064
#> DRR006455 3 0.7104 0.439 0.032 0.360 0.608
#> DRR006456 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006457 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006458 1 0.5785 0.602 0.668 0.000 0.332
#> DRR006459 1 0.5968 0.547 0.636 0.000 0.364
#> DRR006460 2 0.3965 0.818 0.008 0.860 0.132
#> DRR006461 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006462 1 0.2711 0.844 0.912 0.000 0.088
#> DRR006463 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006464 1 0.6735 0.336 0.564 0.424 0.012
#> DRR006465 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006466 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006467 1 0.5905 0.548 0.648 0.000 0.352
#> DRR006468 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006469 2 0.0237 0.952 0.004 0.996 0.000
#> DRR006470 3 0.1529 0.924 0.040 0.000 0.960
#> DRR006471 1 0.5905 0.548 0.648 0.000 0.352
#> DRR006472 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006473 2 0.0237 0.952 0.004 0.996 0.000
#> DRR006474 2 0.1525 0.929 0.004 0.964 0.032
#> DRR006475 1 0.6126 0.468 0.600 0.000 0.400
#> DRR006476 3 0.7175 0.266 0.376 0.032 0.592
#> DRR006477 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006478 1 0.0592 0.879 0.988 0.000 0.012
#> DRR006479 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006480 3 0.6111 0.238 0.396 0.000 0.604
#> DRR006481 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006482 1 0.5835 0.589 0.660 0.000 0.340
#> DRR006483 1 0.5560 0.647 0.700 0.000 0.300
#> DRR006484 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006485 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006486 3 0.1529 0.924 0.040 0.000 0.960
#> DRR006487 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006488 2 0.0000 0.953 0.000 1.000 0.000
#> DRR006489 1 0.0424 0.874 0.992 0.000 0.008
#> DRR006490 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006491 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006492 3 0.0892 0.932 0.020 0.000 0.980
#> DRR006493 3 0.0000 0.927 0.000 0.000 1.000
#> DRR006494 3 0.4702 0.695 0.212 0.000 0.788
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 3 0.6042 0.3697 0.052 0.368 0.580 0.000
#> DRR006375 4 0.4134 0.4206 0.260 0.000 0.000 0.740
#> DRR006376 4 0.3873 0.5191 0.228 0.000 0.000 0.772
#> DRR006377 4 0.6200 0.5178 0.444 0.000 0.052 0.504
#> DRR006378 2 0.1389 0.9529 0.000 0.952 0.048 0.000
#> DRR006379 4 0.3873 0.5191 0.228 0.000 0.000 0.772
#> DRR006380 3 0.6209 0.1072 0.052 0.456 0.492 0.000
#> DRR006381 4 0.4356 0.3908 0.292 0.000 0.000 0.708
#> DRR006382 3 0.5136 0.6350 0.048 0.224 0.728 0.000
#> DRR006383 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> DRR006384 2 0.3991 0.8601 0.048 0.832 0.120 0.000
#> DRR006385 1 0.4999 -0.5298 0.508 0.000 0.000 0.492
#> DRR006386 2 0.0000 0.9499 0.000 1.000 0.000 0.000
#> DRR006387 4 0.3837 0.5203 0.224 0.000 0.000 0.776
#> DRR006388 4 0.5277 0.5464 0.460 0.008 0.000 0.532
#> DRR006389 4 0.5277 0.5464 0.460 0.008 0.000 0.532
#> DRR006390 2 0.0000 0.9499 0.000 1.000 0.000 0.000
#> DRR006391 2 0.0000 0.9499 0.000 1.000 0.000 0.000
#> DRR006392 4 0.4134 0.4206 0.260 0.000 0.000 0.740
#> DRR006393 4 0.4948 0.5547 0.440 0.000 0.000 0.560
#> DRR006394 2 0.1389 0.9529 0.000 0.952 0.048 0.000
#> DRR006395 1 0.6327 -0.4404 0.496 0.000 0.060 0.444
#> DRR006396 4 0.4996 0.5057 0.484 0.000 0.000 0.516
#> DRR006397 4 0.5277 0.5464 0.460 0.008 0.000 0.532
#> DRR006398 4 0.5277 0.5464 0.460 0.008 0.000 0.532
#> DRR006399 4 0.3837 0.5203 0.224 0.000 0.000 0.776
#> DRR006400 4 0.3837 0.5203 0.224 0.000 0.000 0.776
#> DRR006401 2 0.0895 0.9541 0.004 0.976 0.020 0.000
#> DRR006402 2 0.1004 0.9544 0.004 0.972 0.024 0.000
#> DRR006403 4 0.4164 0.5059 0.264 0.000 0.000 0.736
#> DRR006404 4 0.3873 0.5191 0.228 0.000 0.000 0.772
#> DRR006405 4 0.3907 0.5213 0.232 0.000 0.000 0.768
#> DRR006406 4 0.3907 0.5213 0.232 0.000 0.000 0.768
#> DRR006407 4 0.8896 0.1871 0.260 0.232 0.068 0.440
#> DRR006408 4 0.9098 0.0589 0.228 0.344 0.072 0.356
#> DRR006409 3 0.2775 0.7553 0.084 0.000 0.896 0.020
#> DRR006410 4 0.3837 0.5203 0.224 0.000 0.000 0.776
#> DRR006411 1 0.5072 0.3677 0.740 0.208 0.052 0.000
#> DRR006412 2 0.0592 0.9532 0.000 0.984 0.016 0.000
#> DRR006413 1 0.5170 0.4022 0.724 0.000 0.048 0.228
#> DRR006414 3 0.1302 0.7815 0.044 0.000 0.956 0.000
#> DRR006415 3 0.1557 0.7799 0.056 0.000 0.944 0.000
#> DRR006416 4 0.5281 0.5433 0.464 0.008 0.000 0.528
#> DRR006417 3 0.4999 0.2895 0.492 0.000 0.508 0.000
#> DRR006418 1 0.2089 0.4323 0.932 0.000 0.048 0.020
#> DRR006419 3 0.4999 0.2895 0.492 0.000 0.508 0.000
#> DRR006420 3 0.1302 0.7815 0.044 0.000 0.956 0.000
#> DRR006421 3 0.1635 0.7741 0.044 0.008 0.948 0.000
#> DRR006422 4 0.4967 0.5520 0.452 0.000 0.000 0.548
#> DRR006423 2 0.1389 0.9529 0.000 0.952 0.048 0.000
#> DRR006424 4 0.4277 0.4074 0.280 0.000 0.000 0.720
#> DRR006425 2 0.4694 0.8550 0.044 0.824 0.048 0.084
#> DRR006426 1 0.7109 -0.3298 0.520 0.000 0.144 0.336
#> DRR006427 2 0.0000 0.9499 0.000 1.000 0.000 0.000
#> DRR006428 3 0.2011 0.7704 0.080 0.000 0.920 0.000
#> DRR006429 2 0.4024 0.8996 0.048 0.856 0.072 0.024
#> DRR006430 4 0.4164 0.4170 0.264 0.000 0.000 0.736
#> DRR006431 4 0.4193 0.4124 0.268 0.000 0.000 0.732
#> DRR006432 1 0.1389 0.4460 0.952 0.000 0.048 0.000
#> DRR006433 3 0.1452 0.7840 0.008 0.036 0.956 0.000
#> DRR006434 3 0.5136 0.6350 0.048 0.224 0.728 0.000
#> DRR006435 2 0.0000 0.9499 0.000 1.000 0.000 0.000
#> DRR006436 2 0.0000 0.9499 0.000 1.000 0.000 0.000
#> DRR006437 1 0.1854 0.4394 0.940 0.000 0.048 0.012
#> DRR006438 3 0.2011 0.7704 0.080 0.000 0.920 0.000
#> DRR006439 3 0.1716 0.7759 0.064 0.000 0.936 0.000
#> DRR006440 3 0.5988 0.6685 0.100 0.224 0.676 0.000
#> DRR006441 2 0.1389 0.9529 0.000 0.952 0.048 0.000
#> DRR006442 3 0.0188 0.7898 0.004 0.000 0.996 0.000
#> DRR006443 3 0.5136 0.6350 0.048 0.224 0.728 0.000
#> DRR006444 2 0.0000 0.9499 0.000 1.000 0.000 0.000
#> DRR006445 4 0.4977 0.5477 0.460 0.000 0.000 0.540
#> DRR006446 2 0.1302 0.9534 0.000 0.956 0.044 0.000
#> DRR006447 1 0.1489 0.4439 0.952 0.000 0.044 0.004
#> DRR006448 4 0.3873 0.5191 0.228 0.000 0.000 0.772
#> DRR006449 1 0.5000 -0.5336 0.504 0.000 0.000 0.496
#> DRR006450 1 0.1389 0.4460 0.952 0.000 0.048 0.000
#> DRR006451 4 0.4955 0.5539 0.444 0.000 0.000 0.556
#> DRR006452 4 0.4356 0.4158 0.292 0.000 0.000 0.708
#> DRR006453 4 0.4972 0.5487 0.456 0.000 0.000 0.544
#> DRR006454 2 0.3487 0.9172 0.040 0.880 0.064 0.016
#> DRR006455 1 0.6211 -0.2588 0.488 0.460 0.052 0.000
#> DRR006456 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> DRR006457 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> DRR006458 4 0.5623 0.2842 0.292 0.000 0.048 0.660
#> DRR006459 4 0.5839 0.2640 0.292 0.000 0.060 0.648
#> DRR006460 2 0.3485 0.9083 0.048 0.872 0.076 0.004
#> DRR006461 3 0.5136 0.6350 0.048 0.224 0.728 0.000
#> DRR006462 1 0.5408 -0.4002 0.576 0.000 0.016 0.408
#> DRR006463 3 0.5136 0.6350 0.048 0.224 0.728 0.000
#> DRR006464 2 0.2089 0.9443 0.020 0.932 0.048 0.000
#> DRR006465 4 0.4948 0.5547 0.440 0.000 0.000 0.560
#> DRR006466 3 0.5136 0.6350 0.048 0.224 0.728 0.000
#> DRR006467 1 0.5170 0.4022 0.724 0.000 0.048 0.228
#> DRR006468 2 0.0000 0.9499 0.000 1.000 0.000 0.000
#> DRR006469 2 0.1389 0.9529 0.000 0.952 0.048 0.000
#> DRR006470 3 0.5000 0.2816 0.496 0.000 0.504 0.000
#> DRR006471 1 0.5170 0.4022 0.724 0.000 0.048 0.228
#> DRR006472 3 0.2011 0.7532 0.080 0.000 0.920 0.000
#> DRR006473 2 0.1722 0.9507 0.008 0.944 0.048 0.000
#> DRR006474 2 0.3081 0.9198 0.048 0.888 0.064 0.000
#> DRR006475 4 0.7905 -0.1616 0.312 0.000 0.320 0.368
#> DRR006476 3 0.6563 0.1144 0.056 0.448 0.488 0.008
#> DRR006477 3 0.0524 0.7905 0.004 0.008 0.988 0.000
#> DRR006478 4 0.4967 0.5511 0.452 0.000 0.000 0.548
#> DRR006479 3 0.2011 0.7704 0.080 0.000 0.920 0.000
#> DRR006480 3 0.7659 0.1167 0.296 0.000 0.460 0.244
#> DRR006481 3 0.0188 0.7896 0.004 0.000 0.996 0.000
#> DRR006482 1 0.6332 -0.3886 0.532 0.000 0.064 0.404
#> DRR006483 4 0.5848 0.2111 0.336 0.000 0.048 0.616
#> DRR006484 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> DRR006485 3 0.5136 0.6350 0.048 0.224 0.728 0.000
#> DRR006486 3 0.7117 0.4924 0.208 0.000 0.564 0.228
#> DRR006487 3 0.1637 0.7787 0.060 0.000 0.940 0.000
#> DRR006488 2 0.0000 0.9499 0.000 1.000 0.000 0.000
#> DRR006489 4 0.4277 0.4074 0.280 0.000 0.000 0.720
#> DRR006490 3 0.2011 0.7704 0.080 0.000 0.920 0.000
#> DRR006491 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> DRR006492 3 0.1978 0.7726 0.068 0.000 0.928 0.004
#> DRR006493 3 0.0000 0.7897 0.000 0.000 1.000 0.000
#> DRR006494 3 0.5910 0.5954 0.084 0.000 0.672 0.244
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.4822 0.1555 0.000 0.564 0.416 0.004 NA
#> DRR006375 4 0.5650 -0.2730 0.456 0.000 0.000 0.468 NA
#> DRR006376 4 0.2377 0.6928 0.000 0.000 0.000 0.872 NA
#> DRR006377 4 0.2248 0.7086 0.088 0.000 0.012 0.900 NA
#> DRR006378 2 0.0727 0.8486 0.004 0.980 0.012 0.004 NA
#> DRR006379 4 0.1908 0.7037 0.000 0.000 0.000 0.908 NA
#> DRR006380 2 0.4747 0.2743 0.012 0.604 0.376 0.008 NA
#> DRR006381 1 0.5831 0.2768 0.496 0.000 0.000 0.408 NA
#> DRR006382 3 0.4592 0.5036 0.000 0.332 0.644 0.000 NA
#> DRR006383 3 0.0000 0.8088 0.000 0.000 1.000 0.000 NA
#> DRR006384 2 0.1492 0.8327 0.000 0.948 0.040 0.004 NA
#> DRR006385 4 0.3995 0.6757 0.152 0.000 0.000 0.788 NA
#> DRR006386 2 0.2471 0.8024 0.000 0.864 0.000 0.000 NA
#> DRR006387 4 0.0162 0.7106 0.004 0.000 0.000 0.996 NA
#> DRR006388 4 0.5739 0.5595 0.100 0.000 0.000 0.556 NA
#> DRR006389 4 0.5739 0.5595 0.100 0.000 0.000 0.556 NA
#> DRR006390 2 0.2471 0.8024 0.000 0.864 0.000 0.000 NA
#> DRR006391 2 0.2471 0.8024 0.000 0.864 0.000 0.000 NA
#> DRR006392 1 0.5650 0.2451 0.468 0.000 0.000 0.456 NA
#> DRR006393 4 0.2628 0.7031 0.088 0.000 0.000 0.884 NA
#> DRR006394 2 0.0566 0.8491 0.000 0.984 0.012 0.004 NA
#> DRR006395 4 0.3463 0.6755 0.088 0.000 0.040 0.852 NA
#> DRR006396 4 0.4072 0.6333 0.108 0.000 0.000 0.792 NA
#> DRR006397 4 0.5696 0.5637 0.096 0.000 0.000 0.560 NA
#> DRR006398 4 0.5696 0.5637 0.096 0.000 0.000 0.560 NA
#> DRR006399 4 0.0000 0.7090 0.000 0.000 0.000 1.000 NA
#> DRR006400 4 0.0000 0.7090 0.000 0.000 0.000 1.000 NA
#> DRR006401 2 0.0854 0.8483 0.000 0.976 0.012 0.004 NA
#> DRR006402 2 0.0854 0.8483 0.000 0.976 0.012 0.004 NA
#> DRR006403 4 0.0451 0.7075 0.004 0.000 0.000 0.988 NA
#> DRR006404 4 0.0000 0.7090 0.000 0.000 0.000 1.000 NA
#> DRR006405 4 0.2674 0.6910 0.004 0.000 0.000 0.856 NA
#> DRR006406 4 0.2516 0.6881 0.000 0.000 0.000 0.860 NA
#> DRR006407 2 0.5588 0.2668 0.040 0.560 0.020 0.380 NA
#> DRR006408 2 0.4125 0.6624 0.016 0.776 0.024 0.184 NA
#> DRR006409 3 0.6726 0.1195 0.056 0.000 0.564 0.268 NA
#> DRR006410 4 0.0162 0.7106 0.004 0.000 0.000 0.996 NA
#> DRR006411 1 0.6454 0.2458 0.620 0.188 0.036 0.152 NA
#> DRR006412 2 0.0162 0.8456 0.000 0.996 0.000 0.000 NA
#> DRR006413 1 0.0798 0.4340 0.976 0.000 0.000 0.008 NA
#> DRR006414 3 0.0404 0.8047 0.012 0.000 0.988 0.000 NA
#> DRR006415 3 0.0794 0.8001 0.028 0.000 0.972 0.000 NA
#> DRR006416 4 0.5452 0.6597 0.144 0.000 0.000 0.656 NA
#> DRR006417 1 0.6530 0.0339 0.488 0.000 0.320 0.004 NA
#> DRR006418 1 0.5651 0.1319 0.616 0.000 0.008 0.288 NA
#> DRR006419 1 0.6552 0.0758 0.496 0.000 0.296 0.004 NA
#> DRR006420 3 0.0566 0.8064 0.012 0.000 0.984 0.004 NA
#> DRR006421 3 0.2169 0.7874 0.008 0.048 0.924 0.008 NA
#> DRR006422 4 0.4394 0.7148 0.100 0.000 0.000 0.764 NA
#> DRR006423 2 0.0566 0.8491 0.000 0.984 0.012 0.004 NA
#> DRR006424 1 0.5761 0.2684 0.492 0.000 0.000 0.420 NA
#> DRR006425 2 0.1016 0.8481 0.004 0.972 0.012 0.004 NA
#> DRR006426 4 0.7742 0.0347 0.272 0.008 0.332 0.352 NA
#> DRR006427 2 0.2471 0.8024 0.000 0.864 0.000 0.000 NA
#> DRR006428 3 0.3109 0.6505 0.200 0.000 0.800 0.000 NA
#> DRR006429 2 0.0833 0.8479 0.004 0.976 0.016 0.004 NA
#> DRR006430 1 0.5650 0.2371 0.464 0.000 0.000 0.460 NA
#> DRR006431 1 0.5650 0.2451 0.468 0.000 0.000 0.456 NA
#> DRR006432 1 0.6357 0.3015 0.628 0.000 0.056 0.112 NA
#> DRR006433 3 0.0671 0.8053 0.016 0.000 0.980 0.004 NA
#> DRR006434 3 0.4915 0.3055 0.000 0.420 0.556 0.004 NA
#> DRR006435 2 0.3752 0.7250 0.000 0.708 0.000 0.000 NA
#> DRR006436 2 0.3752 0.7250 0.000 0.708 0.000 0.000 NA
#> DRR006437 1 0.5774 -0.0190 0.548 0.000 0.020 0.380 NA
#> DRR006438 3 0.3109 0.6505 0.200 0.000 0.800 0.000 NA
#> DRR006439 3 0.0703 0.8041 0.024 0.000 0.976 0.000 NA
#> DRR006440 3 0.7011 0.3293 0.208 0.332 0.444 0.004 NA
#> DRR006441 2 0.0566 0.8491 0.000 0.984 0.012 0.004 NA
#> DRR006442 3 0.0000 0.8088 0.000 0.000 1.000 0.000 NA
#> DRR006443 3 0.4592 0.5036 0.000 0.332 0.644 0.000 NA
#> DRR006444 2 0.3752 0.7250 0.000 0.708 0.000 0.000 NA
#> DRR006445 4 0.4879 0.6962 0.108 0.000 0.000 0.716 NA
#> DRR006446 2 0.0324 0.8482 0.000 0.992 0.004 0.004 NA
#> DRR006447 1 0.5431 0.0363 0.584 0.000 0.008 0.356 NA
#> DRR006448 4 0.0290 0.7059 0.000 0.000 0.000 0.992 NA
#> DRR006449 4 0.3569 0.6945 0.104 0.000 0.000 0.828 NA
#> DRR006450 1 0.5603 0.0354 0.572 0.000 0.008 0.356 NA
#> DRR006451 4 0.4322 0.7130 0.088 0.000 0.000 0.768 NA
#> DRR006452 4 0.5771 -0.1734 0.432 0.000 0.000 0.480 NA
#> DRR006453 4 0.4704 0.7037 0.112 0.000 0.000 0.736 NA
#> DRR006454 2 0.0727 0.8488 0.004 0.980 0.012 0.004 NA
#> DRR006455 2 0.5036 0.4577 0.372 0.592 0.032 0.004 NA
#> DRR006456 3 0.0000 0.8088 0.000 0.000 1.000 0.000 NA
#> DRR006457 3 0.0000 0.8088 0.000 0.000 1.000 0.000 NA
#> DRR006458 1 0.6229 0.3149 0.468 0.000 0.004 0.404 NA
#> DRR006459 1 0.6229 0.3149 0.468 0.000 0.004 0.404 NA
#> DRR006460 2 0.0854 0.8483 0.000 0.976 0.012 0.004 NA
#> DRR006461 3 0.4592 0.5036 0.000 0.332 0.644 0.000 NA
#> DRR006462 4 0.4823 0.5043 0.316 0.000 0.000 0.644 NA
#> DRR006463 3 0.4592 0.5036 0.000 0.332 0.644 0.000 NA
#> DRR006464 2 0.1200 0.8427 0.016 0.964 0.012 0.008 NA
#> DRR006465 4 0.2628 0.7031 0.088 0.000 0.000 0.884 NA
#> DRR006466 3 0.4742 0.5019 0.012 0.332 0.644 0.008 NA
#> DRR006467 1 0.2777 0.4319 0.864 0.000 0.000 0.120 NA
#> DRR006468 2 0.3752 0.7250 0.000 0.708 0.000 0.000 NA
#> DRR006469 2 0.0566 0.8491 0.000 0.984 0.012 0.004 NA
#> DRR006470 1 0.6516 0.1303 0.512 0.000 0.268 0.004 NA
#> DRR006471 1 0.2293 0.4366 0.900 0.000 0.000 0.084 NA
#> DRR006472 3 0.2151 0.7763 0.016 0.000 0.924 0.040 NA
#> DRR006473 2 0.0566 0.8491 0.000 0.984 0.012 0.004 NA
#> DRR006474 2 0.0854 0.8483 0.000 0.976 0.012 0.004 NA
#> DRR006475 1 0.7188 0.3747 0.500 0.000 0.072 0.304 NA
#> DRR006476 2 0.4805 0.3377 0.012 0.628 0.348 0.004 NA
#> DRR006477 3 0.0671 0.8053 0.016 0.000 0.980 0.004 NA
#> DRR006478 4 0.3146 0.7101 0.092 0.000 0.000 0.856 NA
#> DRR006479 3 0.3039 0.6598 0.192 0.000 0.808 0.000 NA
#> DRR006480 1 0.7586 0.3778 0.480 0.000 0.120 0.276 NA
#> DRR006481 3 0.0000 0.8088 0.000 0.000 1.000 0.000 NA
#> DRR006482 4 0.7291 0.2923 0.320 0.020 0.128 0.496 NA
#> DRR006483 1 0.5323 0.3761 0.624 0.000 0.000 0.296 NA
#> DRR006484 3 0.0000 0.8088 0.000 0.000 1.000 0.000 NA
#> DRR006485 3 0.4592 0.5036 0.000 0.332 0.644 0.000 NA
#> DRR006486 1 0.3718 0.3471 0.784 0.000 0.196 0.004 NA
#> DRR006487 3 0.0880 0.7985 0.032 0.000 0.968 0.000 NA
#> DRR006488 2 0.3752 0.7250 0.000 0.708 0.000 0.000 NA
#> DRR006489 1 0.5757 0.2771 0.496 0.000 0.000 0.416 NA
#> DRR006490 3 0.0880 0.7984 0.032 0.000 0.968 0.000 NA
#> DRR006491 3 0.0000 0.8088 0.000 0.000 1.000 0.000 NA
#> DRR006492 3 0.3077 0.6875 0.028 0.000 0.864 0.100 NA
#> DRR006493 3 0.0000 0.8088 0.000 0.000 1.000 0.000 NA
#> DRR006494 1 0.7762 0.3749 0.460 0.000 0.144 0.272 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.3050 0.6831 0.000 0.832 0.136 0.000 0.028 0.004
#> DRR006375 1 0.0520 0.5977 0.984 0.000 0.000 0.008 0.008 0.000
#> DRR006376 4 0.4172 0.5880 0.280 0.000 0.000 0.680 0.000 0.040
#> DRR006377 4 0.5512 0.4903 0.400 0.088 0.004 0.500 0.000 0.008
#> DRR006378 2 0.0632 0.8278 0.000 0.976 0.000 0.000 0.024 0.000
#> DRR006379 4 0.4332 0.5859 0.316 0.000 0.000 0.644 0.000 0.040
#> DRR006380 2 0.3534 0.6002 0.004 0.772 0.200 0.000 0.024 0.000
#> DRR006381 1 0.2151 0.5934 0.904 0.000 0.000 0.016 0.008 0.072
#> DRR006382 3 0.4861 0.4324 0.000 0.368 0.572 0.000 0.056 0.004
#> DRR006383 3 0.0405 0.7672 0.000 0.004 0.988 0.000 0.008 0.000
#> DRR006384 2 0.0405 0.8272 0.000 0.988 0.004 0.000 0.008 0.000
#> DRR006385 1 0.5642 -0.2083 0.460 0.000 0.000 0.388 0.000 0.152
#> DRR006386 5 0.3810 0.7775 0.000 0.428 0.000 0.000 0.572 0.000
#> DRR006387 4 0.3930 0.5293 0.420 0.000 0.000 0.576 0.000 0.004
#> DRR006388 4 0.4536 0.3459 0.044 0.000 0.000 0.752 0.080 0.124
#> DRR006389 4 0.4536 0.3459 0.044 0.000 0.000 0.752 0.080 0.124
#> DRR006390 5 0.3817 0.7740 0.000 0.432 0.000 0.000 0.568 0.000
#> DRR006391 5 0.3817 0.7740 0.000 0.432 0.000 0.000 0.568 0.000
#> DRR006392 1 0.0508 0.5964 0.984 0.000 0.000 0.004 0.012 0.000
#> DRR006393 1 0.4672 -0.3127 0.548 0.000 0.000 0.416 0.012 0.024
#> DRR006394 2 0.0632 0.8278 0.000 0.976 0.000 0.000 0.024 0.000
#> DRR006395 1 0.5101 -0.2749 0.556 0.000 0.036 0.384 0.004 0.020
#> DRR006396 1 0.3941 0.1804 0.724 0.000 0.000 0.244 0.008 0.024
#> DRR006397 4 0.4536 0.3459 0.044 0.000 0.000 0.752 0.080 0.124
#> DRR006398 4 0.4536 0.3459 0.044 0.000 0.000 0.752 0.080 0.124
#> DRR006399 4 0.4609 0.5305 0.420 0.000 0.000 0.540 0.000 0.040
#> DRR006400 4 0.4609 0.5305 0.420 0.000 0.000 0.540 0.000 0.040
#> DRR006401 2 0.1007 0.8082 0.000 0.956 0.000 0.000 0.044 0.000
#> DRR006402 2 0.1007 0.8082 0.000 0.956 0.000 0.000 0.044 0.000
#> DRR006403 4 0.4609 0.5305 0.420 0.000 0.000 0.540 0.000 0.040
#> DRR006404 4 0.4609 0.5305 0.420 0.000 0.000 0.540 0.000 0.040
#> DRR006405 4 0.3778 0.5948 0.288 0.000 0.000 0.696 0.000 0.016
#> DRR006406 4 0.3990 0.5923 0.284 0.000 0.000 0.688 0.000 0.028
#> DRR006407 2 0.4407 0.4011 0.232 0.692 0.000 0.076 0.000 0.000
#> DRR006408 2 0.2867 0.6461 0.112 0.848 0.000 0.040 0.000 0.000
#> DRR006409 1 0.4777 0.1340 0.504 0.000 0.460 0.004 0.020 0.012
#> DRR006410 4 0.4238 0.4921 0.444 0.000 0.000 0.540 0.000 0.016
#> DRR006411 6 0.5565 0.7161 0.092 0.056 0.040 0.108 0.000 0.704
#> DRR006412 2 0.3244 0.3545 0.000 0.732 0.000 0.000 0.268 0.000
#> DRR006413 6 0.4067 0.4025 0.444 0.000 0.000 0.008 0.000 0.548
#> DRR006414 3 0.0622 0.7654 0.000 0.000 0.980 0.000 0.008 0.012
#> DRR006415 3 0.1320 0.7595 0.000 0.000 0.948 0.000 0.016 0.036
#> DRR006416 4 0.5543 0.4105 0.292 0.000 0.000 0.576 0.016 0.116
#> DRR006417 6 0.4433 0.6201 0.028 0.000 0.212 0.000 0.040 0.720
#> DRR006418 6 0.3908 0.7132 0.100 0.000 0.000 0.132 0.000 0.768
#> DRR006419 6 0.4405 0.6245 0.028 0.000 0.208 0.000 0.040 0.724
#> DRR006420 3 0.2732 0.7274 0.024 0.008 0.880 0.000 0.012 0.076
#> DRR006421 3 0.4557 0.6509 0.012 0.212 0.720 0.004 0.044 0.008
#> DRR006422 4 0.4804 0.4699 0.416 0.012 0.000 0.540 0.000 0.032
#> DRR006423 2 0.0632 0.8278 0.000 0.976 0.000 0.000 0.024 0.000
#> DRR006424 1 0.1850 0.5982 0.924 0.000 0.000 0.016 0.008 0.052
#> DRR006425 2 0.0458 0.8299 0.000 0.984 0.000 0.000 0.016 0.000
#> DRR006426 6 0.8919 0.2663 0.116 0.136 0.200 0.236 0.012 0.300
#> DRR006427 5 0.3817 0.7740 0.000 0.432 0.000 0.000 0.568 0.000
#> DRR006428 3 0.3046 0.6015 0.000 0.000 0.800 0.000 0.012 0.188
#> DRR006429 2 0.0717 0.8215 0.000 0.976 0.008 0.000 0.016 0.000
#> DRR006430 1 0.0146 0.5991 0.996 0.000 0.004 0.000 0.000 0.000
#> DRR006431 1 0.0653 0.5960 0.980 0.000 0.004 0.004 0.012 0.000
#> DRR006432 6 0.4534 0.7279 0.080 0.000 0.052 0.112 0.000 0.756
#> DRR006433 3 0.3250 0.7297 0.068 0.036 0.860 0.004 0.024 0.008
#> DRR006434 2 0.4159 0.5095 0.000 0.704 0.252 0.000 0.040 0.004
#> DRR006435 5 0.3023 0.8568 0.000 0.232 0.000 0.000 0.768 0.000
#> DRR006436 5 0.3023 0.8568 0.000 0.232 0.000 0.000 0.768 0.000
#> DRR006437 6 0.4183 0.6964 0.108 0.000 0.000 0.152 0.000 0.740
#> DRR006438 3 0.3470 0.5342 0.000 0.000 0.740 0.000 0.012 0.248
#> DRR006439 3 0.1434 0.7617 0.020 0.000 0.948 0.000 0.008 0.024
#> DRR006440 3 0.6575 0.2374 0.000 0.356 0.376 0.000 0.028 0.240
#> DRR006441 2 0.0547 0.8294 0.000 0.980 0.000 0.000 0.020 0.000
#> DRR006442 3 0.0748 0.7670 0.000 0.004 0.976 0.000 0.016 0.004
#> DRR006443 3 0.4930 0.4115 0.000 0.376 0.560 0.000 0.060 0.004
#> DRR006444 5 0.3023 0.8568 0.000 0.232 0.000 0.000 0.768 0.000
#> DRR006445 4 0.4727 0.4402 0.368 0.000 0.000 0.576 0.000 0.056
#> DRR006446 2 0.0937 0.8142 0.000 0.960 0.000 0.000 0.040 0.000
#> DRR006447 6 0.3901 0.7138 0.096 0.000 0.000 0.136 0.000 0.768
#> DRR006448 4 0.4609 0.5305 0.420 0.000 0.000 0.540 0.000 0.040
#> DRR006449 1 0.4624 -0.2879 0.528 0.000 0.000 0.432 0.000 0.040
#> DRR006450 6 0.3985 0.7117 0.100 0.000 0.000 0.140 0.000 0.760
#> DRR006451 4 0.3927 0.5556 0.344 0.000 0.000 0.644 0.000 0.012
#> DRR006452 1 0.2822 0.5724 0.868 0.000 0.000 0.056 0.008 0.068
#> DRR006453 4 0.4682 0.4383 0.396 0.000 0.000 0.556 0.000 0.048
#> DRR006454 2 0.0436 0.8276 0.004 0.988 0.004 0.000 0.004 0.000
#> DRR006455 2 0.4658 0.2840 0.000 0.580 0.040 0.000 0.004 0.376
#> DRR006456 3 0.0748 0.7670 0.000 0.004 0.976 0.000 0.016 0.004
#> DRR006457 3 0.0405 0.7672 0.000 0.004 0.988 0.000 0.008 0.000
#> DRR006458 1 0.0982 0.5942 0.968 0.000 0.004 0.004 0.020 0.004
#> DRR006459 1 0.0982 0.5942 0.968 0.000 0.004 0.004 0.020 0.004
#> DRR006460 2 0.0146 0.8286 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006461 3 0.4912 0.4287 0.000 0.368 0.568 0.000 0.060 0.004
#> DRR006462 1 0.5648 0.1388 0.536 0.000 0.000 0.224 0.000 0.240
#> DRR006463 3 0.4697 0.4441 0.000 0.368 0.584 0.000 0.044 0.004
#> DRR006464 2 0.0725 0.8261 0.012 0.976 0.000 0.000 0.012 0.000
#> DRR006465 1 0.4348 -0.3112 0.560 0.000 0.000 0.416 0.000 0.024
#> DRR006466 3 0.5082 0.3819 0.012 0.396 0.544 0.000 0.044 0.004
#> DRR006467 1 0.3993 -0.1041 0.592 0.000 0.000 0.008 0.000 0.400
#> DRR006468 5 0.3023 0.8568 0.000 0.232 0.000 0.000 0.768 0.000
#> DRR006469 2 0.0547 0.8294 0.000 0.980 0.000 0.000 0.020 0.000
#> DRR006470 6 0.4487 0.6324 0.028 0.000 0.200 0.004 0.040 0.728
#> DRR006471 1 0.4051 -0.2029 0.560 0.000 0.000 0.008 0.000 0.432
#> DRR006472 3 0.3643 0.7232 0.060 0.032 0.848 0.016 0.024 0.020
#> DRR006473 2 0.0547 0.8294 0.000 0.980 0.000 0.000 0.020 0.000
#> DRR006474 2 0.0000 0.8282 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006475 1 0.2395 0.5938 0.896 0.000 0.012 0.004 0.016 0.072
#> DRR006476 2 0.3328 0.6905 0.008 0.832 0.112 0.000 0.044 0.004
#> DRR006477 3 0.2663 0.7338 0.068 0.008 0.888 0.004 0.024 0.008
#> DRR006478 1 0.4389 -0.3619 0.528 0.000 0.000 0.448 0.000 0.024
#> DRR006479 3 0.3298 0.5549 0.000 0.000 0.756 0.000 0.008 0.236
#> DRR006480 1 0.2259 0.5874 0.908 0.000 0.040 0.000 0.020 0.032
#> DRR006481 3 0.0291 0.7678 0.000 0.004 0.992 0.000 0.004 0.000
#> DRR006482 4 0.8726 -0.0887 0.208 0.200 0.076 0.268 0.004 0.244
#> DRR006483 1 0.2445 0.5669 0.868 0.000 0.000 0.004 0.008 0.120
#> DRR006484 3 0.0748 0.7670 0.000 0.004 0.976 0.000 0.016 0.004
#> DRR006485 3 0.4753 0.4385 0.000 0.368 0.580 0.000 0.048 0.004
#> DRR006486 6 0.5123 0.4320 0.408 0.000 0.084 0.000 0.000 0.508
#> DRR006487 3 0.2060 0.7400 0.000 0.000 0.900 0.000 0.016 0.084
#> DRR006488 5 0.3023 0.8568 0.000 0.232 0.000 0.000 0.768 0.000
#> DRR006489 1 0.1577 0.6018 0.940 0.000 0.000 0.016 0.008 0.036
#> DRR006490 3 0.1320 0.7554 0.000 0.000 0.948 0.000 0.016 0.036
#> DRR006491 3 0.0748 0.7670 0.000 0.004 0.976 0.000 0.016 0.004
#> DRR006492 3 0.3620 0.5458 0.200 0.000 0.772 0.004 0.016 0.008
#> DRR006493 3 0.0748 0.7670 0.000 0.004 0.976 0.000 0.016 0.004
#> DRR006494 1 0.2182 0.5640 0.900 0.000 0.076 0.000 0.020 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)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.983 0.992 0.4875 0.514 0.514
#> 3 3 0.488 0.552 0.722 0.3431 0.757 0.553
#> 4 4 0.527 0.551 0.753 0.1336 0.735 0.380
#> 5 5 0.661 0.614 0.788 0.0690 0.817 0.438
#> 6 6 0.779 0.741 0.865 0.0463 0.877 0.506
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
#> DRR006374 2 0.0000 0.994 0.000 1.000
#> DRR006375 1 0.0000 0.990 1.000 0.000
#> DRR006376 1 0.0000 0.990 1.000 0.000
#> DRR006377 1 0.0000 0.990 1.000 0.000
#> DRR006378 1 0.0000 0.990 1.000 0.000
#> DRR006379 1 0.0000 0.990 1.000 0.000
#> DRR006380 2 0.0000 0.994 0.000 1.000
#> DRR006381 1 0.0000 0.990 1.000 0.000
#> DRR006382 2 0.0000 0.994 0.000 1.000
#> DRR006383 2 0.0000 0.994 0.000 1.000
#> DRR006384 2 0.0000 0.994 0.000 1.000
#> DRR006385 1 0.0000 0.990 1.000 0.000
#> DRR006386 2 0.0938 0.983 0.012 0.988
#> DRR006387 1 0.0000 0.990 1.000 0.000
#> DRR006388 1 0.0000 0.990 1.000 0.000
#> DRR006389 1 0.0000 0.990 1.000 0.000
#> DRR006390 1 0.5408 0.865 0.876 0.124
#> DRR006391 1 0.6973 0.780 0.812 0.188
#> DRR006392 1 0.0000 0.990 1.000 0.000
#> DRR006393 1 0.0000 0.990 1.000 0.000
#> DRR006394 1 0.0000 0.990 1.000 0.000
#> DRR006395 1 0.0000 0.990 1.000 0.000
#> DRR006396 1 0.0000 0.990 1.000 0.000
#> DRR006397 1 0.0000 0.990 1.000 0.000
#> DRR006398 1 0.0000 0.990 1.000 0.000
#> DRR006399 1 0.0000 0.990 1.000 0.000
#> DRR006400 1 0.0000 0.990 1.000 0.000
#> DRR006401 1 0.0000 0.990 1.000 0.000
#> DRR006402 1 0.0376 0.987 0.996 0.004
#> DRR006403 1 0.0000 0.990 1.000 0.000
#> DRR006404 1 0.0000 0.990 1.000 0.000
#> DRR006405 1 0.0000 0.990 1.000 0.000
#> DRR006406 1 0.0000 0.990 1.000 0.000
#> DRR006407 1 0.0000 0.990 1.000 0.000
#> DRR006408 1 0.3584 0.929 0.932 0.068
#> DRR006409 1 0.0000 0.990 1.000 0.000
#> DRR006410 1 0.0000 0.990 1.000 0.000
#> DRR006411 1 0.0000 0.990 1.000 0.000
#> DRR006412 1 0.6887 0.786 0.816 0.184
#> DRR006413 1 0.0000 0.990 1.000 0.000
#> DRR006414 2 0.0000 0.994 0.000 1.000
#> DRR006415 2 0.0000 0.994 0.000 1.000
#> DRR006416 1 0.0000 0.990 1.000 0.000
#> DRR006417 2 0.0000 0.994 0.000 1.000
#> DRR006418 1 0.0000 0.990 1.000 0.000
#> DRR006419 2 0.0000 0.994 0.000 1.000
#> DRR006420 2 0.0000 0.994 0.000 1.000
#> DRR006421 2 0.0000 0.994 0.000 1.000
#> DRR006422 1 0.0000 0.990 1.000 0.000
#> DRR006423 2 0.3733 0.920 0.072 0.928
#> DRR006424 1 0.0000 0.990 1.000 0.000
#> DRR006425 1 0.0000 0.990 1.000 0.000
#> DRR006426 1 0.1414 0.974 0.980 0.020
#> DRR006427 2 0.0000 0.994 0.000 1.000
#> DRR006428 2 0.0000 0.994 0.000 1.000
#> DRR006429 1 0.2778 0.949 0.952 0.048
#> DRR006430 1 0.0000 0.990 1.000 0.000
#> DRR006431 1 0.0000 0.990 1.000 0.000
#> DRR006432 1 0.0000 0.990 1.000 0.000
#> DRR006433 2 0.0000 0.994 0.000 1.000
#> DRR006434 2 0.0000 0.994 0.000 1.000
#> DRR006435 2 0.0000 0.994 0.000 1.000
#> DRR006436 2 0.0000 0.994 0.000 1.000
#> DRR006437 1 0.0000 0.990 1.000 0.000
#> DRR006438 2 0.0000 0.994 0.000 1.000
#> DRR006439 2 0.0000 0.994 0.000 1.000
#> DRR006440 2 0.0000 0.994 0.000 1.000
#> DRR006441 1 0.1633 0.971 0.976 0.024
#> DRR006442 2 0.0000 0.994 0.000 1.000
#> DRR006443 2 0.0000 0.994 0.000 1.000
#> DRR006444 2 0.0000 0.994 0.000 1.000
#> DRR006445 1 0.0000 0.990 1.000 0.000
#> DRR006446 1 0.0000 0.990 1.000 0.000
#> DRR006447 1 0.0000 0.990 1.000 0.000
#> DRR006448 1 0.0000 0.990 1.000 0.000
#> DRR006449 1 0.0000 0.990 1.000 0.000
#> DRR006450 1 0.0000 0.990 1.000 0.000
#> DRR006451 1 0.0000 0.990 1.000 0.000
#> DRR006452 1 0.0000 0.990 1.000 0.000
#> DRR006453 1 0.0000 0.990 1.000 0.000
#> DRR006454 1 0.0000 0.990 1.000 0.000
#> DRR006455 2 0.0000 0.994 0.000 1.000
#> DRR006456 2 0.0000 0.994 0.000 1.000
#> DRR006457 2 0.0000 0.994 0.000 1.000
#> DRR006458 1 0.0000 0.990 1.000 0.000
#> DRR006459 1 0.0000 0.990 1.000 0.000
#> DRR006460 2 0.0000 0.994 0.000 1.000
#> DRR006461 2 0.0000 0.994 0.000 1.000
#> DRR006462 1 0.0000 0.990 1.000 0.000
#> DRR006463 2 0.0000 0.994 0.000 1.000
#> DRR006464 1 0.0000 0.990 1.000 0.000
#> DRR006465 1 0.0000 0.990 1.000 0.000
#> DRR006466 2 0.0000 0.994 0.000 1.000
#> DRR006467 1 0.0000 0.990 1.000 0.000
#> DRR006468 2 0.0000 0.994 0.000 1.000
#> DRR006469 1 0.0938 0.981 0.988 0.012
#> DRR006470 2 0.0000 0.994 0.000 1.000
#> DRR006471 1 0.0000 0.990 1.000 0.000
#> DRR006472 2 0.0000 0.994 0.000 1.000
#> DRR006473 1 0.2948 0.945 0.948 0.052
#> DRR006474 2 0.0000 0.994 0.000 1.000
#> DRR006475 1 0.0000 0.990 1.000 0.000
#> DRR006476 2 0.0000 0.994 0.000 1.000
#> DRR006477 2 0.0000 0.994 0.000 1.000
#> DRR006478 1 0.0000 0.990 1.000 0.000
#> DRR006479 2 0.0000 0.994 0.000 1.000
#> DRR006480 1 0.0000 0.990 1.000 0.000
#> DRR006481 2 0.0000 0.994 0.000 1.000
#> DRR006482 1 0.0000 0.990 1.000 0.000
#> DRR006483 1 0.0000 0.990 1.000 0.000
#> DRR006484 2 0.0000 0.994 0.000 1.000
#> DRR006485 2 0.0000 0.994 0.000 1.000
#> DRR006486 2 0.7299 0.747 0.204 0.796
#> DRR006487 2 0.0000 0.994 0.000 1.000
#> DRR006488 2 0.0000 0.994 0.000 1.000
#> DRR006489 1 0.0000 0.990 1.000 0.000
#> DRR006490 2 0.0000 0.994 0.000 1.000
#> DRR006491 2 0.0000 0.994 0.000 1.000
#> DRR006492 2 0.0000 0.994 0.000 1.000
#> DRR006493 2 0.0000 0.994 0.000 1.000
#> DRR006494 1 0.0000 0.990 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 3 0.3941 0.7606 0.000 0.156 0.844
#> DRR006375 2 0.6095 0.3934 0.392 0.608 0.000
#> DRR006376 2 0.5058 0.5636 0.244 0.756 0.000
#> DRR006377 2 0.0237 0.6270 0.004 0.996 0.000
#> DRR006378 2 0.4002 0.6038 0.160 0.840 0.000
#> DRR006379 2 0.3686 0.6129 0.140 0.860 0.000
#> DRR006380 3 0.5948 0.6456 0.000 0.360 0.640
#> DRR006381 1 0.4002 0.6395 0.840 0.160 0.000
#> DRR006382 3 0.0237 0.8002 0.000 0.004 0.996
#> DRR006383 3 0.0237 0.7985 0.004 0.000 0.996
#> DRR006384 3 0.6026 0.6307 0.000 0.376 0.624
#> DRR006385 1 0.5706 0.5163 0.680 0.320 0.000
#> DRR006386 2 0.6045 -0.1534 0.000 0.620 0.380
#> DRR006387 2 0.5785 0.4900 0.332 0.668 0.000
#> DRR006388 2 0.6180 0.3147 0.416 0.584 0.000
#> DRR006389 2 0.6252 0.2229 0.444 0.556 0.000
#> DRR006390 2 0.3816 0.4952 0.000 0.852 0.148
#> DRR006391 2 0.4062 0.4635 0.000 0.836 0.164
#> DRR006392 1 0.5926 0.4521 0.644 0.356 0.000
#> DRR006393 2 0.6235 0.2457 0.436 0.564 0.000
#> DRR006394 2 0.0237 0.6270 0.004 0.996 0.000
#> DRR006395 2 0.0000 0.6260 0.000 1.000 0.000
#> DRR006396 2 0.6286 0.1856 0.464 0.536 0.000
#> DRR006397 2 0.6095 0.3919 0.392 0.608 0.000
#> DRR006398 2 0.6095 0.3919 0.392 0.608 0.000
#> DRR006399 2 0.3879 0.6058 0.152 0.848 0.000
#> DRR006400 2 0.3686 0.6091 0.140 0.860 0.000
#> DRR006401 2 0.1031 0.6137 0.000 0.976 0.024
#> DRR006402 2 0.2356 0.5784 0.000 0.928 0.072
#> DRR006403 2 0.0000 0.6260 0.000 1.000 0.000
#> DRR006404 2 0.0237 0.6270 0.004 0.996 0.000
#> DRR006405 2 0.5678 0.5050 0.316 0.684 0.000
#> DRR006406 2 0.5591 0.5173 0.304 0.696 0.000
#> DRR006407 2 0.0000 0.6260 0.000 1.000 0.000
#> DRR006408 2 0.3619 0.5166 0.000 0.864 0.136
#> DRR006409 2 0.9717 -0.0592 0.388 0.392 0.220
#> DRR006410 2 0.5810 0.4846 0.336 0.664 0.000
#> DRR006411 1 0.2165 0.6222 0.936 0.064 0.000
#> DRR006412 2 0.4399 0.4137 0.000 0.812 0.188
#> DRR006413 1 0.0000 0.6219 1.000 0.000 0.000
#> DRR006414 3 0.1529 0.7825 0.040 0.000 0.960
#> DRR006415 3 0.0237 0.7985 0.004 0.000 0.996
#> DRR006416 1 0.5363 0.5724 0.724 0.276 0.000
#> DRR006417 1 0.6111 0.0786 0.604 0.000 0.396
#> DRR006418 1 0.1753 0.6375 0.952 0.048 0.000
#> DRR006419 1 0.5810 0.2299 0.664 0.000 0.336
#> DRR006420 1 0.6062 0.1441 0.616 0.000 0.384
#> DRR006421 3 0.0000 0.7996 0.000 0.000 1.000
#> DRR006422 1 0.5905 0.4592 0.648 0.352 0.000
#> DRR006423 3 0.6140 0.5927 0.000 0.404 0.596
#> DRR006424 1 0.5098 0.5947 0.752 0.248 0.000
#> DRR006425 2 0.0747 0.6275 0.016 0.984 0.000
#> DRR006426 1 0.7403 0.5193 0.688 0.216 0.096
#> DRR006427 3 0.5948 0.6452 0.000 0.360 0.640
#> DRR006428 3 0.5216 0.6101 0.260 0.000 0.740
#> DRR006429 1 0.8349 0.3868 0.584 0.308 0.108
#> DRR006430 1 0.5835 0.4818 0.660 0.340 0.000
#> DRR006431 1 0.6204 0.2519 0.576 0.424 0.000
#> DRR006432 1 0.0592 0.6157 0.988 0.000 0.012
#> DRR006433 3 0.5650 0.6801 0.000 0.312 0.688
#> DRR006434 3 0.0237 0.8002 0.000 0.004 0.996
#> DRR006435 3 0.6045 0.6260 0.000 0.380 0.620
#> DRR006436 3 0.6045 0.6260 0.000 0.380 0.620
#> DRR006437 1 0.0237 0.6232 0.996 0.004 0.000
#> DRR006438 3 0.6308 0.2118 0.492 0.000 0.508
#> DRR006439 3 0.5327 0.5975 0.272 0.000 0.728
#> DRR006440 3 0.3941 0.7031 0.156 0.000 0.844
#> DRR006441 2 0.0892 0.6161 0.000 0.980 0.020
#> DRR006442 3 0.0000 0.7996 0.000 0.000 1.000
#> DRR006443 3 0.0237 0.8002 0.000 0.004 0.996
#> DRR006444 3 0.5948 0.6452 0.000 0.360 0.640
#> DRR006445 1 0.5988 0.4199 0.632 0.368 0.000
#> DRR006446 2 0.5859 0.4687 0.344 0.656 0.000
#> DRR006447 1 0.1643 0.6365 0.956 0.044 0.000
#> DRR006448 2 0.5926 0.4575 0.356 0.644 0.000
#> DRR006449 1 0.5621 0.5296 0.692 0.308 0.000
#> DRR006450 1 0.0000 0.6219 1.000 0.000 0.000
#> DRR006451 2 0.5859 0.4757 0.344 0.656 0.000
#> DRR006452 1 0.5465 0.5587 0.712 0.288 0.000
#> DRR006453 1 0.5760 0.5121 0.672 0.328 0.000
#> DRR006454 2 0.5098 0.5488 0.248 0.752 0.000
#> DRR006455 3 0.5733 0.5342 0.324 0.000 0.676
#> DRR006456 3 0.0000 0.7996 0.000 0.000 1.000
#> DRR006457 3 0.0000 0.7996 0.000 0.000 1.000
#> DRR006458 1 0.5529 0.5501 0.704 0.296 0.000
#> DRR006459 2 0.6154 0.3369 0.408 0.592 0.000
#> DRR006460 3 0.5968 0.6420 0.000 0.364 0.636
#> DRR006461 3 0.0237 0.8002 0.000 0.004 0.996
#> DRR006462 2 0.6140 0.3730 0.404 0.596 0.000
#> DRR006463 3 0.0237 0.8002 0.000 0.004 0.996
#> DRR006464 1 0.6168 0.4150 0.588 0.412 0.000
#> DRR006465 1 0.6308 0.0186 0.508 0.492 0.000
#> DRR006466 3 0.1529 0.7966 0.000 0.040 0.960
#> DRR006467 1 0.0000 0.6219 1.000 0.000 0.000
#> DRR006468 3 0.6045 0.6260 0.000 0.380 0.620
#> DRR006469 2 0.0424 0.6226 0.000 0.992 0.008
#> DRR006470 1 0.5733 0.2553 0.676 0.000 0.324
#> DRR006471 1 0.0000 0.6219 1.000 0.000 0.000
#> DRR006472 3 0.2625 0.7841 0.000 0.084 0.916
#> DRR006473 1 0.9181 0.0942 0.448 0.404 0.148
#> DRR006474 3 0.5497 0.6915 0.000 0.292 0.708
#> DRR006475 1 0.4291 0.6330 0.820 0.180 0.000
#> DRR006476 3 0.5988 0.6381 0.000 0.368 0.632
#> DRR006477 3 0.5178 0.7127 0.000 0.256 0.744
#> DRR006478 1 0.6260 0.2096 0.552 0.448 0.000
#> DRR006479 3 0.6305 0.2319 0.484 0.000 0.516
#> DRR006480 1 0.3941 0.6400 0.844 0.156 0.000
#> DRR006481 3 0.0000 0.7996 0.000 0.000 1.000
#> DRR006482 1 0.2066 0.6377 0.940 0.060 0.000
#> DRR006483 1 0.4121 0.6375 0.832 0.168 0.000
#> DRR006484 3 0.0237 0.7985 0.004 0.000 0.996
#> DRR006485 3 0.0237 0.8002 0.000 0.004 0.996
#> DRR006486 1 0.5098 0.3986 0.752 0.000 0.248
#> DRR006487 3 0.5948 0.4781 0.360 0.000 0.640
#> DRR006488 3 0.6026 0.6303 0.000 0.376 0.624
#> DRR006489 1 0.5216 0.5854 0.740 0.260 0.000
#> DRR006490 3 0.3412 0.7300 0.124 0.000 0.876
#> DRR006491 3 0.0237 0.7985 0.004 0.000 0.996
#> DRR006492 3 0.3340 0.7727 0.000 0.120 0.880
#> DRR006493 3 0.0000 0.7996 0.000 0.000 1.000
#> DRR006494 1 0.4002 0.6395 0.840 0.160 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 3 0.2589 0.82965 0.000 0.116 0.884 0.000
#> DRR006375 1 0.1743 0.61229 0.940 0.056 0.000 0.004
#> DRR006376 1 0.6011 -0.11130 0.484 0.476 0.000 0.040
#> DRR006377 2 0.4171 0.64249 0.088 0.828 0.000 0.084
#> DRR006378 2 0.4171 0.64120 0.084 0.828 0.000 0.088
#> DRR006379 1 0.4790 0.17505 0.620 0.380 0.000 0.000
#> DRR006380 3 0.4431 0.57274 0.000 0.304 0.696 0.000
#> DRR006381 1 0.3444 0.61486 0.816 0.000 0.000 0.184
#> DRR006382 3 0.0188 0.89140 0.000 0.004 0.996 0.000
#> DRR006383 3 0.1406 0.87176 0.016 0.000 0.960 0.024
#> DRR006384 3 0.4998 0.11267 0.000 0.488 0.512 0.000
#> DRR006385 1 0.4049 0.60280 0.780 0.008 0.000 0.212
#> DRR006386 2 0.1545 0.65627 0.000 0.952 0.040 0.008
#> DRR006387 1 0.3172 0.56679 0.840 0.160 0.000 0.000
#> DRR006388 2 0.7390 0.22450 0.204 0.512 0.000 0.284
#> DRR006389 2 0.7542 0.14917 0.208 0.472 0.000 0.320
#> DRR006390 2 0.1489 0.66334 0.004 0.952 0.000 0.044
#> DRR006391 2 0.1022 0.66296 0.000 0.968 0.000 0.032
#> DRR006392 1 0.4546 0.59563 0.732 0.012 0.000 0.256
#> DRR006393 1 0.7441 0.38812 0.536 0.244 0.004 0.216
#> DRR006394 2 0.3128 0.65634 0.040 0.884 0.000 0.076
#> DRR006395 2 0.2266 0.65759 0.084 0.912 0.004 0.000
#> DRR006396 1 0.2546 0.62854 0.912 0.028 0.000 0.060
#> DRR006397 2 0.7714 0.15285 0.260 0.448 0.000 0.292
#> DRR006398 2 0.7763 0.09695 0.248 0.420 0.000 0.332
#> DRR006399 1 0.4040 0.46465 0.752 0.248 0.000 0.000
#> DRR006400 1 0.4072 0.45834 0.748 0.252 0.000 0.000
#> DRR006401 2 0.2611 0.65400 0.096 0.896 0.008 0.000
#> DRR006402 2 0.2741 0.65399 0.096 0.892 0.012 0.000
#> DRR006403 2 0.4522 0.45518 0.320 0.680 0.000 0.000
#> DRR006404 2 0.4522 0.46237 0.320 0.680 0.000 0.000
#> DRR006405 2 0.6497 0.40237 0.304 0.596 0.000 0.100
#> DRR006406 2 0.6457 0.41647 0.296 0.604 0.000 0.100
#> DRR006407 2 0.2329 0.66062 0.072 0.916 0.000 0.012
#> DRR006408 2 0.4206 0.63181 0.136 0.816 0.048 0.000
#> DRR006409 1 0.4057 0.50660 0.812 0.000 0.160 0.028
#> DRR006410 1 0.3311 0.56466 0.828 0.172 0.000 0.000
#> DRR006411 4 0.4541 0.55544 0.060 0.144 0.000 0.796
#> DRR006412 2 0.4175 0.60435 0.016 0.784 0.000 0.200
#> DRR006413 4 0.4761 0.22620 0.372 0.000 0.000 0.628
#> DRR006414 3 0.1284 0.87565 0.012 0.000 0.964 0.024
#> DRR006415 3 0.0188 0.89097 0.000 0.000 0.996 0.004
#> DRR006416 1 0.7766 0.29766 0.412 0.244 0.000 0.344
#> DRR006417 4 0.4017 0.63450 0.000 0.044 0.128 0.828
#> DRR006418 4 0.1820 0.57842 0.036 0.020 0.000 0.944
#> DRR006419 4 0.3166 0.63105 0.016 0.000 0.116 0.868
#> DRR006420 4 0.5740 0.55560 0.092 0.000 0.208 0.700
#> DRR006421 3 0.0188 0.89097 0.000 0.000 0.996 0.004
#> DRR006422 1 0.4053 0.60482 0.768 0.000 0.004 0.228
#> DRR006423 2 0.3570 0.63664 0.000 0.860 0.092 0.048
#> DRR006424 1 0.3907 0.59771 0.768 0.000 0.000 0.232
#> DRR006425 2 0.5371 0.58013 0.188 0.732 0.000 0.080
#> DRR006426 2 0.7367 0.21633 0.124 0.452 0.008 0.416
#> DRR006427 2 0.5069 0.38347 0.000 0.664 0.320 0.016
#> DRR006428 3 0.3942 0.58407 0.000 0.000 0.764 0.236
#> DRR006429 2 0.6512 0.49597 0.068 0.624 0.016 0.292
#> DRR006430 1 0.3123 0.62819 0.844 0.000 0.000 0.156
#> DRR006431 1 0.1637 0.62996 0.940 0.000 0.000 0.060
#> DRR006432 4 0.3128 0.57378 0.008 0.108 0.008 0.876
#> DRR006433 3 0.3024 0.80376 0.000 0.148 0.852 0.000
#> DRR006434 3 0.0921 0.88537 0.000 0.028 0.972 0.000
#> DRR006435 2 0.4980 0.40511 0.000 0.680 0.304 0.016
#> DRR006436 2 0.5149 0.34179 0.000 0.648 0.336 0.016
#> DRR006437 4 0.4830 0.23875 0.392 0.000 0.000 0.608
#> DRR006438 4 0.4679 0.42786 0.000 0.000 0.352 0.648
#> DRR006439 3 0.1474 0.85860 0.000 0.000 0.948 0.052
#> DRR006440 4 0.6474 0.30305 0.000 0.076 0.388 0.536
#> DRR006441 2 0.0707 0.66148 0.000 0.980 0.000 0.020
#> DRR006442 3 0.0657 0.88670 0.004 0.000 0.984 0.012
#> DRR006443 3 0.0188 0.89097 0.000 0.000 0.996 0.004
#> DRR006444 2 0.5364 0.20715 0.000 0.592 0.392 0.016
#> DRR006445 1 0.4770 0.57675 0.700 0.012 0.000 0.288
#> DRR006446 2 0.4370 0.62951 0.044 0.800 0.000 0.156
#> DRR006447 4 0.3074 0.52903 0.152 0.000 0.000 0.848
#> DRR006448 1 0.2704 0.58145 0.876 0.124 0.000 0.000
#> DRR006449 1 0.2466 0.62803 0.900 0.004 0.000 0.096
#> DRR006450 4 0.4454 0.40414 0.308 0.000 0.000 0.692
#> DRR006451 1 0.3688 0.52589 0.792 0.208 0.000 0.000
#> DRR006452 1 0.3400 0.61908 0.820 0.000 0.000 0.180
#> DRR006453 1 0.7423 0.37928 0.476 0.180 0.000 0.344
#> DRR006454 2 0.5941 0.49727 0.276 0.652 0.000 0.072
#> DRR006455 4 0.6055 0.52780 0.000 0.096 0.240 0.664
#> DRR006456 3 0.0000 0.89108 0.000 0.000 1.000 0.000
#> DRR006457 3 0.0000 0.89108 0.000 0.000 1.000 0.000
#> DRR006458 1 0.3306 0.63212 0.840 0.000 0.004 0.156
#> DRR006459 1 0.3731 0.61758 0.856 0.032 0.008 0.104
#> DRR006460 2 0.5300 0.15896 0.000 0.580 0.408 0.012
#> DRR006461 3 0.0707 0.88825 0.000 0.020 0.980 0.000
#> DRR006462 1 0.3616 0.58425 0.852 0.112 0.000 0.036
#> DRR006463 3 0.0336 0.89045 0.000 0.000 0.992 0.008
#> DRR006464 2 0.5837 0.39233 0.036 0.564 0.000 0.400
#> DRR006465 1 0.6597 0.49407 0.588 0.108 0.000 0.304
#> DRR006466 3 0.1474 0.87394 0.000 0.052 0.948 0.000
#> DRR006467 4 0.4998 -0.16161 0.488 0.000 0.000 0.512
#> DRR006468 2 0.5003 0.39824 0.000 0.676 0.308 0.016
#> DRR006469 2 0.3335 0.64274 0.020 0.860 0.000 0.120
#> DRR006470 4 0.3764 0.63579 0.000 0.040 0.116 0.844
#> DRR006471 4 0.4624 0.15220 0.340 0.000 0.000 0.660
#> DRR006472 3 0.6532 0.44840 0.004 0.232 0.640 0.124
#> DRR006473 2 0.7369 0.36121 0.212 0.576 0.012 0.200
#> DRR006474 3 0.4220 0.66890 0.004 0.248 0.748 0.000
#> DRR006475 1 0.5070 0.43160 0.580 0.000 0.004 0.416
#> DRR006476 2 0.4992 -0.00775 0.000 0.524 0.476 0.000
#> DRR006477 3 0.2704 0.82307 0.000 0.124 0.876 0.000
#> DRR006478 1 0.7857 0.27494 0.392 0.280 0.000 0.328
#> DRR006479 4 0.4804 0.37326 0.000 0.000 0.384 0.616
#> DRR006480 1 0.5300 0.50241 0.664 0.000 0.028 0.308
#> DRR006481 3 0.0336 0.89045 0.000 0.000 0.992 0.008
#> DRR006482 1 0.4730 0.30088 0.636 0.000 0.000 0.364
#> DRR006483 1 0.6990 0.34245 0.476 0.116 0.000 0.408
#> DRR006484 3 0.0188 0.89097 0.000 0.000 0.996 0.004
#> DRR006485 3 0.0336 0.89045 0.000 0.000 0.992 0.008
#> DRR006486 4 0.6617 0.37274 0.280 0.000 0.120 0.600
#> DRR006487 3 0.2011 0.83061 0.000 0.000 0.920 0.080
#> DRR006488 2 0.5026 0.39123 0.000 0.672 0.312 0.016
#> DRR006489 1 0.3942 0.59505 0.764 0.000 0.000 0.236
#> DRR006490 3 0.1389 0.85895 0.000 0.000 0.952 0.048
#> DRR006491 3 0.0524 0.88836 0.004 0.000 0.988 0.008
#> DRR006492 3 0.2125 0.85829 0.000 0.076 0.920 0.004
#> DRR006493 3 0.0000 0.89108 0.000 0.000 1.000 0.000
#> DRR006494 1 0.6136 0.41836 0.584 0.000 0.060 0.356
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 3 0.4045 0.5084 0.000 0.356 0.644 0.000 0.000
#> DRR006375 4 0.0404 0.7707 0.000 0.000 0.000 0.988 0.012
#> DRR006376 4 0.5203 0.5387 0.212 0.056 0.000 0.704 0.028
#> DRR006377 2 0.6413 0.1835 0.412 0.480 0.000 0.060 0.048
#> DRR006378 2 0.6131 0.2964 0.376 0.532 0.000 0.048 0.044
#> DRR006379 4 0.0566 0.7700 0.000 0.012 0.000 0.984 0.004
#> DRR006380 3 0.4546 0.2327 0.000 0.460 0.532 0.008 0.000
#> DRR006381 4 0.4210 0.6707 0.124 0.000 0.000 0.780 0.096
#> DRR006382 3 0.0727 0.9038 0.004 0.012 0.980 0.000 0.004
#> DRR006383 3 0.2124 0.8421 0.096 0.000 0.900 0.000 0.004
#> DRR006384 2 0.4037 0.4861 0.000 0.704 0.288 0.004 0.004
#> DRR006385 4 0.4133 0.6646 0.052 0.000 0.000 0.768 0.180
#> DRR006386 2 0.0290 0.7675 0.000 0.992 0.008 0.000 0.000
#> DRR006387 4 0.0740 0.7697 0.008 0.004 0.000 0.980 0.008
#> DRR006388 1 0.5831 0.2669 0.588 0.324 0.000 0.020 0.068
#> DRR006389 1 0.5816 0.3100 0.608 0.300 0.000 0.024 0.068
#> DRR006390 2 0.1800 0.7593 0.020 0.932 0.000 0.000 0.048
#> DRR006391 2 0.1701 0.7604 0.016 0.936 0.000 0.000 0.048
#> DRR006392 1 0.3229 0.5666 0.840 0.000 0.000 0.128 0.032
#> DRR006393 1 0.0671 0.6086 0.980 0.016 0.000 0.000 0.004
#> DRR006394 2 0.6210 0.3445 0.348 0.548 0.000 0.036 0.068
#> DRR006395 2 0.4816 0.6841 0.060 0.748 0.000 0.168 0.024
#> DRR006396 4 0.1836 0.7571 0.032 0.000 0.000 0.932 0.036
#> DRR006397 4 0.6908 0.4925 0.112 0.084 0.000 0.572 0.232
#> DRR006398 4 0.6905 0.4577 0.112 0.072 0.000 0.556 0.260
#> DRR006399 4 0.0451 0.7709 0.000 0.008 0.000 0.988 0.004
#> DRR006400 4 0.0451 0.7709 0.000 0.008 0.000 0.988 0.004
#> DRR006401 2 0.2512 0.7515 0.004 0.892 0.004 0.092 0.008
#> DRR006402 2 0.2580 0.7537 0.004 0.892 0.008 0.088 0.008
#> DRR006403 4 0.4986 0.3261 0.024 0.368 0.000 0.600 0.008
#> DRR006404 4 0.5847 0.3498 0.096 0.312 0.000 0.584 0.008
#> DRR006405 1 0.6298 0.1602 0.540 0.352 0.000 0.064 0.044
#> DRR006406 1 0.6318 0.1387 0.532 0.360 0.000 0.064 0.044
#> DRR006407 2 0.5713 0.5422 0.228 0.660 0.000 0.084 0.028
#> DRR006408 2 0.4237 0.6026 0.004 0.724 0.008 0.256 0.008
#> DRR006409 3 0.6027 0.4655 0.184 0.000 0.612 0.196 0.008
#> DRR006410 4 0.1857 0.7502 0.060 0.004 0.000 0.928 0.008
#> DRR006411 5 0.1626 0.7795 0.016 0.044 0.000 0.000 0.940
#> DRR006412 2 0.3639 0.7117 0.076 0.824 0.000 0.000 0.100
#> DRR006413 5 0.5357 0.4447 0.264 0.000 0.000 0.096 0.640
#> DRR006414 3 0.1251 0.8908 0.036 0.000 0.956 0.000 0.008
#> DRR006415 3 0.0162 0.9045 0.000 0.000 0.996 0.000 0.004
#> DRR006416 1 0.0807 0.6084 0.976 0.012 0.000 0.000 0.012
#> DRR006417 5 0.2110 0.8011 0.000 0.016 0.072 0.000 0.912
#> DRR006418 1 0.4449 0.0220 0.512 0.004 0.000 0.000 0.484
#> DRR006419 5 0.1670 0.8005 0.012 0.000 0.052 0.000 0.936
#> DRR006420 1 0.5201 0.1679 0.532 0.000 0.424 0.000 0.044
#> DRR006421 3 0.0162 0.9045 0.000 0.000 0.996 0.000 0.004
#> DRR006422 1 0.5035 0.4208 0.672 0.000 0.000 0.252 0.076
#> DRR006423 2 0.2179 0.7401 0.100 0.896 0.000 0.000 0.004
#> DRR006424 1 0.5741 0.2314 0.544 0.000 0.000 0.360 0.096
#> DRR006425 1 0.5161 -0.0987 0.488 0.480 0.000 0.008 0.024
#> DRR006426 1 0.4555 0.4491 0.720 0.224 0.000 0.000 0.056
#> DRR006427 2 0.1357 0.7662 0.000 0.948 0.048 0.000 0.004
#> DRR006428 3 0.2074 0.8233 0.000 0.000 0.896 0.000 0.104
#> DRR006429 1 0.5420 0.0562 0.524 0.416 0.000 0.000 0.060
#> DRR006430 1 0.5569 0.2388 0.556 0.000 0.000 0.364 0.080
#> DRR006431 4 0.4292 0.5243 0.272 0.000 0.000 0.704 0.024
#> DRR006432 5 0.3381 0.6390 0.176 0.016 0.000 0.000 0.808
#> DRR006433 3 0.1731 0.8762 0.000 0.060 0.932 0.004 0.004
#> DRR006434 3 0.0510 0.9034 0.000 0.016 0.984 0.000 0.000
#> DRR006435 2 0.1282 0.7663 0.000 0.952 0.044 0.000 0.004
#> DRR006436 2 0.1831 0.7531 0.000 0.920 0.076 0.000 0.004
#> DRR006437 5 0.4147 0.4285 0.008 0.000 0.000 0.316 0.676
#> DRR006438 5 0.3003 0.7469 0.000 0.000 0.188 0.000 0.812
#> DRR006439 3 0.0162 0.9045 0.000 0.000 0.996 0.000 0.004
#> DRR006440 5 0.4547 0.7208 0.000 0.072 0.192 0.000 0.736
#> DRR006441 2 0.0703 0.7664 0.000 0.976 0.000 0.000 0.024
#> DRR006442 3 0.1412 0.8928 0.036 0.008 0.952 0.000 0.004
#> DRR006443 3 0.0162 0.9047 0.000 0.004 0.996 0.000 0.000
#> DRR006444 2 0.2806 0.6963 0.000 0.844 0.152 0.000 0.004
#> DRR006445 1 0.4618 0.4881 0.724 0.000 0.000 0.208 0.068
#> DRR006446 2 0.6083 0.3039 0.360 0.528 0.000 0.008 0.104
#> DRR006447 5 0.2142 0.7621 0.028 0.004 0.000 0.048 0.920
#> DRR006448 4 0.0162 0.7709 0.000 0.000 0.000 0.996 0.004
#> DRR006449 4 0.3201 0.7216 0.096 0.000 0.000 0.852 0.052
#> DRR006450 5 0.3130 0.7286 0.048 0.000 0.000 0.096 0.856
#> DRR006451 4 0.0324 0.7714 0.000 0.004 0.000 0.992 0.004
#> DRR006452 4 0.5066 0.5162 0.240 0.000 0.000 0.676 0.084
#> DRR006453 1 0.0693 0.6088 0.980 0.008 0.000 0.000 0.012
#> DRR006454 4 0.5922 0.4515 0.040 0.296 0.000 0.608 0.056
#> DRR006455 5 0.3339 0.7678 0.000 0.112 0.048 0.000 0.840
#> DRR006456 3 0.0324 0.9053 0.004 0.000 0.992 0.000 0.004
#> DRR006457 3 0.0613 0.9049 0.008 0.004 0.984 0.000 0.004
#> DRR006458 1 0.4479 0.4456 0.704 0.000 0.004 0.264 0.028
#> DRR006459 1 0.1901 0.6048 0.928 0.000 0.004 0.056 0.012
#> DRR006460 2 0.2806 0.6989 0.000 0.844 0.152 0.000 0.004
#> DRR006461 3 0.1197 0.8868 0.000 0.048 0.952 0.000 0.000
#> DRR006462 4 0.1043 0.7654 0.000 0.000 0.000 0.960 0.040
#> DRR006463 3 0.0290 0.9043 0.000 0.008 0.992 0.000 0.000
#> DRR006464 1 0.5678 0.0959 0.524 0.392 0.000 0.000 0.084
#> DRR006465 1 0.0290 0.6085 0.992 0.000 0.000 0.000 0.008
#> DRR006466 3 0.0703 0.9009 0.000 0.024 0.976 0.000 0.000
#> DRR006467 1 0.4761 0.2974 0.616 0.000 0.000 0.028 0.356
#> DRR006468 2 0.1357 0.7654 0.000 0.948 0.048 0.000 0.004
#> DRR006469 2 0.1952 0.7543 0.004 0.912 0.000 0.000 0.084
#> DRR006470 5 0.2026 0.8022 0.016 0.012 0.044 0.000 0.928
#> DRR006471 1 0.2516 0.5675 0.860 0.000 0.000 0.000 0.140
#> DRR006472 1 0.7582 -0.0603 0.352 0.308 0.300 0.000 0.040
#> DRR006473 1 0.4206 0.4164 0.708 0.272 0.000 0.000 0.020
#> DRR006474 2 0.4703 0.4159 0.016 0.640 0.336 0.000 0.008
#> DRR006475 1 0.3730 0.5736 0.832 0.000 0.016 0.104 0.048
#> DRR006476 2 0.4391 0.7008 0.060 0.768 0.164 0.000 0.008
#> DRR006477 3 0.3177 0.7328 0.000 0.208 0.792 0.000 0.000
#> DRR006478 1 0.1628 0.6013 0.936 0.056 0.000 0.000 0.008
#> DRR006479 5 0.3707 0.6337 0.000 0.000 0.284 0.000 0.716
#> DRR006480 1 0.6217 0.3984 0.628 0.000 0.056 0.232 0.084
#> DRR006481 3 0.0162 0.9045 0.000 0.000 0.996 0.000 0.004
#> DRR006482 4 0.4216 0.6019 0.008 0.012 0.000 0.720 0.260
#> DRR006483 1 0.0963 0.6062 0.964 0.000 0.000 0.000 0.036
#> DRR006484 3 0.0324 0.9043 0.004 0.000 0.992 0.000 0.004
#> DRR006485 3 0.0404 0.9034 0.000 0.012 0.988 0.000 0.000
#> DRR006486 1 0.4671 0.3227 0.640 0.000 0.028 0.000 0.332
#> DRR006487 3 0.1117 0.8912 0.020 0.000 0.964 0.000 0.016
#> DRR006488 2 0.1282 0.7663 0.000 0.952 0.044 0.000 0.004
#> DRR006489 1 0.5675 0.2533 0.556 0.000 0.000 0.352 0.092
#> DRR006490 3 0.0671 0.9001 0.016 0.000 0.980 0.000 0.004
#> DRR006491 3 0.0955 0.8972 0.028 0.000 0.968 0.000 0.004
#> DRR006492 3 0.3365 0.7609 0.008 0.180 0.808 0.000 0.004
#> DRR006493 3 0.0162 0.9052 0.004 0.000 0.996 0.000 0.000
#> DRR006494 1 0.3629 0.5430 0.824 0.000 0.136 0.028 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 5 0.0458 0.9119 0.000 0.000 0.016 0.000 0.984 0.000
#> DRR006375 4 0.1958 0.7913 0.100 0.000 0.000 0.896 0.000 0.004
#> DRR006376 4 0.3789 0.5109 0.000 0.332 0.000 0.660 0.008 0.000
#> DRR006377 2 0.1149 0.7713 0.024 0.960 0.000 0.008 0.000 0.008
#> DRR006378 2 0.0508 0.7743 0.012 0.984 0.000 0.000 0.004 0.000
#> DRR006379 4 0.0551 0.8088 0.004 0.008 0.000 0.984 0.000 0.004
#> DRR006380 5 0.0458 0.9107 0.000 0.000 0.016 0.000 0.984 0.000
#> DRR006381 1 0.4939 0.3739 0.612 0.000 0.000 0.292 0.000 0.096
#> DRR006382 3 0.2092 0.8396 0.000 0.000 0.876 0.000 0.124 0.000
#> DRR006383 1 0.5672 0.3322 0.512 0.000 0.184 0.000 0.304 0.000
#> DRR006384 5 0.0363 0.9141 0.000 0.000 0.012 0.000 0.988 0.000
#> DRR006385 4 0.4800 0.6135 0.164 0.000 0.000 0.672 0.000 0.164
#> DRR006386 5 0.0405 0.9158 0.000 0.004 0.008 0.000 0.988 0.000
#> DRR006387 4 0.1554 0.8014 0.004 0.044 0.000 0.940 0.004 0.008
#> DRR006388 2 0.1743 0.7691 0.008 0.936 0.000 0.028 0.004 0.024
#> DRR006389 2 0.1965 0.7654 0.008 0.924 0.000 0.040 0.004 0.024
#> DRR006390 2 0.4411 0.4224 0.000 0.612 0.000 0.004 0.356 0.028
#> DRR006391 2 0.4370 0.2135 0.000 0.536 0.000 0.004 0.444 0.016
#> DRR006392 1 0.1285 0.8422 0.944 0.052 0.004 0.000 0.000 0.000
#> DRR006393 1 0.3430 0.7385 0.772 0.208 0.016 0.000 0.000 0.004
#> DRR006394 2 0.1148 0.7681 0.000 0.960 0.000 0.016 0.004 0.020
#> DRR006395 2 0.4336 0.1456 0.000 0.572 0.000 0.408 0.012 0.008
#> DRR006396 4 0.2445 0.7794 0.108 0.000 0.000 0.872 0.000 0.020
#> DRR006397 4 0.5011 0.6238 0.016 0.204 0.000 0.672 0.000 0.108
#> DRR006398 4 0.5315 0.5830 0.016 0.216 0.000 0.636 0.000 0.132
#> DRR006399 4 0.0665 0.8067 0.004 0.008 0.000 0.980 0.008 0.000
#> DRR006400 4 0.0665 0.8067 0.004 0.008 0.000 0.980 0.008 0.000
#> DRR006401 5 0.3582 0.7201 0.000 0.024 0.000 0.192 0.776 0.008
#> DRR006402 5 0.3626 0.7215 0.000 0.028 0.000 0.188 0.776 0.008
#> DRR006403 4 0.2566 0.7693 0.000 0.112 0.000 0.868 0.012 0.008
#> DRR006404 4 0.2967 0.7576 0.004 0.136 0.000 0.840 0.012 0.008
#> DRR006405 2 0.0922 0.7723 0.024 0.968 0.000 0.004 0.000 0.004
#> DRR006406 2 0.1116 0.7710 0.028 0.960 0.000 0.008 0.000 0.004
#> DRR006407 2 0.2325 0.7180 0.000 0.884 0.000 0.100 0.008 0.008
#> DRR006408 4 0.4488 0.6596 0.000 0.128 0.000 0.728 0.136 0.008
#> DRR006409 3 0.4503 0.6384 0.204 0.000 0.720 0.060 0.008 0.008
#> DRR006410 4 0.3196 0.7485 0.008 0.148 0.000 0.824 0.012 0.008
#> DRR006411 6 0.1219 0.7287 0.004 0.048 0.000 0.000 0.000 0.948
#> DRR006412 2 0.4541 0.6036 0.000 0.704 0.000 0.004 0.196 0.096
#> DRR006413 1 0.3606 0.6006 0.728 0.000 0.000 0.016 0.000 0.256
#> DRR006414 3 0.0865 0.9180 0.036 0.000 0.964 0.000 0.000 0.000
#> DRR006415 3 0.0291 0.9364 0.000 0.000 0.992 0.000 0.004 0.004
#> DRR006416 1 0.2838 0.7683 0.808 0.188 0.000 0.000 0.000 0.004
#> DRR006417 6 0.0935 0.7444 0.000 0.004 0.032 0.000 0.000 0.964
#> DRR006418 2 0.4526 0.0831 0.032 0.512 0.000 0.000 0.000 0.456
#> DRR006419 6 0.0862 0.7438 0.004 0.008 0.016 0.000 0.000 0.972
#> DRR006420 3 0.3362 0.7023 0.184 0.012 0.792 0.000 0.000 0.012
#> DRR006421 3 0.0547 0.9361 0.000 0.000 0.980 0.000 0.000 0.020
#> DRR006422 1 0.0508 0.8413 0.984 0.012 0.004 0.000 0.000 0.000
#> DRR006423 5 0.1049 0.8945 0.008 0.032 0.000 0.000 0.960 0.000
#> DRR006424 1 0.1421 0.8190 0.944 0.000 0.000 0.028 0.000 0.028
#> DRR006425 2 0.2698 0.7173 0.120 0.860 0.000 0.004 0.008 0.008
#> DRR006426 2 0.1867 0.7602 0.064 0.916 0.000 0.000 0.000 0.020
#> DRR006427 5 0.0405 0.9158 0.000 0.004 0.008 0.000 0.988 0.000
#> DRR006428 3 0.2378 0.8303 0.000 0.000 0.848 0.000 0.000 0.152
#> DRR006429 2 0.1245 0.7721 0.032 0.952 0.000 0.000 0.000 0.016
#> DRR006430 1 0.0964 0.8323 0.968 0.004 0.000 0.016 0.000 0.012
#> DRR006431 1 0.1843 0.8027 0.912 0.004 0.000 0.080 0.000 0.004
#> DRR006432 6 0.3864 -0.0522 0.000 0.480 0.000 0.000 0.000 0.520
#> DRR006433 3 0.1340 0.9161 0.000 0.000 0.948 0.040 0.008 0.004
#> DRR006434 3 0.0909 0.9319 0.000 0.000 0.968 0.000 0.020 0.012
#> DRR006435 5 0.0405 0.9158 0.000 0.004 0.008 0.000 0.988 0.000
#> DRR006436 5 0.0405 0.9158 0.000 0.004 0.008 0.000 0.988 0.000
#> DRR006437 6 0.4844 -0.0890 0.056 0.000 0.000 0.440 0.000 0.504
#> DRR006438 6 0.2219 0.7119 0.000 0.000 0.136 0.000 0.000 0.864
#> DRR006439 3 0.0790 0.9331 0.000 0.000 0.968 0.000 0.000 0.032
#> DRR006440 6 0.2491 0.6912 0.000 0.000 0.164 0.000 0.000 0.836
#> DRR006441 5 0.0458 0.9089 0.000 0.016 0.000 0.000 0.984 0.000
#> DRR006442 3 0.0260 0.9343 0.008 0.000 0.992 0.000 0.000 0.000
#> DRR006443 3 0.0777 0.9346 0.000 0.000 0.972 0.000 0.004 0.024
#> DRR006444 5 0.0405 0.9158 0.000 0.004 0.008 0.000 0.988 0.000
#> DRR006445 1 0.1503 0.8421 0.944 0.032 0.000 0.008 0.000 0.016
#> DRR006446 2 0.1511 0.7641 0.000 0.940 0.000 0.004 0.012 0.044
#> DRR006447 6 0.3269 0.6795 0.120 0.028 0.000 0.020 0.000 0.832
#> DRR006448 4 0.1349 0.8043 0.056 0.000 0.000 0.940 0.000 0.004
#> DRR006449 4 0.4408 0.5633 0.292 0.000 0.000 0.656 0.000 0.052
#> DRR006450 6 0.4032 0.5896 0.192 0.000 0.000 0.068 0.000 0.740
#> DRR006451 4 0.0862 0.8091 0.016 0.004 0.000 0.972 0.000 0.008
#> DRR006452 1 0.3202 0.7185 0.816 0.000 0.000 0.144 0.000 0.040
#> DRR006453 1 0.2854 0.7492 0.792 0.208 0.000 0.000 0.000 0.000
#> DRR006454 4 0.2554 0.8000 0.044 0.024 0.000 0.892 0.000 0.040
#> DRR006455 6 0.2882 0.6510 0.000 0.000 0.008 0.000 0.180 0.812
#> DRR006456 3 0.0000 0.9357 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006457 3 0.0291 0.9349 0.004 0.000 0.992 0.000 0.004 0.000
#> DRR006458 1 0.1261 0.8418 0.952 0.024 0.024 0.000 0.000 0.000
#> DRR006459 1 0.3838 0.7582 0.772 0.184 0.032 0.004 0.004 0.004
#> DRR006460 5 0.0551 0.9082 0.000 0.004 0.004 0.008 0.984 0.000
#> DRR006461 3 0.1663 0.8872 0.000 0.000 0.912 0.000 0.088 0.000
#> DRR006462 4 0.2680 0.7792 0.056 0.000 0.000 0.868 0.000 0.076
#> DRR006463 3 0.0777 0.9346 0.000 0.000 0.972 0.000 0.004 0.024
#> DRR006464 2 0.1719 0.7666 0.032 0.932 0.000 0.000 0.004 0.032
#> DRR006465 1 0.2135 0.8141 0.872 0.128 0.000 0.000 0.000 0.000
#> DRR006466 3 0.0725 0.9353 0.000 0.000 0.976 0.012 0.000 0.012
#> DRR006467 1 0.1411 0.8214 0.936 0.000 0.000 0.004 0.000 0.060
#> DRR006468 5 0.0405 0.9158 0.000 0.004 0.008 0.000 0.988 0.000
#> DRR006469 5 0.3179 0.7985 0.000 0.092 0.000 0.032 0.848 0.028
#> DRR006470 6 0.0777 0.7399 0.000 0.024 0.004 0.000 0.000 0.972
#> DRR006471 1 0.2680 0.8189 0.856 0.124 0.004 0.000 0.000 0.016
#> DRR006472 2 0.4162 0.6744 0.028 0.784 0.116 0.000 0.068 0.004
#> DRR006473 5 0.5974 0.0788 0.312 0.248 0.000 0.000 0.440 0.000
#> DRR006474 5 0.1138 0.9020 0.012 0.004 0.024 0.000 0.960 0.000
#> DRR006475 1 0.2052 0.8376 0.912 0.056 0.028 0.000 0.000 0.004
#> DRR006476 2 0.6323 0.2830 0.000 0.504 0.340 0.044 0.100 0.012
#> DRR006477 3 0.2003 0.8527 0.000 0.000 0.884 0.000 0.116 0.000
#> DRR006478 2 0.3737 0.2296 0.392 0.608 0.000 0.000 0.000 0.000
#> DRR006479 6 0.3864 0.0325 0.000 0.000 0.480 0.000 0.000 0.520
#> DRR006480 1 0.1511 0.8338 0.940 0.000 0.044 0.004 0.000 0.012
#> DRR006481 3 0.0865 0.9312 0.000 0.000 0.964 0.000 0.000 0.036
#> DRR006482 4 0.4066 0.6669 0.064 0.000 0.000 0.732 0.000 0.204
#> DRR006483 1 0.3789 0.5705 0.668 0.324 0.004 0.000 0.000 0.004
#> DRR006484 3 0.0713 0.9344 0.000 0.000 0.972 0.000 0.000 0.028
#> DRR006485 3 0.0858 0.9338 0.000 0.000 0.968 0.000 0.004 0.028
#> DRR006486 1 0.1787 0.8390 0.932 0.016 0.032 0.000 0.000 0.020
#> DRR006487 3 0.0622 0.9361 0.008 0.000 0.980 0.000 0.000 0.012
#> DRR006488 5 0.0405 0.9158 0.000 0.004 0.008 0.000 0.988 0.000
#> DRR006489 1 0.1334 0.8221 0.948 0.000 0.000 0.032 0.000 0.020
#> DRR006490 3 0.0260 0.9343 0.008 0.000 0.992 0.000 0.000 0.000
#> DRR006491 3 0.0260 0.9343 0.008 0.000 0.992 0.000 0.000 0.000
#> DRR006492 3 0.1493 0.9030 0.000 0.000 0.936 0.004 0.056 0.004
#> DRR006493 3 0.0000 0.9357 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006494 1 0.2070 0.8333 0.908 0.044 0.048 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)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.899 0.918 0.966 0.3600 0.660 0.660
#> 3 3 0.478 0.727 0.828 0.5718 0.747 0.625
#> 4 4 0.573 0.625 0.806 0.2040 0.901 0.779
#> 5 5 0.594 0.575 0.743 0.0647 0.965 0.906
#> 6 6 0.625 0.595 0.715 0.0557 0.852 0.584
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
#> DRR006374 2 0.0000 0.963 0.000 1.000
#> DRR006375 1 0.0000 0.964 1.000 0.000
#> DRR006376 1 0.0000 0.964 1.000 0.000
#> DRR006377 1 0.1843 0.943 0.972 0.028
#> DRR006378 1 0.9933 0.179 0.548 0.452
#> DRR006379 1 0.0000 0.964 1.000 0.000
#> DRR006380 1 0.7602 0.727 0.780 0.220
#> DRR006381 1 0.0000 0.964 1.000 0.000
#> DRR006382 1 0.8144 0.672 0.748 0.252
#> DRR006383 1 0.8144 0.672 0.748 0.252
#> DRR006384 2 0.0000 0.963 0.000 1.000
#> DRR006385 1 0.0000 0.964 1.000 0.000
#> DRR006386 2 0.0000 0.963 0.000 1.000
#> DRR006387 1 0.0000 0.964 1.000 0.000
#> DRR006388 1 0.0000 0.964 1.000 0.000
#> DRR006389 1 0.0000 0.964 1.000 0.000
#> DRR006390 2 0.0000 0.963 0.000 1.000
#> DRR006391 2 0.0000 0.963 0.000 1.000
#> DRR006392 1 0.0000 0.964 1.000 0.000
#> DRR006393 1 0.0000 0.964 1.000 0.000
#> DRR006394 1 0.9933 0.179 0.548 0.452
#> DRR006395 1 0.0000 0.964 1.000 0.000
#> DRR006396 1 0.0000 0.964 1.000 0.000
#> DRR006397 1 0.0000 0.964 1.000 0.000
#> DRR006398 1 0.0000 0.964 1.000 0.000
#> DRR006399 1 0.0000 0.964 1.000 0.000
#> DRR006400 1 0.0000 0.964 1.000 0.000
#> DRR006401 2 0.0000 0.963 0.000 1.000
#> DRR006402 2 0.0000 0.963 0.000 1.000
#> DRR006403 1 0.0000 0.964 1.000 0.000
#> DRR006404 1 0.0000 0.964 1.000 0.000
#> DRR006405 1 0.0000 0.964 1.000 0.000
#> DRR006406 1 0.0000 0.964 1.000 0.000
#> DRR006407 1 0.1843 0.943 0.972 0.028
#> DRR006408 1 0.6887 0.778 0.816 0.184
#> DRR006409 1 0.0000 0.964 1.000 0.000
#> DRR006410 1 0.0000 0.964 1.000 0.000
#> DRR006411 1 0.0000 0.964 1.000 0.000
#> DRR006412 2 0.0000 0.963 0.000 1.000
#> DRR006413 1 0.0000 0.964 1.000 0.000
#> DRR006414 1 0.0000 0.964 1.000 0.000
#> DRR006415 1 0.0000 0.964 1.000 0.000
#> DRR006416 1 0.0000 0.964 1.000 0.000
#> DRR006417 1 0.0000 0.964 1.000 0.000
#> DRR006418 1 0.0000 0.964 1.000 0.000
#> DRR006419 1 0.0000 0.964 1.000 0.000
#> DRR006420 1 0.0000 0.964 1.000 0.000
#> DRR006421 1 0.0376 0.961 0.996 0.004
#> DRR006422 1 0.4022 0.898 0.920 0.080
#> DRR006423 2 0.0000 0.963 0.000 1.000
#> DRR006424 1 0.0000 0.964 1.000 0.000
#> DRR006425 1 0.4022 0.898 0.920 0.080
#> DRR006426 1 0.0000 0.964 1.000 0.000
#> DRR006427 2 0.0000 0.963 0.000 1.000
#> DRR006428 1 0.0000 0.964 1.000 0.000
#> DRR006429 1 0.3431 0.913 0.936 0.064
#> DRR006430 1 0.0000 0.964 1.000 0.000
#> DRR006431 1 0.0000 0.964 1.000 0.000
#> DRR006432 1 0.0000 0.964 1.000 0.000
#> DRR006433 1 0.1843 0.943 0.972 0.028
#> DRR006434 2 0.0000 0.963 0.000 1.000
#> DRR006435 2 0.0000 0.963 0.000 1.000
#> DRR006436 2 0.0000 0.963 0.000 1.000
#> DRR006437 1 0.0000 0.964 1.000 0.000
#> DRR006438 1 0.0000 0.964 1.000 0.000
#> DRR006439 1 0.0000 0.964 1.000 0.000
#> DRR006440 2 0.7453 0.745 0.212 0.788
#> DRR006441 1 0.9933 0.179 0.548 0.452
#> DRR006442 1 0.0000 0.964 1.000 0.000
#> DRR006443 2 0.7453 0.745 0.212 0.788
#> DRR006444 2 0.0000 0.963 0.000 1.000
#> DRR006445 1 0.0000 0.964 1.000 0.000
#> DRR006446 2 0.0000 0.963 0.000 1.000
#> DRR006447 1 0.0000 0.964 1.000 0.000
#> DRR006448 1 0.0000 0.964 1.000 0.000
#> DRR006449 1 0.0000 0.964 1.000 0.000
#> DRR006450 1 0.0000 0.964 1.000 0.000
#> DRR006451 1 0.0000 0.964 1.000 0.000
#> DRR006452 1 0.0000 0.964 1.000 0.000
#> DRR006453 1 0.0000 0.964 1.000 0.000
#> DRR006454 1 0.0000 0.964 1.000 0.000
#> DRR006455 2 0.0000 0.963 0.000 1.000
#> DRR006456 1 0.0000 0.964 1.000 0.000
#> DRR006457 1 0.0000 0.964 1.000 0.000
#> DRR006458 1 0.0000 0.964 1.000 0.000
#> DRR006459 1 0.0000 0.964 1.000 0.000
#> DRR006460 2 0.0000 0.963 0.000 1.000
#> DRR006461 2 0.0000 0.963 0.000 1.000
#> DRR006462 1 0.0000 0.964 1.000 0.000
#> DRR006463 2 0.7453 0.745 0.212 0.788
#> DRR006464 1 0.3431 0.913 0.936 0.064
#> DRR006465 1 0.0000 0.964 1.000 0.000
#> DRR006466 1 0.1843 0.943 0.972 0.028
#> DRR006467 1 0.0000 0.964 1.000 0.000
#> DRR006468 2 0.0000 0.963 0.000 1.000
#> DRR006469 1 0.9933 0.179 0.548 0.452
#> DRR006470 1 0.0000 0.964 1.000 0.000
#> DRR006471 1 0.0000 0.964 1.000 0.000
#> DRR006472 1 0.1184 0.952 0.984 0.016
#> DRR006473 2 0.0000 0.963 0.000 1.000
#> DRR006474 2 0.0000 0.963 0.000 1.000
#> DRR006475 1 0.0000 0.964 1.000 0.000
#> DRR006476 1 0.3879 0.902 0.924 0.076
#> DRR006477 1 0.3584 0.908 0.932 0.068
#> DRR006478 1 0.0000 0.964 1.000 0.000
#> DRR006479 1 0.0000 0.964 1.000 0.000
#> DRR006480 1 0.0000 0.964 1.000 0.000
#> DRR006481 1 0.0000 0.964 1.000 0.000
#> DRR006482 1 0.0000 0.964 1.000 0.000
#> DRR006483 1 0.0000 0.964 1.000 0.000
#> DRR006484 1 0.0000 0.964 1.000 0.000
#> DRR006485 2 0.7453 0.745 0.212 0.788
#> DRR006486 1 0.0000 0.964 1.000 0.000
#> DRR006487 1 0.0000 0.964 1.000 0.000
#> DRR006488 2 0.0000 0.963 0.000 1.000
#> DRR006489 1 0.0000 0.964 1.000 0.000
#> DRR006490 1 0.0000 0.964 1.000 0.000
#> DRR006491 1 0.0000 0.964 1.000 0.000
#> DRR006492 1 0.0000 0.964 1.000 0.000
#> DRR006493 1 0.0000 0.964 1.000 0.000
#> DRR006494 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
#> DRR006374 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006375 1 0.0592 0.8233 0.988 0.000 0.012
#> DRR006376 3 0.6026 0.7816 0.376 0.000 0.624
#> DRR006377 3 0.5016 0.7893 0.240 0.000 0.760
#> DRR006378 2 0.9494 -0.0719 0.184 0.412 0.404
#> DRR006379 3 0.6045 0.7816 0.380 0.000 0.620
#> DRR006380 3 0.8199 0.6540 0.200 0.160 0.640
#> DRR006381 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006382 1 0.9149 0.0353 0.440 0.144 0.416
#> DRR006383 1 0.9149 0.0353 0.440 0.144 0.416
#> DRR006384 2 0.1031 0.8703 0.000 0.976 0.024
#> DRR006385 1 0.4062 0.7446 0.836 0.000 0.164
#> DRR006386 2 0.0424 0.8678 0.000 0.992 0.008
#> DRR006387 1 0.1529 0.8126 0.960 0.000 0.040
#> DRR006388 1 0.4504 0.7364 0.804 0.000 0.196
#> DRR006389 1 0.4504 0.7364 0.804 0.000 0.196
#> DRR006390 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006391 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006392 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006393 1 0.4121 0.6999 0.832 0.000 0.168
#> DRR006394 2 0.9494 -0.0719 0.184 0.412 0.404
#> DRR006395 1 0.5810 0.3451 0.664 0.000 0.336
#> DRR006396 1 0.1529 0.8126 0.960 0.000 0.040
#> DRR006397 1 0.4504 0.7364 0.804 0.000 0.196
#> DRR006398 1 0.4504 0.7364 0.804 0.000 0.196
#> DRR006399 3 0.6045 0.7816 0.380 0.000 0.620
#> DRR006400 3 0.6045 0.7816 0.380 0.000 0.620
#> DRR006401 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006402 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006403 3 0.6045 0.7816 0.380 0.000 0.620
#> DRR006404 3 0.6026 0.7816 0.376 0.000 0.624
#> DRR006405 1 0.4842 0.6065 0.776 0.000 0.224
#> DRR006406 1 0.4842 0.6065 0.776 0.000 0.224
#> DRR006407 3 0.5016 0.7893 0.240 0.000 0.760
#> DRR006408 3 0.8493 0.7294 0.248 0.148 0.604
#> DRR006409 1 0.0237 0.8250 0.996 0.000 0.004
#> DRR006410 1 0.1529 0.8126 0.960 0.000 0.040
#> DRR006411 1 0.4399 0.7453 0.812 0.000 0.188
#> DRR006412 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006413 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006414 1 0.5363 0.6364 0.724 0.000 0.276
#> DRR006415 1 0.5363 0.6364 0.724 0.000 0.276
#> DRR006416 1 0.2165 0.8154 0.936 0.000 0.064
#> DRR006417 1 0.3879 0.7788 0.848 0.000 0.152
#> DRR006418 1 0.1643 0.8241 0.956 0.000 0.044
#> DRR006419 1 0.2165 0.8173 0.936 0.000 0.064
#> DRR006420 1 0.2165 0.8173 0.936 0.000 0.064
#> DRR006421 1 0.5216 0.6770 0.740 0.000 0.260
#> DRR006422 3 0.6049 0.7841 0.204 0.040 0.756
#> DRR006423 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006424 1 0.0424 0.8242 0.992 0.000 0.008
#> DRR006425 3 0.6049 0.7841 0.204 0.040 0.756
#> DRR006426 1 0.3879 0.7788 0.848 0.000 0.152
#> DRR006427 2 0.0424 0.8678 0.000 0.992 0.008
#> DRR006428 1 0.1753 0.8182 0.952 0.000 0.048
#> DRR006429 3 0.5772 0.7874 0.220 0.024 0.756
#> DRR006430 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006431 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006432 1 0.3879 0.7788 0.848 0.000 0.152
#> DRR006433 1 0.6154 0.2172 0.592 0.000 0.408
#> DRR006434 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006435 2 0.0424 0.8678 0.000 0.992 0.008
#> DRR006436 2 0.0424 0.8678 0.000 0.992 0.008
#> DRR006437 1 0.4062 0.7446 0.836 0.000 0.164
#> DRR006438 1 0.2165 0.8173 0.936 0.000 0.064
#> DRR006439 1 0.4062 0.7705 0.836 0.000 0.164
#> DRR006440 2 0.5650 0.6880 0.000 0.688 0.312
#> DRR006441 2 0.9494 -0.0719 0.184 0.412 0.404
#> DRR006442 1 0.2356 0.8079 0.928 0.000 0.072
#> DRR006443 2 0.5650 0.6880 0.000 0.688 0.312
#> DRR006444 2 0.0424 0.8678 0.000 0.992 0.008
#> DRR006445 1 0.0747 0.8222 0.984 0.000 0.016
#> DRR006446 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006447 1 0.1643 0.8241 0.956 0.000 0.044
#> DRR006448 3 0.6045 0.7816 0.380 0.000 0.620
#> DRR006449 1 0.0747 0.8222 0.984 0.000 0.016
#> DRR006450 1 0.0747 0.8222 0.984 0.000 0.016
#> DRR006451 3 0.6045 0.7816 0.380 0.000 0.620
#> DRR006452 1 0.0747 0.8222 0.984 0.000 0.016
#> DRR006453 1 0.4121 0.6999 0.832 0.000 0.168
#> DRR006454 1 0.4399 0.7444 0.812 0.000 0.188
#> DRR006455 2 0.0424 0.8678 0.000 0.992 0.008
#> DRR006456 1 0.5363 0.6364 0.724 0.000 0.276
#> DRR006457 1 0.4291 0.7569 0.820 0.000 0.180
#> DRR006458 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006459 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006460 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006461 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006462 1 0.1529 0.8126 0.960 0.000 0.040
#> DRR006463 2 0.5650 0.6880 0.000 0.688 0.312
#> DRR006464 3 0.5772 0.7874 0.220 0.024 0.756
#> DRR006465 1 0.4002 0.7105 0.840 0.000 0.160
#> DRR006466 1 0.6308 -0.1726 0.508 0.000 0.492
#> DRR006467 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006468 2 0.0424 0.8678 0.000 0.992 0.008
#> DRR006469 2 0.9494 -0.0719 0.184 0.412 0.404
#> DRR006470 1 0.3879 0.7788 0.848 0.000 0.152
#> DRR006471 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006472 3 0.6045 0.5204 0.380 0.000 0.620
#> DRR006473 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006474 2 0.0747 0.8733 0.000 0.984 0.016
#> DRR006475 1 0.0237 0.8250 0.996 0.000 0.004
#> DRR006476 3 0.5940 0.7847 0.204 0.036 0.760
#> DRR006477 3 0.6521 0.7060 0.340 0.016 0.644
#> DRR006478 1 0.4121 0.6999 0.832 0.000 0.168
#> DRR006479 1 0.2165 0.8173 0.936 0.000 0.064
#> DRR006480 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006481 1 0.4291 0.7569 0.820 0.000 0.180
#> DRR006482 1 0.4062 0.7446 0.836 0.000 0.164
#> DRR006483 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006484 1 0.3116 0.8004 0.892 0.000 0.108
#> DRR006485 2 0.5650 0.6880 0.000 0.688 0.312
#> DRR006486 1 0.0237 0.8250 0.996 0.000 0.004
#> DRR006487 1 0.5363 0.6364 0.724 0.000 0.276
#> DRR006488 2 0.0424 0.8678 0.000 0.992 0.008
#> DRR006489 1 0.0000 0.8253 1.000 0.000 0.000
#> DRR006490 1 0.2356 0.8079 0.928 0.000 0.072
#> DRR006491 1 0.2356 0.8079 0.928 0.000 0.072
#> DRR006492 1 0.5810 0.3451 0.664 0.000 0.336
#> DRR006493 1 0.5363 0.6364 0.724 0.000 0.276
#> DRR006494 1 0.0000 0.8253 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.0188 0.9164 0.000 0.996 0.004 0.000
#> DRR006375 1 0.1389 0.7141 0.952 0.000 0.000 0.048
#> DRR006376 4 0.2048 0.6841 0.064 0.000 0.008 0.928
#> DRR006377 4 0.5251 0.6325 0.032 0.016 0.212 0.740
#> DRR006378 4 0.7423 0.2567 0.000 0.404 0.168 0.428
#> DRR006379 4 0.1867 0.6845 0.072 0.000 0.000 0.928
#> DRR006380 4 0.4940 0.6092 0.000 0.128 0.096 0.776
#> DRR006381 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006382 3 0.2644 0.5035 0.000 0.060 0.908 0.032
#> DRR006383 3 0.2644 0.5035 0.000 0.060 0.908 0.032
#> DRR006384 2 0.1174 0.8993 0.000 0.968 0.020 0.012
#> DRR006385 1 0.6159 0.5511 0.672 0.000 0.196 0.132
#> DRR006386 2 0.1743 0.9011 0.000 0.940 0.056 0.004
#> DRR006387 1 0.2081 0.6984 0.916 0.000 0.000 0.084
#> DRR006388 1 0.7302 0.2088 0.500 0.000 0.332 0.168
#> DRR006389 1 0.7302 0.2088 0.500 0.000 0.332 0.168
#> DRR006390 2 0.0000 0.9168 0.000 1.000 0.000 0.000
#> DRR006391 2 0.0000 0.9168 0.000 1.000 0.000 0.000
#> DRR006392 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006393 1 0.5354 0.5620 0.712 0.000 0.056 0.232
#> DRR006394 4 0.7423 0.2567 0.000 0.404 0.168 0.428
#> DRR006395 1 0.7246 0.1379 0.448 0.000 0.144 0.408
#> DRR006396 1 0.2081 0.6984 0.916 0.000 0.000 0.084
#> DRR006397 1 0.7302 0.2088 0.500 0.000 0.332 0.168
#> DRR006398 1 0.7302 0.2088 0.500 0.000 0.332 0.168
#> DRR006399 4 0.1867 0.6845 0.072 0.000 0.000 0.928
#> DRR006400 4 0.1867 0.6845 0.072 0.000 0.000 0.928
#> DRR006401 2 0.0000 0.9168 0.000 1.000 0.000 0.000
#> DRR006402 2 0.0000 0.9168 0.000 1.000 0.000 0.000
#> DRR006403 4 0.1867 0.6845 0.072 0.000 0.000 0.928
#> DRR006404 4 0.2048 0.6841 0.064 0.000 0.008 0.928
#> DRR006405 1 0.6106 0.4141 0.604 0.000 0.064 0.332
#> DRR006406 1 0.6106 0.4141 0.604 0.000 0.064 0.332
#> DRR006407 4 0.5251 0.6325 0.032 0.016 0.212 0.740
#> DRR006408 4 0.3842 0.6385 0.000 0.128 0.036 0.836
#> DRR006409 1 0.0469 0.7243 0.988 0.000 0.012 0.000
#> DRR006410 1 0.2081 0.6984 0.916 0.000 0.000 0.084
#> DRR006411 1 0.7146 0.2283 0.516 0.000 0.336 0.148
#> DRR006412 2 0.0000 0.9168 0.000 1.000 0.000 0.000
#> DRR006413 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006414 3 0.4134 0.8099 0.260 0.000 0.740 0.000
#> DRR006415 3 0.4134 0.8099 0.260 0.000 0.740 0.000
#> DRR006416 1 0.4188 0.6782 0.824 0.000 0.112 0.064
#> DRR006417 1 0.4776 0.3915 0.624 0.000 0.376 0.000
#> DRR006418 1 0.3108 0.6988 0.872 0.000 0.112 0.016
#> DRR006419 1 0.3172 0.6633 0.840 0.000 0.160 0.000
#> DRR006420 1 0.3172 0.6633 0.840 0.000 0.160 0.000
#> DRR006421 1 0.6577 0.2157 0.540 0.004 0.384 0.072
#> DRR006422 4 0.4524 0.6590 0.000 0.028 0.204 0.768
#> DRR006423 2 0.0188 0.9164 0.000 0.996 0.004 0.000
#> DRR006424 1 0.0921 0.7204 0.972 0.000 0.000 0.028
#> DRR006425 4 0.4524 0.6590 0.000 0.028 0.204 0.768
#> DRR006426 1 0.4776 0.3915 0.624 0.000 0.376 0.000
#> DRR006427 2 0.1743 0.9011 0.000 0.940 0.056 0.004
#> DRR006428 1 0.2704 0.6880 0.876 0.000 0.124 0.000
#> DRR006429 4 0.4809 0.6483 0.012 0.016 0.220 0.752
#> DRR006430 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006432 1 0.4776 0.3915 0.624 0.000 0.376 0.000
#> DRR006433 1 0.8305 -0.1424 0.400 0.016 0.300 0.284
#> DRR006434 2 0.0188 0.9164 0.000 0.996 0.004 0.000
#> DRR006435 2 0.1743 0.9011 0.000 0.940 0.056 0.004
#> DRR006436 2 0.1743 0.9011 0.000 0.940 0.056 0.004
#> DRR006437 1 0.6159 0.5511 0.672 0.000 0.196 0.132
#> DRR006438 1 0.3311 0.6540 0.828 0.000 0.172 0.000
#> DRR006439 1 0.4897 0.4596 0.660 0.000 0.332 0.008
#> DRR006440 2 0.5339 0.5706 0.000 0.624 0.356 0.020
#> DRR006441 4 0.7423 0.2567 0.000 0.404 0.168 0.428
#> DRR006442 1 0.3074 0.6729 0.848 0.000 0.152 0.000
#> DRR006443 2 0.5339 0.5706 0.000 0.624 0.356 0.020
#> DRR006444 2 0.1743 0.9011 0.000 0.940 0.056 0.004
#> DRR006445 1 0.1474 0.7129 0.948 0.000 0.000 0.052
#> DRR006446 2 0.0000 0.9168 0.000 1.000 0.000 0.000
#> DRR006447 1 0.3108 0.6988 0.872 0.000 0.112 0.016
#> DRR006448 4 0.1867 0.6845 0.072 0.000 0.000 0.928
#> DRR006449 1 0.1474 0.7129 0.948 0.000 0.000 0.052
#> DRR006450 1 0.1474 0.7129 0.948 0.000 0.000 0.052
#> DRR006451 4 0.1867 0.6845 0.072 0.000 0.000 0.928
#> DRR006452 1 0.1474 0.7129 0.948 0.000 0.000 0.052
#> DRR006453 1 0.5321 0.5657 0.716 0.000 0.056 0.228
#> DRR006454 1 0.7238 0.2207 0.508 0.000 0.332 0.160
#> DRR006455 2 0.1743 0.9011 0.000 0.940 0.056 0.004
#> DRR006456 3 0.4134 0.8099 0.260 0.000 0.740 0.000
#> DRR006457 1 0.5099 0.3648 0.612 0.000 0.380 0.008
#> DRR006458 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006460 2 0.0000 0.9168 0.000 1.000 0.000 0.000
#> DRR006461 2 0.0188 0.9164 0.000 0.996 0.004 0.000
#> DRR006462 1 0.2081 0.6984 0.916 0.000 0.000 0.084
#> DRR006463 2 0.5339 0.5706 0.000 0.624 0.356 0.020
#> DRR006464 4 0.4809 0.6483 0.012 0.016 0.220 0.752
#> DRR006465 1 0.5254 0.5741 0.724 0.000 0.056 0.220
#> DRR006466 4 0.8310 -0.1985 0.356 0.016 0.260 0.368
#> DRR006467 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006468 2 0.1743 0.9011 0.000 0.940 0.056 0.004
#> DRR006469 4 0.7423 0.2567 0.000 0.404 0.168 0.428
#> DRR006470 1 0.4776 0.3915 0.624 0.000 0.376 0.000
#> DRR006471 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006472 4 0.8197 -0.0537 0.292 0.016 0.256 0.436
#> DRR006473 2 0.0188 0.9164 0.000 0.996 0.004 0.000
#> DRR006474 2 0.0188 0.9164 0.000 0.996 0.004 0.000
#> DRR006475 1 0.0188 0.7256 0.996 0.000 0.004 0.000
#> DRR006476 4 0.4742 0.6576 0.004 0.028 0.208 0.760
#> DRR006477 4 0.6196 0.4857 0.100 0.004 0.228 0.668
#> DRR006478 1 0.5354 0.5620 0.712 0.000 0.056 0.232
#> DRR006479 1 0.3311 0.6540 0.828 0.000 0.172 0.000
#> DRR006480 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006481 1 0.5099 0.3648 0.612 0.000 0.380 0.008
#> DRR006482 1 0.6159 0.5511 0.672 0.000 0.196 0.132
#> DRR006483 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006484 1 0.4331 0.5279 0.712 0.000 0.288 0.000
#> DRR006485 2 0.5339 0.5706 0.000 0.624 0.356 0.020
#> DRR006486 1 0.0336 0.7250 0.992 0.000 0.008 0.000
#> DRR006487 3 0.4134 0.8099 0.260 0.000 0.740 0.000
#> DRR006488 2 0.1743 0.9011 0.000 0.940 0.056 0.004
#> DRR006489 1 0.0000 0.7263 1.000 0.000 0.000 0.000
#> DRR006490 1 0.3074 0.6729 0.848 0.000 0.152 0.000
#> DRR006491 1 0.3074 0.6729 0.848 0.000 0.152 0.000
#> DRR006492 1 0.7246 0.1379 0.448 0.000 0.144 0.408
#> DRR006493 3 0.4134 0.8099 0.260 0.000 0.740 0.000
#> DRR006494 1 0.0000 0.7263 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
#> DRR006374 2 0.0162 0.8447 0.000 0.996 0.000 0.000 0.004
#> DRR006375 1 0.1270 0.6827 0.948 0.000 0.000 0.052 0.000
#> DRR006376 4 0.1697 0.6338 0.060 0.000 0.000 0.932 0.008
#> DRR006377 4 0.4589 0.6136 0.000 0.000 0.064 0.724 0.212
#> DRR006378 4 0.7010 0.2292 0.000 0.392 0.024 0.408 0.176
#> DRR006379 4 0.1544 0.6342 0.068 0.000 0.000 0.932 0.000
#> DRR006380 4 0.4660 0.5414 0.000 0.000 0.080 0.728 0.192
#> DRR006381 1 0.0000 0.6971 1.000 0.000 0.000 0.000 0.000
#> DRR006382 3 0.5639 0.5531 0.000 0.060 0.616 0.020 0.304
#> DRR006383 3 0.5639 0.5531 0.000 0.060 0.616 0.020 0.304
#> DRR006384 2 0.3423 0.7660 0.000 0.856 0.068 0.016 0.060
#> DRR006385 1 0.6819 0.5212 0.600 0.000 0.176 0.136 0.088
#> DRR006386 2 0.3210 0.7784 0.000 0.788 0.000 0.000 0.212
#> DRR006387 1 0.1851 0.6688 0.912 0.000 0.000 0.088 0.000
#> DRR006388 1 0.8287 0.1648 0.340 0.000 0.320 0.176 0.164
#> DRR006389 1 0.8287 0.1648 0.340 0.000 0.320 0.176 0.164
#> DRR006390 2 0.0000 0.8452 0.000 1.000 0.000 0.000 0.000
#> DRR006391 2 0.0000 0.8452 0.000 1.000 0.000 0.000 0.000
#> DRR006392 1 0.0162 0.6966 0.996 0.000 0.000 0.004 0.000
#> DRR006393 1 0.5178 0.5525 0.692 0.000 0.056 0.232 0.020
#> DRR006394 4 0.7010 0.2292 0.000 0.392 0.024 0.408 0.176
#> DRR006395 4 0.7187 -0.0869 0.396 0.000 0.136 0.416 0.052
#> DRR006396 1 0.1851 0.6688 0.912 0.000 0.000 0.088 0.000
#> DRR006397 1 0.8287 0.1648 0.340 0.000 0.320 0.176 0.164
#> DRR006398 1 0.8287 0.1648 0.340 0.000 0.320 0.176 0.164
#> DRR006399 4 0.1544 0.6342 0.068 0.000 0.000 0.932 0.000
#> DRR006400 4 0.1544 0.6342 0.068 0.000 0.000 0.932 0.000
#> DRR006401 2 0.0000 0.8452 0.000 1.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.8452 0.000 1.000 0.000 0.000 0.000
#> DRR006403 4 0.1544 0.6342 0.068 0.000 0.000 0.932 0.000
#> DRR006404 4 0.1697 0.6338 0.060 0.000 0.000 0.932 0.008
#> DRR006405 1 0.5731 0.3972 0.592 0.000 0.048 0.332 0.028
#> DRR006406 1 0.5731 0.3972 0.592 0.000 0.048 0.332 0.028
#> DRR006407 4 0.4589 0.6136 0.000 0.000 0.064 0.724 0.212
#> DRR006408 4 0.4083 0.5751 0.000 0.000 0.080 0.788 0.132
#> DRR006409 1 0.0693 0.6956 0.980 0.000 0.008 0.000 0.012
#> DRR006410 1 0.1851 0.6688 0.912 0.000 0.000 0.088 0.000
#> DRR006411 1 0.8213 0.1795 0.356 0.000 0.320 0.156 0.168
#> DRR006412 2 0.0000 0.8452 0.000 1.000 0.000 0.000 0.000
#> DRR006413 1 0.0000 0.6971 1.000 0.000 0.000 0.000 0.000
#> DRR006414 3 0.2984 0.8358 0.124 0.000 0.856 0.004 0.016
#> DRR006415 3 0.2984 0.8358 0.124 0.000 0.856 0.004 0.016
#> DRR006416 1 0.4866 0.6426 0.768 0.000 0.112 0.072 0.048
#> DRR006417 1 0.6762 0.3107 0.452 0.000 0.336 0.008 0.204
#> DRR006418 1 0.4016 0.6601 0.816 0.000 0.112 0.024 0.048
#> DRR006419 1 0.4933 0.5672 0.688 0.000 0.236 0.000 0.076
#> DRR006420 1 0.4933 0.5672 0.688 0.000 0.236 0.000 0.076
#> DRR006421 1 0.7814 0.1243 0.360 0.000 0.348 0.072 0.220
#> DRR006422 4 0.4193 0.6205 0.000 0.000 0.040 0.748 0.212
#> DRR006423 2 0.0162 0.8447 0.000 0.996 0.000 0.000 0.004
#> DRR006424 1 0.0880 0.6899 0.968 0.000 0.000 0.032 0.000
#> DRR006425 4 0.4193 0.6205 0.000 0.000 0.040 0.748 0.212
#> DRR006426 1 0.6762 0.3107 0.452 0.000 0.336 0.008 0.204
#> DRR006427 2 0.3210 0.7784 0.000 0.788 0.000 0.000 0.212
#> DRR006428 1 0.3878 0.5970 0.748 0.000 0.236 0.000 0.016
#> DRR006429 4 0.4450 0.6177 0.004 0.000 0.044 0.736 0.216
#> DRR006430 1 0.0162 0.6966 0.996 0.000 0.000 0.004 0.000
#> DRR006431 1 0.0000 0.6971 1.000 0.000 0.000 0.000 0.000
#> DRR006432 1 0.6762 0.3107 0.452 0.000 0.336 0.008 0.204
#> DRR006433 4 0.8533 -0.0508 0.280 0.000 0.244 0.288 0.188
#> DRR006434 2 0.0162 0.8447 0.000 0.996 0.000 0.000 0.004
#> DRR006435 2 0.3210 0.7784 0.000 0.788 0.000 0.000 0.212
#> DRR006436 2 0.3210 0.7784 0.000 0.788 0.000 0.000 0.212
#> DRR006437 1 0.6819 0.5212 0.600 0.000 0.176 0.136 0.088
#> DRR006438 1 0.5028 0.5483 0.668 0.000 0.260 0.000 0.072
#> DRR006439 1 0.6452 0.3568 0.500 0.000 0.340 0.008 0.152
#> DRR006440 2 0.5619 0.3994 0.000 0.516 0.064 0.004 0.416
#> DRR006441 4 0.7010 0.2292 0.000 0.392 0.024 0.408 0.176
#> DRR006442 1 0.4132 0.5757 0.720 0.000 0.260 0.000 0.020
#> DRR006443 2 0.5619 0.3994 0.000 0.516 0.064 0.004 0.416
#> DRR006444 2 0.3210 0.7784 0.000 0.788 0.000 0.000 0.212
#> DRR006445 1 0.1341 0.6814 0.944 0.000 0.000 0.056 0.000
#> DRR006446 2 0.0000 0.8452 0.000 1.000 0.000 0.000 0.000
#> DRR006447 1 0.4016 0.6601 0.816 0.000 0.112 0.024 0.048
#> DRR006448 4 0.1544 0.6342 0.068 0.000 0.000 0.932 0.000
#> DRR006449 1 0.1341 0.6814 0.944 0.000 0.000 0.056 0.000
#> DRR006450 1 0.1341 0.6814 0.944 0.000 0.000 0.056 0.000
#> DRR006451 4 0.1544 0.6342 0.068 0.000 0.000 0.932 0.000
#> DRR006452 1 0.1341 0.6814 0.944 0.000 0.000 0.056 0.000
#> DRR006453 1 0.5212 0.5554 0.692 0.000 0.060 0.228 0.020
#> DRR006454 1 0.8252 0.1741 0.348 0.000 0.320 0.168 0.164
#> DRR006455 2 0.3210 0.7784 0.000 0.788 0.000 0.000 0.212
#> DRR006456 3 0.2984 0.8358 0.124 0.000 0.856 0.004 0.016
#> DRR006457 1 0.6861 0.2454 0.424 0.000 0.368 0.012 0.196
#> DRR006458 1 0.0000 0.6971 1.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.6971 1.000 0.000 0.000 0.000 0.000
#> DRR006460 2 0.0000 0.8452 0.000 1.000 0.000 0.000 0.000
#> DRR006461 2 0.0162 0.8447 0.000 0.996 0.000 0.000 0.004
#> DRR006462 1 0.1851 0.6688 0.912 0.000 0.000 0.088 0.000
#> DRR006463 2 0.5619 0.3994 0.000 0.516 0.064 0.004 0.416
#> DRR006464 4 0.4450 0.6177 0.004 0.000 0.044 0.736 0.216
#> DRR006465 1 0.5094 0.5664 0.704 0.000 0.056 0.220 0.020
#> DRR006466 4 0.8349 0.1073 0.248 0.000 0.204 0.372 0.176
#> DRR006467 1 0.0162 0.6966 0.996 0.000 0.000 0.004 0.000
#> DRR006468 2 0.3210 0.7784 0.000 0.788 0.000 0.000 0.212
#> DRR006469 4 0.7010 0.2292 0.000 0.392 0.024 0.408 0.176
#> DRR006470 1 0.6762 0.3107 0.452 0.000 0.336 0.008 0.204
#> DRR006471 1 0.0162 0.6967 0.996 0.000 0.004 0.000 0.000
#> DRR006472 4 0.8125 0.2086 0.188 0.000 0.184 0.432 0.196
#> DRR006473 2 0.0162 0.8447 0.000 0.996 0.000 0.000 0.004
#> DRR006474 2 0.0162 0.8447 0.000 0.996 0.000 0.000 0.004
#> DRR006475 1 0.0324 0.6962 0.992 0.000 0.004 0.000 0.004
#> DRR006476 4 0.4119 0.6208 0.000 0.000 0.036 0.752 0.212
#> DRR006477 4 0.6411 0.4862 0.064 0.000 0.120 0.632 0.184
#> DRR006478 1 0.5178 0.5525 0.692 0.000 0.056 0.232 0.020
#> DRR006479 1 0.5028 0.5483 0.668 0.000 0.260 0.000 0.072
#> DRR006480 1 0.0000 0.6971 1.000 0.000 0.000 0.000 0.000
#> DRR006481 1 0.6876 0.2473 0.424 0.000 0.364 0.012 0.200
#> DRR006482 1 0.6819 0.5212 0.600 0.000 0.176 0.136 0.088
#> DRR006483 1 0.0162 0.6967 0.996 0.000 0.004 0.000 0.000
#> DRR006484 1 0.6162 0.4081 0.532 0.000 0.308 0.000 0.160
#> DRR006485 2 0.5619 0.3994 0.000 0.516 0.064 0.004 0.416
#> DRR006486 1 0.0671 0.6948 0.980 0.000 0.016 0.000 0.004
#> DRR006487 3 0.2984 0.8358 0.124 0.000 0.856 0.004 0.016
#> DRR006488 2 0.3210 0.7784 0.000 0.788 0.000 0.000 0.212
#> DRR006489 1 0.0162 0.6966 0.996 0.000 0.000 0.004 0.000
#> DRR006490 1 0.4132 0.5757 0.720 0.000 0.260 0.000 0.020
#> DRR006491 1 0.4132 0.5757 0.720 0.000 0.260 0.000 0.020
#> DRR006492 4 0.7187 -0.0869 0.396 0.000 0.136 0.416 0.052
#> DRR006493 3 0.2984 0.8358 0.124 0.000 0.856 0.004 0.016
#> DRR006494 1 0.0000 0.6971 1.000 0.000 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
#> DRR006374 5 0.3747 0.73517 0.000 0.396 0.000 0.000 0.604 0.000
#> DRR006375 1 0.0865 0.75749 0.964 0.000 0.000 0.036 0.000 0.000
#> DRR006376 4 0.1732 0.63541 0.072 0.000 0.004 0.920 0.000 0.004
#> DRR006377 4 0.5573 0.54107 0.000 0.208 0.020 0.612 0.000 0.160
#> DRR006378 2 0.5007 0.58885 0.000 0.636 0.012 0.272 0.000 0.080
#> DRR006379 4 0.1757 0.63584 0.076 0.000 0.008 0.916 0.000 0.000
#> DRR006380 4 0.6508 0.44131 0.000 0.172 0.132 0.560 0.000 0.136
#> DRR006381 1 0.0632 0.76491 0.976 0.000 0.000 0.000 0.000 0.024
#> DRR006382 3 0.2982 0.51803 0.000 0.152 0.828 0.008 0.000 0.012
#> DRR006383 3 0.2982 0.51803 0.000 0.152 0.828 0.008 0.000 0.012
#> DRR006384 5 0.5770 0.53288 0.000 0.416 0.104 0.008 0.464 0.008
#> DRR006385 1 0.5268 0.23119 0.572 0.000 0.000 0.128 0.000 0.300
#> DRR006386 5 0.0000 0.62664 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006387 1 0.1501 0.74099 0.924 0.000 0.000 0.076 0.000 0.000
#> DRR006388 6 0.5528 0.68128 0.180 0.040 0.004 0.120 0.000 0.656
#> DRR006389 6 0.5528 0.68128 0.180 0.040 0.004 0.120 0.000 0.656
#> DRR006390 5 0.3737 0.73700 0.000 0.392 0.000 0.000 0.608 0.000
#> DRR006391 5 0.3737 0.73700 0.000 0.392 0.000 0.000 0.608 0.000
#> DRR006392 1 0.0547 0.76558 0.980 0.000 0.000 0.000 0.000 0.020
#> DRR006393 1 0.4632 0.54461 0.688 0.004 0.000 0.216 0.000 0.092
#> DRR006394 2 0.5007 0.58885 0.000 0.636 0.012 0.272 0.000 0.080
#> DRR006395 4 0.6317 -0.07365 0.352 0.000 0.012 0.392 0.000 0.244
#> DRR006396 1 0.1501 0.74099 0.924 0.000 0.000 0.076 0.000 0.000
#> DRR006397 6 0.5528 0.68128 0.180 0.040 0.004 0.120 0.000 0.656
#> DRR006398 6 0.5528 0.68128 0.180 0.040 0.004 0.120 0.000 0.656
#> DRR006399 4 0.1757 0.63584 0.076 0.000 0.008 0.916 0.000 0.000
#> DRR006400 4 0.1757 0.63584 0.076 0.000 0.008 0.916 0.000 0.000
#> DRR006401 5 0.3737 0.73700 0.000 0.392 0.000 0.000 0.608 0.000
#> DRR006402 5 0.3737 0.73700 0.000 0.392 0.000 0.000 0.608 0.000
#> DRR006403 4 0.1757 0.63584 0.076 0.000 0.008 0.916 0.000 0.000
#> DRR006404 4 0.1732 0.63541 0.072 0.000 0.004 0.920 0.000 0.004
#> DRR006405 1 0.5172 0.42353 0.592 0.004 0.004 0.316 0.000 0.084
#> DRR006406 1 0.5172 0.42353 0.592 0.004 0.004 0.316 0.000 0.084
#> DRR006407 4 0.5573 0.54107 0.000 0.208 0.020 0.612 0.000 0.160
#> DRR006408 4 0.6202 0.48192 0.000 0.144 0.120 0.600 0.000 0.136
#> DRR006409 1 0.1700 0.73251 0.916 0.000 0.000 0.004 0.000 0.080
#> DRR006410 1 0.1501 0.74099 0.924 0.000 0.000 0.076 0.000 0.000
#> DRR006411 6 0.5518 0.68787 0.192 0.040 0.008 0.100 0.000 0.660
#> DRR006412 5 0.3737 0.73700 0.000 0.392 0.000 0.000 0.608 0.000
#> DRR006413 1 0.0632 0.76491 0.976 0.000 0.000 0.000 0.000 0.024
#> DRR006414 3 0.3659 0.81717 0.000 0.000 0.636 0.000 0.000 0.364
#> DRR006415 3 0.3659 0.81717 0.000 0.000 0.636 0.000 0.000 0.364
#> DRR006416 1 0.4723 0.37338 0.636 0.004 0.000 0.064 0.000 0.296
#> DRR006417 6 0.3298 0.71532 0.236 0.000 0.008 0.000 0.000 0.756
#> DRR006418 1 0.4025 0.39837 0.668 0.004 0.000 0.016 0.000 0.312
#> DRR006419 6 0.4177 0.39449 0.468 0.000 0.012 0.000 0.000 0.520
#> DRR006420 6 0.4177 0.39449 0.468 0.000 0.012 0.000 0.000 0.520
#> DRR006421 6 0.5206 0.63581 0.192 0.020 0.028 0.068 0.000 0.692
#> DRR006422 4 0.5354 0.53061 0.000 0.244 0.028 0.632 0.000 0.096
#> DRR006423 5 0.3747 0.73517 0.000 0.396 0.000 0.000 0.604 0.000
#> DRR006424 1 0.0458 0.76196 0.984 0.000 0.000 0.016 0.000 0.000
#> DRR006425 4 0.5354 0.53061 0.000 0.244 0.028 0.632 0.000 0.096
#> DRR006426 6 0.3298 0.71532 0.236 0.000 0.008 0.000 0.000 0.756
#> DRR006427 5 0.0000 0.62664 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006428 1 0.4184 0.04592 0.576 0.000 0.016 0.000 0.000 0.408
#> DRR006429 4 0.5526 0.53431 0.000 0.232 0.024 0.616 0.000 0.128
#> DRR006430 1 0.0547 0.76558 0.980 0.000 0.000 0.000 0.000 0.020
#> DRR006431 1 0.0632 0.76491 0.976 0.000 0.000 0.000 0.000 0.024
#> DRR006432 6 0.3298 0.71532 0.236 0.000 0.008 0.000 0.000 0.756
#> DRR006433 6 0.7120 0.31266 0.136 0.092 0.028 0.236 0.000 0.508
#> DRR006434 5 0.3747 0.73517 0.000 0.396 0.000 0.000 0.604 0.000
#> DRR006435 5 0.0000 0.62664 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006436 5 0.0000 0.62664 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006437 1 0.5268 0.23119 0.572 0.000 0.000 0.128 0.000 0.300
#> DRR006438 6 0.4129 0.46624 0.424 0.000 0.012 0.000 0.000 0.564
#> DRR006439 6 0.4230 0.61474 0.324 0.000 0.024 0.004 0.000 0.648
#> DRR006440 2 0.3241 0.60766 0.000 0.848 0.080 0.000 0.032 0.040
#> DRR006441 2 0.5007 0.58885 0.000 0.636 0.012 0.272 0.000 0.080
#> DRR006442 1 0.4763 -0.04880 0.536 0.000 0.052 0.000 0.000 0.412
#> DRR006443 2 0.3241 0.60766 0.000 0.848 0.080 0.000 0.032 0.040
#> DRR006444 5 0.0000 0.62664 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006445 1 0.0937 0.75640 0.960 0.000 0.000 0.040 0.000 0.000
#> DRR006446 5 0.3737 0.73700 0.000 0.392 0.000 0.000 0.608 0.000
#> DRR006447 1 0.4025 0.39837 0.668 0.004 0.000 0.016 0.000 0.312
#> DRR006448 4 0.1757 0.63584 0.076 0.000 0.008 0.916 0.000 0.000
#> DRR006449 1 0.0937 0.75640 0.960 0.000 0.000 0.040 0.000 0.000
#> DRR006450 1 0.0937 0.75640 0.960 0.000 0.000 0.040 0.000 0.000
#> DRR006451 4 0.1757 0.63584 0.076 0.000 0.008 0.916 0.000 0.000
#> DRR006452 1 0.0937 0.75640 0.960 0.000 0.000 0.040 0.000 0.000
#> DRR006453 1 0.4694 0.54083 0.684 0.004 0.000 0.212 0.000 0.100
#> DRR006454 6 0.5507 0.68489 0.188 0.040 0.004 0.112 0.000 0.656
#> DRR006455 5 0.0000 0.62664 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006456 3 0.3659 0.81717 0.000 0.000 0.636 0.000 0.000 0.364
#> DRR006457 6 0.3351 0.63949 0.168 0.000 0.028 0.004 0.000 0.800
#> DRR006458 1 0.0632 0.76491 0.976 0.000 0.000 0.000 0.000 0.024
#> DRR006459 1 0.0632 0.76491 0.976 0.000 0.000 0.000 0.000 0.024
#> DRR006460 5 0.3737 0.73700 0.000 0.392 0.000 0.000 0.608 0.000
#> DRR006461 5 0.3747 0.73517 0.000 0.396 0.000 0.000 0.604 0.000
#> DRR006462 1 0.1501 0.74099 0.924 0.000 0.000 0.076 0.000 0.000
#> DRR006463 2 0.3241 0.60766 0.000 0.848 0.080 0.000 0.032 0.040
#> DRR006464 4 0.5526 0.53431 0.000 0.232 0.024 0.616 0.000 0.128
#> DRR006465 1 0.4552 0.55895 0.700 0.004 0.000 0.204 0.000 0.092
#> DRR006466 6 0.7296 -0.00126 0.104 0.112 0.028 0.320 0.000 0.436
#> DRR006467 1 0.0547 0.76558 0.980 0.000 0.000 0.000 0.000 0.020
#> DRR006468 5 0.0000 0.62664 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006469 2 0.5007 0.58885 0.000 0.636 0.012 0.272 0.000 0.080
#> DRR006470 6 0.3298 0.71532 0.236 0.000 0.008 0.000 0.000 0.756
#> DRR006471 1 0.0790 0.76170 0.968 0.000 0.000 0.000 0.000 0.032
#> DRR006472 4 0.6457 0.24704 0.060 0.068 0.020 0.428 0.000 0.424
#> DRR006473 5 0.3747 0.73517 0.000 0.396 0.000 0.000 0.604 0.000
#> DRR006474 5 0.3747 0.73517 0.000 0.396 0.000 0.000 0.604 0.000
#> DRR006475 1 0.0865 0.75890 0.964 0.000 0.000 0.000 0.000 0.036
#> DRR006476 4 0.5475 0.53403 0.000 0.244 0.028 0.620 0.000 0.108
#> DRR006477 4 0.6976 0.47169 0.048 0.132 0.044 0.504 0.000 0.272
#> DRR006478 1 0.4632 0.54461 0.688 0.004 0.000 0.216 0.000 0.092
#> DRR006479 6 0.4129 0.46624 0.424 0.000 0.012 0.000 0.000 0.564
#> DRR006480 1 0.0632 0.76491 0.976 0.000 0.000 0.000 0.000 0.024
#> DRR006481 6 0.3274 0.64161 0.168 0.000 0.024 0.004 0.000 0.804
#> DRR006482 1 0.5268 0.23119 0.572 0.000 0.000 0.128 0.000 0.300
#> DRR006483 1 0.0790 0.76170 0.968 0.000 0.000 0.000 0.000 0.032
#> DRR006484 6 0.3564 0.67100 0.264 0.000 0.012 0.000 0.000 0.724
#> DRR006485 2 0.3241 0.60766 0.000 0.848 0.080 0.000 0.032 0.040
#> DRR006486 1 0.1219 0.74863 0.948 0.000 0.004 0.000 0.000 0.048
#> DRR006487 3 0.3659 0.81717 0.000 0.000 0.636 0.000 0.000 0.364
#> DRR006488 5 0.0000 0.62664 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006489 1 0.0547 0.76558 0.980 0.000 0.000 0.000 0.000 0.020
#> DRR006490 1 0.4763 -0.04880 0.536 0.000 0.052 0.000 0.000 0.412
#> DRR006491 1 0.4763 -0.04880 0.536 0.000 0.052 0.000 0.000 0.412
#> DRR006492 4 0.6317 -0.07365 0.352 0.000 0.012 0.392 0.000 0.244
#> DRR006493 3 0.3659 0.81717 0.000 0.000 0.636 0.000 0.000 0.364
#> DRR006494 1 0.0632 0.76491 0.976 0.000 0.000 0.000 0.000 0.024
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.977 0.991 0.4200 0.579 0.579
#> 3 3 0.584 0.735 0.858 0.5433 0.742 0.562
#> 4 4 0.608 0.639 0.787 0.1328 0.819 0.527
#> 5 5 0.684 0.728 0.815 0.0747 0.928 0.724
#> 6 6 0.749 0.666 0.755 0.0429 0.927 0.670
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
#> DRR006374 2 0.000 0.980 0.000 1.000
#> DRR006375 1 0.000 0.995 1.000 0.000
#> DRR006376 1 0.000 0.995 1.000 0.000
#> DRR006377 1 0.000 0.995 1.000 0.000
#> DRR006378 2 0.000 0.980 0.000 1.000
#> DRR006379 1 0.000 0.995 1.000 0.000
#> DRR006380 2 0.000 0.980 0.000 1.000
#> DRR006381 1 0.000 0.995 1.000 0.000
#> DRR006382 2 0.000 0.980 0.000 1.000
#> DRR006383 1 0.518 0.864 0.884 0.116
#> DRR006384 2 0.000 0.980 0.000 1.000
#> DRR006385 1 0.000 0.995 1.000 0.000
#> DRR006386 2 0.000 0.980 0.000 1.000
#> DRR006387 1 0.000 0.995 1.000 0.000
#> DRR006388 1 0.000 0.995 1.000 0.000
#> DRR006389 1 0.000 0.995 1.000 0.000
#> DRR006390 2 0.000 0.980 0.000 1.000
#> DRR006391 2 0.000 0.980 0.000 1.000
#> DRR006392 1 0.000 0.995 1.000 0.000
#> DRR006393 1 0.000 0.995 1.000 0.000
#> DRR006394 2 0.000 0.980 0.000 1.000
#> DRR006395 1 0.000 0.995 1.000 0.000
#> DRR006396 1 0.000 0.995 1.000 0.000
#> DRR006397 1 0.000 0.995 1.000 0.000
#> DRR006398 1 0.000 0.995 1.000 0.000
#> DRR006399 1 0.000 0.995 1.000 0.000
#> DRR006400 1 0.000 0.995 1.000 0.000
#> DRR006401 2 0.000 0.980 0.000 1.000
#> DRR006402 2 0.000 0.980 0.000 1.000
#> DRR006403 1 0.000 0.995 1.000 0.000
#> DRR006404 1 0.000 0.995 1.000 0.000
#> DRR006405 1 0.000 0.995 1.000 0.000
#> DRR006406 1 0.000 0.995 1.000 0.000
#> DRR006407 1 0.000 0.995 1.000 0.000
#> DRR006408 2 0.000 0.980 0.000 1.000
#> DRR006409 1 0.000 0.995 1.000 0.000
#> DRR006410 1 0.000 0.995 1.000 0.000
#> DRR006411 1 0.000 0.995 1.000 0.000
#> DRR006412 2 0.000 0.980 0.000 1.000
#> DRR006413 1 0.000 0.995 1.000 0.000
#> DRR006414 1 0.000 0.995 1.000 0.000
#> DRR006415 1 0.000 0.995 1.000 0.000
#> DRR006416 1 0.000 0.995 1.000 0.000
#> DRR006417 1 0.000 0.995 1.000 0.000
#> DRR006418 1 0.000 0.995 1.000 0.000
#> DRR006419 1 0.000 0.995 1.000 0.000
#> DRR006420 1 0.000 0.995 1.000 0.000
#> DRR006421 1 0.000 0.995 1.000 0.000
#> DRR006422 1 0.861 0.595 0.716 0.284
#> DRR006423 2 0.000 0.980 0.000 1.000
#> DRR006424 1 0.000 0.995 1.000 0.000
#> DRR006425 2 0.000 0.980 0.000 1.000
#> DRR006426 1 0.000 0.995 1.000 0.000
#> DRR006427 2 0.000 0.980 0.000 1.000
#> DRR006428 1 0.000 0.995 1.000 0.000
#> DRR006429 2 0.995 0.158 0.460 0.540
#> DRR006430 1 0.000 0.995 1.000 0.000
#> DRR006431 1 0.000 0.995 1.000 0.000
#> DRR006432 1 0.000 0.995 1.000 0.000
#> DRR006433 1 0.000 0.995 1.000 0.000
#> DRR006434 2 0.000 0.980 0.000 1.000
#> DRR006435 2 0.000 0.980 0.000 1.000
#> DRR006436 2 0.000 0.980 0.000 1.000
#> DRR006437 1 0.000 0.995 1.000 0.000
#> DRR006438 1 0.000 0.995 1.000 0.000
#> DRR006439 1 0.000 0.995 1.000 0.000
#> DRR006440 2 0.000 0.980 0.000 1.000
#> DRR006441 2 0.000 0.980 0.000 1.000
#> DRR006442 1 0.000 0.995 1.000 0.000
#> DRR006443 2 0.000 0.980 0.000 1.000
#> DRR006444 2 0.000 0.980 0.000 1.000
#> DRR006445 1 0.000 0.995 1.000 0.000
#> DRR006446 2 0.000 0.980 0.000 1.000
#> DRR006447 1 0.000 0.995 1.000 0.000
#> DRR006448 1 0.000 0.995 1.000 0.000
#> DRR006449 1 0.000 0.995 1.000 0.000
#> DRR006450 1 0.000 0.995 1.000 0.000
#> DRR006451 1 0.000 0.995 1.000 0.000
#> DRR006452 1 0.000 0.995 1.000 0.000
#> DRR006453 1 0.000 0.995 1.000 0.000
#> DRR006454 1 0.000 0.995 1.000 0.000
#> DRR006455 2 0.000 0.980 0.000 1.000
#> DRR006456 1 0.000 0.995 1.000 0.000
#> DRR006457 1 0.000 0.995 1.000 0.000
#> DRR006458 1 0.000 0.995 1.000 0.000
#> DRR006459 1 0.000 0.995 1.000 0.000
#> DRR006460 2 0.000 0.980 0.000 1.000
#> DRR006461 2 0.000 0.980 0.000 1.000
#> DRR006462 1 0.000 0.995 1.000 0.000
#> DRR006463 2 0.000 0.980 0.000 1.000
#> DRR006464 1 0.000 0.995 1.000 0.000
#> DRR006465 1 0.000 0.995 1.000 0.000
#> DRR006466 1 0.000 0.995 1.000 0.000
#> DRR006467 1 0.000 0.995 1.000 0.000
#> DRR006468 2 0.000 0.980 0.000 1.000
#> DRR006469 2 0.000 0.980 0.000 1.000
#> DRR006470 1 0.000 0.995 1.000 0.000
#> DRR006471 1 0.000 0.995 1.000 0.000
#> DRR006472 1 0.000 0.995 1.000 0.000
#> DRR006473 2 0.000 0.980 0.000 1.000
#> DRR006474 2 0.000 0.980 0.000 1.000
#> DRR006475 1 0.000 0.995 1.000 0.000
#> DRR006476 2 0.760 0.714 0.220 0.780
#> DRR006477 1 0.000 0.995 1.000 0.000
#> DRR006478 1 0.000 0.995 1.000 0.000
#> DRR006479 1 0.000 0.995 1.000 0.000
#> DRR006480 1 0.000 0.995 1.000 0.000
#> DRR006481 1 0.000 0.995 1.000 0.000
#> DRR006482 1 0.000 0.995 1.000 0.000
#> DRR006483 1 0.000 0.995 1.000 0.000
#> DRR006484 1 0.000 0.995 1.000 0.000
#> DRR006485 2 0.000 0.980 0.000 1.000
#> DRR006486 1 0.000 0.995 1.000 0.000
#> DRR006487 1 0.000 0.995 1.000 0.000
#> DRR006488 2 0.000 0.980 0.000 1.000
#> DRR006489 1 0.000 0.995 1.000 0.000
#> DRR006490 1 0.000 0.995 1.000 0.000
#> DRR006491 1 0.000 0.995 1.000 0.000
#> DRR006492 1 0.000 0.995 1.000 0.000
#> DRR006493 1 0.000 0.995 1.000 0.000
#> DRR006494 1 0.000 0.995 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.0237 0.912 0.000 0.996 0.004
#> DRR006375 1 0.0000 0.832 1.000 0.000 0.000
#> DRR006376 1 0.5016 0.692 0.760 0.000 0.240
#> DRR006377 3 0.1525 0.734 0.032 0.004 0.964
#> DRR006378 2 0.2796 0.881 0.000 0.908 0.092
#> DRR006379 1 0.5016 0.692 0.760 0.000 0.240
#> DRR006380 2 0.5591 0.699 0.000 0.696 0.304
#> DRR006381 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006382 2 0.3941 0.845 0.000 0.844 0.156
#> DRR006383 3 0.2187 0.736 0.028 0.024 0.948
#> DRR006384 2 0.0000 0.912 0.000 1.000 0.000
#> DRR006385 1 0.1411 0.826 0.964 0.000 0.036
#> DRR006386 2 0.1289 0.904 0.000 0.968 0.032
#> DRR006387 1 0.1643 0.824 0.956 0.000 0.044
#> DRR006388 1 0.6274 0.306 0.544 0.000 0.456
#> DRR006389 1 0.6274 0.306 0.544 0.000 0.456
#> DRR006390 2 0.0237 0.912 0.000 0.996 0.004
#> DRR006391 2 0.0237 0.912 0.000 0.996 0.004
#> DRR006392 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006393 1 0.0000 0.832 1.000 0.000 0.000
#> DRR006394 2 0.6095 0.561 0.000 0.608 0.392
#> DRR006395 1 0.5016 0.692 0.760 0.000 0.240
#> DRR006396 1 0.1643 0.824 0.956 0.000 0.044
#> DRR006397 1 0.6274 0.306 0.544 0.000 0.456
#> DRR006398 1 0.6274 0.306 0.544 0.000 0.456
#> DRR006399 1 0.4702 0.713 0.788 0.000 0.212
#> DRR006400 1 0.4702 0.713 0.788 0.000 0.212
#> DRR006401 2 0.0000 0.912 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.912 0.000 1.000 0.000
#> DRR006403 1 0.4702 0.713 0.788 0.000 0.212
#> DRR006404 1 0.5016 0.692 0.760 0.000 0.240
#> DRR006405 1 0.5016 0.692 0.760 0.000 0.240
#> DRR006406 1 0.5016 0.692 0.760 0.000 0.240
#> DRR006407 3 0.4912 0.596 0.196 0.008 0.796
#> DRR006408 2 0.6282 0.561 0.004 0.612 0.384
#> DRR006409 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006410 1 0.1163 0.828 0.972 0.000 0.028
#> DRR006411 3 0.4887 0.568 0.228 0.000 0.772
#> DRR006412 2 0.0237 0.912 0.000 0.996 0.004
#> DRR006413 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006414 3 0.6192 0.584 0.420 0.000 0.580
#> DRR006415 3 0.5058 0.724 0.244 0.000 0.756
#> DRR006416 3 0.5882 0.373 0.348 0.000 0.652
#> DRR006417 3 0.5058 0.724 0.244 0.000 0.756
#> DRR006418 1 0.6260 -0.277 0.552 0.000 0.448
#> DRR006419 3 0.6095 0.616 0.392 0.000 0.608
#> DRR006420 3 0.6095 0.616 0.392 0.000 0.608
#> DRR006421 3 0.2066 0.746 0.060 0.000 0.940
#> DRR006422 3 0.4784 0.529 0.004 0.200 0.796
#> DRR006423 2 0.0237 0.912 0.000 0.996 0.004
#> DRR006424 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006425 2 0.5529 0.703 0.000 0.704 0.296
#> DRR006426 3 0.2356 0.748 0.072 0.000 0.928
#> DRR006427 2 0.1289 0.904 0.000 0.968 0.032
#> DRR006428 3 0.6225 0.564 0.432 0.000 0.568
#> DRR006429 3 0.1525 0.723 0.004 0.032 0.964
#> DRR006430 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006431 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006432 3 0.2066 0.746 0.060 0.000 0.940
#> DRR006433 3 0.1525 0.734 0.032 0.004 0.964
#> DRR006434 2 0.0237 0.912 0.000 0.996 0.004
#> DRR006435 2 0.1289 0.904 0.000 0.968 0.032
#> DRR006436 2 0.1289 0.904 0.000 0.968 0.032
#> DRR006437 1 0.1643 0.824 0.956 0.000 0.044
#> DRR006438 3 0.6215 0.571 0.428 0.000 0.572
#> DRR006439 3 0.6095 0.616 0.392 0.000 0.608
#> DRR006440 2 0.3941 0.845 0.000 0.844 0.156
#> DRR006441 2 0.5138 0.760 0.000 0.748 0.252
#> DRR006442 3 0.6180 0.589 0.416 0.000 0.584
#> DRR006443 2 0.3941 0.845 0.000 0.844 0.156
#> DRR006444 2 0.1289 0.904 0.000 0.968 0.032
#> DRR006445 1 0.1643 0.824 0.956 0.000 0.044
#> DRR006446 2 0.0237 0.912 0.000 0.996 0.004
#> DRR006447 1 0.1289 0.827 0.968 0.000 0.032
#> DRR006448 1 0.4702 0.713 0.788 0.000 0.212
#> DRR006449 1 0.1031 0.829 0.976 0.000 0.024
#> DRR006450 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006451 1 0.5016 0.692 0.760 0.000 0.240
#> DRR006452 1 0.0000 0.832 1.000 0.000 0.000
#> DRR006453 1 0.0000 0.832 1.000 0.000 0.000
#> DRR006454 3 0.4750 0.584 0.216 0.000 0.784
#> DRR006455 2 0.1289 0.904 0.000 0.968 0.032
#> DRR006456 3 0.4291 0.749 0.152 0.008 0.840
#> DRR006457 3 0.3816 0.752 0.148 0.000 0.852
#> DRR006458 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006459 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006460 2 0.0424 0.910 0.000 0.992 0.008
#> DRR006461 2 0.0237 0.912 0.000 0.996 0.004
#> DRR006462 1 0.1643 0.824 0.956 0.000 0.044
#> DRR006463 2 0.3941 0.845 0.000 0.844 0.156
#> DRR006464 3 0.1525 0.723 0.004 0.032 0.964
#> DRR006465 1 0.0000 0.832 1.000 0.000 0.000
#> DRR006466 3 0.1525 0.723 0.004 0.032 0.964
#> DRR006467 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006468 2 0.1289 0.904 0.000 0.968 0.032
#> DRR006469 2 0.4235 0.832 0.000 0.824 0.176
#> DRR006470 3 0.5465 0.705 0.288 0.000 0.712
#> DRR006471 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006472 3 0.1964 0.745 0.056 0.000 0.944
#> DRR006473 2 0.0237 0.912 0.000 0.996 0.004
#> DRR006474 2 0.0237 0.912 0.000 0.996 0.004
#> DRR006475 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006476 3 0.4733 0.535 0.004 0.196 0.800
#> DRR006477 3 0.1585 0.733 0.028 0.008 0.964
#> DRR006478 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006479 3 0.6260 0.532 0.448 0.000 0.552
#> DRR006480 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006481 3 0.3816 0.752 0.148 0.000 0.852
#> DRR006482 1 0.6180 0.389 0.584 0.000 0.416
#> DRR006483 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006484 3 0.5882 0.661 0.348 0.000 0.652
#> DRR006485 2 0.3941 0.845 0.000 0.844 0.156
#> DRR006486 1 0.6192 -0.181 0.580 0.000 0.420
#> DRR006487 3 0.5058 0.724 0.244 0.000 0.756
#> DRR006488 2 0.1289 0.904 0.000 0.968 0.032
#> DRR006489 1 0.0237 0.831 0.996 0.000 0.004
#> DRR006490 3 0.6215 0.571 0.428 0.000 0.572
#> DRR006491 3 0.6180 0.589 0.416 0.000 0.584
#> DRR006492 1 0.3879 0.635 0.848 0.000 0.152
#> DRR006493 3 0.5058 0.724 0.244 0.000 0.756
#> DRR006494 1 0.0237 0.831 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.1661 0.82578 0.000 0.944 0.004 0.052
#> DRR006375 1 0.1305 0.84050 0.960 0.000 0.004 0.036
#> DRR006376 4 0.5050 0.38173 0.408 0.000 0.004 0.588
#> DRR006377 4 0.5203 0.25644 0.016 0.000 0.348 0.636
#> DRR006378 2 0.5769 0.55083 0.000 0.588 0.036 0.376
#> DRR006379 4 0.4819 0.46455 0.344 0.000 0.004 0.652
#> DRR006380 4 0.6980 0.07786 0.000 0.264 0.164 0.572
#> DRR006381 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006382 2 0.7241 0.53303 0.000 0.540 0.196 0.264
#> DRR006383 3 0.4773 0.46752 0.004 0.008 0.708 0.280
#> DRR006384 2 0.0524 0.82802 0.000 0.988 0.004 0.008
#> DRR006385 1 0.4252 0.62488 0.744 0.000 0.004 0.252
#> DRR006386 2 0.2670 0.80364 0.000 0.904 0.024 0.072
#> DRR006387 1 0.4134 0.61609 0.740 0.000 0.000 0.260
#> DRR006388 4 0.6640 0.52516 0.268 0.000 0.128 0.604
#> DRR006389 4 0.6640 0.52516 0.268 0.000 0.128 0.604
#> DRR006390 2 0.1022 0.82990 0.000 0.968 0.000 0.032
#> DRR006391 2 0.1022 0.82990 0.000 0.968 0.000 0.032
#> DRR006392 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006393 1 0.0707 0.84773 0.980 0.000 0.000 0.020
#> DRR006394 4 0.6823 0.20623 0.000 0.200 0.196 0.604
#> DRR006395 4 0.5016 0.40017 0.396 0.000 0.004 0.600
#> DRR006396 1 0.4134 0.61609 0.740 0.000 0.000 0.260
#> DRR006397 4 0.6640 0.52516 0.268 0.000 0.128 0.604
#> DRR006398 4 0.6640 0.52516 0.268 0.000 0.128 0.604
#> DRR006399 4 0.5070 0.36522 0.416 0.000 0.004 0.580
#> DRR006400 4 0.5070 0.36522 0.416 0.000 0.004 0.580
#> DRR006401 2 0.0524 0.82802 0.000 0.988 0.004 0.008
#> DRR006402 2 0.0524 0.82802 0.000 0.988 0.004 0.008
#> DRR006403 4 0.5070 0.36522 0.416 0.000 0.004 0.580
#> DRR006404 4 0.4819 0.46455 0.344 0.000 0.004 0.652
#> DRR006405 4 0.5050 0.38173 0.408 0.000 0.004 0.588
#> DRR006406 4 0.5050 0.38173 0.408 0.000 0.004 0.588
#> DRR006407 4 0.3017 0.52968 0.024 0.028 0.044 0.904
#> DRR006408 4 0.2647 0.43965 0.000 0.120 0.000 0.880
#> DRR006409 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006410 1 0.3801 0.66876 0.780 0.000 0.000 0.220
#> DRR006411 4 0.5574 0.56438 0.148 0.000 0.124 0.728
#> DRR006412 2 0.1022 0.82990 0.000 0.968 0.000 0.032
#> DRR006413 1 0.0376 0.85563 0.992 0.000 0.004 0.004
#> DRR006414 3 0.3870 0.78282 0.208 0.000 0.788 0.004
#> DRR006415 3 0.0921 0.78588 0.028 0.000 0.972 0.000
#> DRR006416 4 0.6897 0.52304 0.256 0.000 0.160 0.584
#> DRR006417 3 0.0921 0.78588 0.028 0.000 0.972 0.000
#> DRR006418 3 0.5212 0.47247 0.420 0.000 0.572 0.008
#> DRR006419 3 0.3791 0.78554 0.200 0.000 0.796 0.004
#> DRR006420 3 0.3791 0.78554 0.200 0.000 0.796 0.004
#> DRR006421 3 0.1174 0.77376 0.012 0.000 0.968 0.020
#> DRR006422 4 0.6300 0.33520 0.000 0.108 0.252 0.640
#> DRR006423 2 0.1978 0.82253 0.000 0.928 0.004 0.068
#> DRR006424 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006425 4 0.6856 0.05430 0.000 0.284 0.140 0.576
#> DRR006426 3 0.1284 0.77196 0.012 0.000 0.964 0.024
#> DRR006427 2 0.2670 0.80364 0.000 0.904 0.024 0.072
#> DRR006428 3 0.4164 0.75141 0.264 0.000 0.736 0.000
#> DRR006429 4 0.6280 0.27226 0.000 0.080 0.316 0.604
#> DRR006430 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006431 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006432 3 0.1284 0.77196 0.012 0.000 0.964 0.024
#> DRR006433 3 0.5024 0.30187 0.008 0.000 0.632 0.360
#> DRR006434 2 0.1902 0.82281 0.000 0.932 0.004 0.064
#> DRR006435 2 0.2670 0.80364 0.000 0.904 0.024 0.072
#> DRR006436 2 0.2670 0.80364 0.000 0.904 0.024 0.072
#> DRR006437 1 0.4343 0.60754 0.732 0.000 0.004 0.264
#> DRR006438 3 0.4103 0.75796 0.256 0.000 0.744 0.000
#> DRR006439 3 0.3649 0.78524 0.204 0.000 0.796 0.000
#> DRR006440 2 0.7377 0.51049 0.000 0.520 0.216 0.264
#> DRR006441 4 0.7374 -0.25049 0.000 0.380 0.164 0.456
#> DRR006442 3 0.3975 0.76858 0.240 0.000 0.760 0.000
#> DRR006443 2 0.7297 0.52452 0.000 0.532 0.204 0.264
#> DRR006444 2 0.2670 0.80364 0.000 0.904 0.024 0.072
#> DRR006445 1 0.4220 0.63143 0.748 0.000 0.004 0.248
#> DRR006446 2 0.1022 0.82990 0.000 0.968 0.000 0.032
#> DRR006447 1 0.4262 0.64146 0.756 0.000 0.008 0.236
#> DRR006448 4 0.5070 0.36522 0.416 0.000 0.004 0.580
#> DRR006449 1 0.3688 0.67936 0.792 0.000 0.000 0.208
#> DRR006450 1 0.0336 0.85433 0.992 0.000 0.000 0.008
#> DRR006451 4 0.4991 0.40914 0.388 0.000 0.004 0.608
#> DRR006452 1 0.1557 0.82795 0.944 0.000 0.000 0.056
#> DRR006453 1 0.2593 0.78872 0.892 0.000 0.004 0.104
#> DRR006454 4 0.5624 0.56344 0.148 0.000 0.128 0.724
#> DRR006455 2 0.2670 0.80364 0.000 0.904 0.024 0.072
#> DRR006456 3 0.0927 0.78058 0.016 0.000 0.976 0.008
#> DRR006457 3 0.1059 0.77837 0.012 0.000 0.972 0.016
#> DRR006458 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006459 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006460 2 0.0376 0.82717 0.000 0.992 0.004 0.004
#> DRR006461 2 0.4576 0.69384 0.000 0.728 0.012 0.260
#> DRR006462 1 0.4164 0.60826 0.736 0.000 0.000 0.264
#> DRR006463 2 0.7241 0.53303 0.000 0.540 0.196 0.264
#> DRR006464 4 0.5601 0.20412 0.004 0.020 0.380 0.596
#> DRR006465 1 0.0000 0.85593 1.000 0.000 0.000 0.000
#> DRR006466 3 0.5277 0.08660 0.000 0.008 0.532 0.460
#> DRR006467 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006468 2 0.2670 0.80364 0.000 0.904 0.024 0.072
#> DRR006469 2 0.7362 0.40013 0.000 0.464 0.164 0.372
#> DRR006470 3 0.3444 0.78880 0.184 0.000 0.816 0.000
#> DRR006471 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006472 3 0.2021 0.74889 0.012 0.000 0.932 0.056
#> DRR006473 2 0.1978 0.82253 0.000 0.928 0.004 0.068
#> DRR006474 2 0.1902 0.82281 0.000 0.932 0.004 0.064
#> DRR006475 1 0.2281 0.75566 0.904 0.000 0.096 0.000
#> DRR006476 4 0.6473 0.30664 0.000 0.108 0.280 0.612
#> DRR006477 4 0.5406 0.00659 0.012 0.000 0.480 0.508
#> DRR006478 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006479 3 0.4741 0.66376 0.328 0.000 0.668 0.004
#> DRR006480 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006481 3 0.1059 0.77837 0.012 0.000 0.972 0.016
#> DRR006482 4 0.6949 0.43047 0.348 0.000 0.124 0.528
#> DRR006483 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006484 3 0.3528 0.78816 0.192 0.000 0.808 0.000
#> DRR006485 2 0.7241 0.53303 0.000 0.540 0.196 0.264
#> DRR006486 1 0.5050 -0.06916 0.588 0.000 0.408 0.004
#> DRR006487 3 0.0921 0.78588 0.028 0.000 0.972 0.000
#> DRR006488 2 0.2670 0.80364 0.000 0.904 0.024 0.072
#> DRR006489 1 0.0336 0.85817 0.992 0.000 0.008 0.000
#> DRR006490 3 0.4164 0.75141 0.264 0.000 0.736 0.000
#> DRR006491 3 0.4008 0.76610 0.244 0.000 0.756 0.000
#> DRR006492 1 0.2944 0.72773 0.868 0.000 0.128 0.004
#> DRR006493 3 0.0921 0.78588 0.028 0.000 0.972 0.000
#> DRR006494 1 0.0336 0.85817 0.992 0.000 0.008 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 5 0.3967 0.7706 0.000 0.264 0.000 0.012 0.724
#> DRR006375 1 0.1282 0.7921 0.952 0.004 0.000 0.044 0.000
#> DRR006376 4 0.3242 0.7502 0.172 0.012 0.000 0.816 0.000
#> DRR006377 4 0.5664 0.1347 0.000 0.348 0.092 0.560 0.000
#> DRR006378 2 0.4927 0.7295 0.000 0.744 0.016 0.104 0.136
#> DRR006379 4 0.2519 0.7704 0.100 0.016 0.000 0.884 0.000
#> DRR006380 2 0.3148 0.7812 0.000 0.864 0.004 0.072 0.060
#> DRR006381 1 0.0898 0.8016 0.972 0.020 0.000 0.008 0.000
#> DRR006382 2 0.3595 0.7399 0.000 0.836 0.024 0.024 0.116
#> DRR006383 2 0.4558 0.4838 0.000 0.652 0.324 0.024 0.000
#> DRR006384 5 0.2818 0.8633 0.000 0.132 0.000 0.012 0.856
#> DRR006385 1 0.6231 0.2383 0.500 0.056 0.040 0.404 0.000
#> DRR006386 5 0.0771 0.8370 0.000 0.000 0.004 0.020 0.976
#> DRR006387 1 0.5352 0.3383 0.556 0.048 0.004 0.392 0.000
#> DRR006388 4 0.5758 0.7202 0.068 0.168 0.072 0.692 0.000
#> DRR006389 4 0.5758 0.7202 0.068 0.168 0.072 0.692 0.000
#> DRR006390 5 0.3053 0.8567 0.000 0.164 0.000 0.008 0.828
#> DRR006391 5 0.3053 0.8567 0.000 0.164 0.000 0.008 0.828
#> DRR006392 1 0.0290 0.8033 0.992 0.000 0.000 0.008 0.000
#> DRR006393 1 0.0912 0.8021 0.972 0.016 0.000 0.012 0.000
#> DRR006394 2 0.3898 0.7711 0.000 0.820 0.032 0.120 0.028
#> DRR006395 4 0.2997 0.7613 0.148 0.012 0.000 0.840 0.000
#> DRR006396 1 0.5983 0.3388 0.544 0.052 0.032 0.372 0.000
#> DRR006397 4 0.5758 0.7202 0.068 0.168 0.072 0.692 0.000
#> DRR006398 4 0.5758 0.7202 0.068 0.168 0.072 0.692 0.000
#> DRR006399 4 0.3391 0.7353 0.188 0.012 0.000 0.800 0.000
#> DRR006400 4 0.3391 0.7353 0.188 0.012 0.000 0.800 0.000
#> DRR006401 5 0.2920 0.8631 0.000 0.132 0.000 0.016 0.852
#> DRR006402 5 0.2920 0.8631 0.000 0.132 0.000 0.016 0.852
#> DRR006403 4 0.3355 0.7398 0.184 0.012 0.000 0.804 0.000
#> DRR006404 4 0.2625 0.7693 0.108 0.016 0.000 0.876 0.000
#> DRR006405 4 0.3242 0.7502 0.172 0.012 0.000 0.816 0.000
#> DRR006406 4 0.3242 0.7502 0.172 0.012 0.000 0.816 0.000
#> DRR006407 4 0.4550 0.4431 0.004 0.276 0.028 0.692 0.000
#> DRR006408 4 0.4585 0.0797 0.000 0.396 0.008 0.592 0.004
#> DRR006409 1 0.0609 0.7972 0.980 0.000 0.020 0.000 0.000
#> DRR006410 1 0.4557 0.5825 0.700 0.032 0.004 0.264 0.000
#> DRR006411 4 0.5465 0.6546 0.020 0.216 0.084 0.680 0.000
#> DRR006412 5 0.3053 0.8567 0.000 0.164 0.000 0.008 0.828
#> DRR006413 1 0.3082 0.7667 0.880 0.052 0.036 0.032 0.000
#> DRR006414 3 0.1901 0.8821 0.056 0.012 0.928 0.004 0.000
#> DRR006415 3 0.2408 0.8583 0.004 0.096 0.892 0.008 0.000
#> DRR006416 4 0.6115 0.6883 0.060 0.176 0.104 0.660 0.000
#> DRR006417 3 0.2060 0.8742 0.008 0.052 0.924 0.016 0.000
#> DRR006418 3 0.5595 0.6338 0.216 0.048 0.680 0.056 0.000
#> DRR006419 3 0.2243 0.8774 0.056 0.012 0.916 0.016 0.000
#> DRR006420 3 0.1983 0.8797 0.060 0.008 0.924 0.008 0.000
#> DRR006421 3 0.1591 0.8701 0.004 0.052 0.940 0.004 0.000
#> DRR006422 2 0.4612 0.7056 0.000 0.740 0.056 0.196 0.008
#> DRR006423 5 0.3861 0.7731 0.000 0.264 0.000 0.008 0.728
#> DRR006424 1 0.0693 0.8024 0.980 0.012 0.000 0.008 0.000
#> DRR006425 2 0.4280 0.7712 0.000 0.796 0.016 0.112 0.076
#> DRR006426 3 0.2629 0.8653 0.008 0.064 0.896 0.032 0.000
#> DRR006427 5 0.0771 0.8370 0.000 0.000 0.004 0.020 0.976
#> DRR006428 3 0.2536 0.8590 0.128 0.000 0.868 0.004 0.000
#> DRR006429 2 0.4149 0.7478 0.000 0.784 0.088 0.128 0.000
#> DRR006430 1 0.0290 0.8033 0.992 0.000 0.000 0.008 0.000
#> DRR006431 1 0.0162 0.8032 0.996 0.000 0.000 0.004 0.000
#> DRR006432 3 0.3117 0.8373 0.004 0.100 0.860 0.036 0.000
#> DRR006433 3 0.5245 0.4332 0.000 0.280 0.640 0.080 0.000
#> DRR006434 5 0.4306 0.6866 0.000 0.328 0.000 0.012 0.660
#> DRR006435 5 0.0771 0.8370 0.000 0.000 0.004 0.020 0.976
#> DRR006436 5 0.0771 0.8370 0.000 0.000 0.004 0.020 0.976
#> DRR006437 1 0.6106 0.2404 0.504 0.056 0.032 0.408 0.000
#> DRR006438 3 0.2707 0.8545 0.132 0.000 0.860 0.008 0.000
#> DRR006439 3 0.1851 0.8782 0.088 0.000 0.912 0.000 0.000
#> DRR006440 2 0.3633 0.7449 0.000 0.832 0.040 0.012 0.116
#> DRR006441 2 0.4023 0.7755 0.000 0.816 0.016 0.092 0.076
#> DRR006442 3 0.2621 0.8680 0.112 0.008 0.876 0.004 0.000
#> DRR006443 2 0.3633 0.7449 0.000 0.832 0.040 0.012 0.116
#> DRR006444 5 0.0771 0.8370 0.000 0.000 0.004 0.020 0.976
#> DRR006445 1 0.5952 0.4001 0.572 0.056 0.032 0.340 0.000
#> DRR006446 5 0.3053 0.8567 0.000 0.164 0.000 0.008 0.828
#> DRR006447 1 0.6264 0.3702 0.552 0.064 0.044 0.340 0.000
#> DRR006448 4 0.3318 0.7296 0.180 0.012 0.000 0.808 0.000
#> DRR006449 1 0.5511 0.5469 0.660 0.052 0.032 0.256 0.000
#> DRR006450 1 0.3581 0.7507 0.852 0.052 0.032 0.064 0.000
#> DRR006451 4 0.2719 0.7618 0.144 0.004 0.000 0.852 0.000
#> DRR006452 1 0.3515 0.7530 0.856 0.052 0.032 0.060 0.000
#> DRR006453 1 0.5423 0.5795 0.680 0.056 0.032 0.232 0.000
#> DRR006454 4 0.5440 0.6635 0.024 0.208 0.080 0.688 0.000
#> DRR006455 5 0.0771 0.8370 0.000 0.000 0.004 0.020 0.976
#> DRR006456 3 0.2956 0.8257 0.004 0.140 0.848 0.008 0.000
#> DRR006457 3 0.1357 0.8733 0.004 0.048 0.948 0.000 0.000
#> DRR006458 1 0.0510 0.7985 0.984 0.000 0.016 0.000 0.000
#> DRR006459 1 0.0880 0.7919 0.968 0.000 0.032 0.000 0.000
#> DRR006460 5 0.2818 0.8633 0.000 0.132 0.000 0.012 0.856
#> DRR006461 2 0.4206 0.4935 0.000 0.708 0.000 0.020 0.272
#> DRR006462 1 0.5494 0.1358 0.484 0.052 0.004 0.460 0.000
#> DRR006463 2 0.3711 0.7284 0.000 0.820 0.032 0.012 0.136
#> DRR006464 2 0.5159 0.6559 0.000 0.688 0.124 0.188 0.000
#> DRR006465 1 0.0290 0.8033 0.992 0.000 0.000 0.008 0.000
#> DRR006466 2 0.5040 0.6419 0.000 0.680 0.236 0.084 0.000
#> DRR006467 1 0.0162 0.8032 0.996 0.000 0.000 0.004 0.000
#> DRR006468 5 0.0771 0.8370 0.000 0.000 0.004 0.020 0.976
#> DRR006469 2 0.3970 0.7720 0.000 0.820 0.016 0.080 0.084
#> DRR006470 3 0.1799 0.8769 0.028 0.012 0.940 0.020 0.000
#> DRR006471 1 0.1251 0.7856 0.956 0.000 0.036 0.008 0.000
#> DRR006472 3 0.3536 0.7634 0.000 0.156 0.812 0.032 0.000
#> DRR006473 5 0.3957 0.7540 0.000 0.280 0.000 0.008 0.712
#> DRR006474 5 0.3861 0.7746 0.000 0.264 0.000 0.008 0.728
#> DRR006475 1 0.1764 0.7630 0.928 0.000 0.064 0.008 0.000
#> DRR006476 2 0.4439 0.7214 0.000 0.760 0.056 0.176 0.008
#> DRR006477 2 0.6625 0.3376 0.000 0.456 0.276 0.268 0.000
#> DRR006478 1 0.0162 0.8032 0.996 0.000 0.000 0.004 0.000
#> DRR006479 3 0.2886 0.8423 0.148 0.000 0.844 0.008 0.000
#> DRR006480 1 0.0880 0.7919 0.968 0.000 0.032 0.000 0.000
#> DRR006481 3 0.1282 0.8751 0.004 0.044 0.952 0.000 0.000
#> DRR006482 4 0.6239 0.6695 0.172 0.124 0.056 0.648 0.000
#> DRR006483 1 0.1251 0.7856 0.956 0.000 0.036 0.008 0.000
#> DRR006484 3 0.1544 0.8830 0.068 0.000 0.932 0.000 0.000
#> DRR006485 2 0.3711 0.7284 0.000 0.820 0.032 0.012 0.136
#> DRR006486 1 0.3612 0.5541 0.764 0.000 0.228 0.008 0.000
#> DRR006487 3 0.2408 0.8583 0.004 0.096 0.892 0.008 0.000
#> DRR006488 5 0.0771 0.8370 0.000 0.000 0.004 0.020 0.976
#> DRR006489 1 0.0451 0.8031 0.988 0.004 0.000 0.008 0.000
#> DRR006490 3 0.2536 0.8590 0.128 0.000 0.868 0.004 0.000
#> DRR006491 3 0.2597 0.8637 0.120 0.004 0.872 0.004 0.000
#> DRR006492 1 0.3203 0.6853 0.820 0.012 0.168 0.000 0.000
#> DRR006493 3 0.2408 0.8583 0.004 0.096 0.892 0.008 0.000
#> DRR006494 1 0.0963 0.7899 0.964 0.000 0.036 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 5 0.5823 0.3443 0.000 0.372 0.000 0.188 0.440 0.000
#> DRR006375 1 0.2039 0.8179 0.904 0.000 0.000 0.020 0.000 0.076
#> DRR006376 4 0.4899 0.7872 0.064 0.000 0.000 0.532 0.000 0.404
#> DRR006377 4 0.6733 0.3322 0.000 0.256 0.052 0.448 0.000 0.244
#> DRR006378 2 0.2980 0.7195 0.000 0.868 0.004 0.056 0.016 0.056
#> DRR006379 4 0.4314 0.7595 0.020 0.000 0.000 0.536 0.000 0.444
#> DRR006380 2 0.2505 0.7433 0.000 0.888 0.004 0.080 0.012 0.016
#> DRR006381 1 0.2191 0.7978 0.876 0.000 0.004 0.000 0.000 0.120
#> DRR006382 2 0.4420 0.6977 0.000 0.708 0.028 0.240 0.016 0.008
#> DRR006383 2 0.5937 0.2895 0.000 0.496 0.356 0.124 0.000 0.024
#> DRR006384 5 0.4077 0.7857 0.000 0.124 0.000 0.100 0.768 0.008
#> DRR006385 6 0.3979 0.5433 0.264 0.000 0.008 0.020 0.000 0.708
#> DRR006386 5 0.0146 0.7754 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006387 6 0.5213 0.2831 0.420 0.000 0.000 0.092 0.000 0.488
#> DRR006388 6 0.3695 0.3528 0.012 0.040 0.032 0.088 0.000 0.828
#> DRR006389 6 0.3695 0.3528 0.012 0.040 0.032 0.088 0.000 0.828
#> DRR006390 5 0.4205 0.7683 0.000 0.188 0.000 0.084 0.728 0.000
#> DRR006391 5 0.4205 0.7683 0.000 0.188 0.000 0.084 0.728 0.000
#> DRR006392 1 0.0713 0.8551 0.972 0.000 0.000 0.000 0.000 0.028
#> DRR006393 1 0.2100 0.8022 0.884 0.000 0.000 0.004 0.000 0.112
#> DRR006394 2 0.2890 0.7408 0.000 0.860 0.012 0.032 0.000 0.096
#> DRR006395 4 0.4768 0.7842 0.052 0.000 0.000 0.532 0.000 0.416
#> DRR006396 6 0.4652 0.5080 0.312 0.000 0.000 0.064 0.000 0.624
#> DRR006397 6 0.3645 0.3524 0.012 0.040 0.032 0.084 0.000 0.832
#> DRR006398 6 0.3645 0.3524 0.012 0.040 0.032 0.084 0.000 0.832
#> DRR006399 4 0.5029 0.7717 0.076 0.000 0.000 0.524 0.000 0.400
#> DRR006400 4 0.5029 0.7717 0.076 0.000 0.000 0.524 0.000 0.400
#> DRR006401 5 0.3955 0.7866 0.000 0.132 0.000 0.092 0.772 0.004
#> DRR006402 5 0.3955 0.7866 0.000 0.132 0.000 0.092 0.772 0.004
#> DRR006403 4 0.5029 0.7717 0.076 0.000 0.000 0.524 0.000 0.400
#> DRR006404 4 0.4561 0.7729 0.036 0.000 0.000 0.536 0.000 0.428
#> DRR006405 4 0.4899 0.7872 0.064 0.000 0.000 0.532 0.000 0.404
#> DRR006406 4 0.4899 0.7872 0.064 0.000 0.000 0.532 0.000 0.404
#> DRR006407 4 0.6029 0.4751 0.000 0.224 0.008 0.488 0.000 0.280
#> DRR006408 4 0.5645 0.2452 0.000 0.392 0.000 0.456 0.000 0.152
#> DRR006409 1 0.0622 0.8545 0.980 0.000 0.000 0.012 0.000 0.008
#> DRR006410 1 0.5016 0.2912 0.592 0.000 0.000 0.096 0.000 0.312
#> DRR006411 6 0.4128 0.3265 0.004 0.060 0.040 0.104 0.000 0.792
#> DRR006412 5 0.4205 0.7683 0.000 0.188 0.000 0.084 0.728 0.000
#> DRR006413 1 0.3872 0.3175 0.604 0.000 0.004 0.000 0.000 0.392
#> DRR006414 3 0.2384 0.8457 0.032 0.004 0.904 0.044 0.000 0.016
#> DRR006415 3 0.2125 0.8352 0.004 0.016 0.908 0.068 0.000 0.004
#> DRR006416 6 0.4677 0.3380 0.012 0.060 0.048 0.124 0.000 0.756
#> DRR006417 3 0.4108 0.7959 0.016 0.008 0.788 0.104 0.000 0.084
#> DRR006418 6 0.7039 -0.0579 0.100 0.012 0.360 0.112 0.000 0.416
#> DRR006419 3 0.4247 0.8023 0.036 0.000 0.776 0.096 0.000 0.092
#> DRR006420 3 0.3648 0.8265 0.040 0.000 0.824 0.064 0.000 0.072
#> DRR006421 3 0.0810 0.8487 0.004 0.004 0.976 0.008 0.000 0.008
#> DRR006422 2 0.3256 0.7397 0.000 0.836 0.020 0.032 0.000 0.112
#> DRR006423 5 0.5205 0.4608 0.000 0.412 0.000 0.092 0.496 0.000
#> DRR006424 1 0.1588 0.8334 0.924 0.000 0.004 0.000 0.000 0.072
#> DRR006425 2 0.2146 0.7453 0.000 0.908 0.008 0.024 0.000 0.060
#> DRR006426 3 0.5206 0.7150 0.016 0.016 0.688 0.108 0.000 0.172
#> DRR006427 5 0.0146 0.7754 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006428 3 0.2705 0.8409 0.072 0.004 0.872 0.052 0.000 0.000
#> DRR006429 2 0.3384 0.7342 0.000 0.828 0.028 0.028 0.000 0.116
#> DRR006430 1 0.0713 0.8551 0.972 0.000 0.000 0.000 0.000 0.028
#> DRR006431 1 0.0547 0.8562 0.980 0.000 0.000 0.000 0.000 0.020
#> DRR006432 3 0.5643 0.6770 0.016 0.028 0.652 0.120 0.000 0.184
#> DRR006433 3 0.4238 0.6705 0.000 0.160 0.760 0.040 0.000 0.040
#> DRR006434 2 0.5961 -0.1376 0.000 0.432 0.000 0.232 0.336 0.000
#> DRR006435 5 0.0146 0.7754 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006436 5 0.0146 0.7754 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006437 6 0.4468 0.5235 0.288 0.000 0.004 0.048 0.000 0.660
#> DRR006438 3 0.2882 0.8403 0.076 0.000 0.860 0.060 0.000 0.004
#> DRR006439 3 0.1625 0.8506 0.060 0.000 0.928 0.012 0.000 0.000
#> DRR006440 2 0.3909 0.7086 0.000 0.732 0.020 0.236 0.012 0.000
#> DRR006441 2 0.1606 0.7480 0.000 0.932 0.008 0.000 0.004 0.056
#> DRR006442 3 0.2610 0.8403 0.060 0.004 0.884 0.048 0.000 0.004
#> DRR006443 2 0.3909 0.7086 0.000 0.732 0.020 0.236 0.012 0.000
#> DRR006444 5 0.0146 0.7754 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006445 6 0.4302 0.4786 0.344 0.000 0.004 0.024 0.000 0.628
#> DRR006446 5 0.4186 0.7678 0.000 0.192 0.000 0.080 0.728 0.000
#> DRR006447 6 0.3852 0.5441 0.256 0.000 0.016 0.008 0.000 0.720
#> DRR006448 4 0.4882 0.7515 0.060 0.000 0.000 0.512 0.000 0.428
#> DRR006449 6 0.4591 0.3525 0.408 0.000 0.000 0.040 0.000 0.552
#> DRR006450 1 0.3982 0.0726 0.536 0.000 0.004 0.000 0.000 0.460
#> DRR006451 4 0.4632 0.7719 0.040 0.000 0.000 0.520 0.000 0.440
#> DRR006452 1 0.3881 0.3065 0.600 0.000 0.004 0.000 0.000 0.396
#> DRR006453 6 0.3930 0.3173 0.420 0.000 0.004 0.000 0.000 0.576
#> DRR006454 6 0.3984 0.3234 0.004 0.068 0.036 0.088 0.000 0.804
#> DRR006455 5 0.0146 0.7754 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006456 3 0.2812 0.8145 0.004 0.040 0.868 0.084 0.000 0.004
#> DRR006457 3 0.0551 0.8491 0.004 0.004 0.984 0.008 0.000 0.000
#> DRR006458 1 0.0291 0.8544 0.992 0.000 0.000 0.004 0.000 0.004
#> DRR006459 1 0.0458 0.8504 0.984 0.000 0.000 0.016 0.000 0.000
#> DRR006460 5 0.3915 0.7872 0.000 0.128 0.000 0.092 0.776 0.004
#> DRR006461 2 0.4112 0.6662 0.000 0.724 0.004 0.224 0.048 0.000
#> DRR006462 6 0.4934 0.4336 0.264 0.000 0.000 0.108 0.000 0.628
#> DRR006463 2 0.3998 0.7043 0.000 0.728 0.016 0.236 0.020 0.000
#> DRR006464 2 0.5999 0.5317 0.000 0.596 0.076 0.104 0.000 0.224
#> DRR006465 1 0.0935 0.8544 0.964 0.000 0.000 0.004 0.000 0.032
#> DRR006466 2 0.5575 0.5824 0.000 0.632 0.228 0.064 0.000 0.076
#> DRR006467 1 0.0632 0.8561 0.976 0.000 0.000 0.000 0.000 0.024
#> DRR006468 5 0.0146 0.7754 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006469 2 0.2145 0.7455 0.000 0.912 0.008 0.020 0.004 0.056
#> DRR006470 3 0.4188 0.7859 0.016 0.004 0.776 0.108 0.000 0.096
#> DRR006471 1 0.0692 0.8435 0.976 0.000 0.004 0.020 0.000 0.000
#> DRR006472 3 0.4967 0.7287 0.004 0.040 0.720 0.104 0.000 0.132
#> DRR006473 5 0.5350 0.4291 0.000 0.416 0.000 0.108 0.476 0.000
#> DRR006474 5 0.5475 0.4067 0.000 0.416 0.000 0.124 0.460 0.000
#> DRR006475 1 0.0909 0.8365 0.968 0.000 0.012 0.020 0.000 0.000
#> DRR006476 2 0.3181 0.7399 0.000 0.840 0.020 0.028 0.000 0.112
#> DRR006477 3 0.6523 0.2578 0.000 0.280 0.500 0.156 0.000 0.064
#> DRR006478 1 0.1010 0.8552 0.960 0.000 0.000 0.004 0.000 0.036
#> DRR006479 3 0.3196 0.8111 0.136 0.000 0.824 0.036 0.000 0.004
#> DRR006480 1 0.0363 0.8505 0.988 0.000 0.000 0.012 0.000 0.000
#> DRR006481 3 0.2253 0.8394 0.012 0.004 0.896 0.084 0.000 0.004
#> DRR006482 6 0.3134 0.4065 0.068 0.004 0.024 0.044 0.000 0.860
#> DRR006483 1 0.0692 0.8435 0.976 0.000 0.004 0.020 0.000 0.000
#> DRR006484 3 0.1864 0.8520 0.040 0.000 0.924 0.032 0.000 0.004
#> DRR006485 2 0.3998 0.7043 0.000 0.728 0.016 0.236 0.020 0.000
#> DRR006486 1 0.2147 0.7579 0.896 0.000 0.084 0.020 0.000 0.000
#> DRR006487 3 0.2125 0.8352 0.004 0.016 0.908 0.068 0.000 0.004
#> DRR006488 5 0.0146 0.7754 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006489 1 0.0937 0.8522 0.960 0.000 0.000 0.000 0.000 0.040
#> DRR006490 3 0.2641 0.8417 0.072 0.004 0.876 0.048 0.000 0.000
#> DRR006491 3 0.2604 0.8397 0.064 0.004 0.884 0.044 0.000 0.004
#> DRR006492 1 0.4341 0.6510 0.748 0.000 0.152 0.016 0.000 0.084
#> DRR006493 3 0.2125 0.8352 0.004 0.016 0.908 0.068 0.000 0.004
#> DRR006494 1 0.0603 0.8483 0.980 0.000 0.004 0.016 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)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.983 0.941 0.978 0.4703 0.533 0.533
#> 3 3 0.989 0.965 0.985 0.3812 0.799 0.630
#> 4 4 0.916 0.880 0.941 0.1321 0.898 0.720
#> 5 5 0.894 0.876 0.915 0.0492 0.952 0.828
#> 6 6 0.856 0.796 0.885 0.0391 0.940 0.755
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DRR006374 2 0.000 0.977 0.000 1.000
#> DRR006375 1 0.000 0.977 1.000 0.000
#> DRR006376 1 0.000 0.977 1.000 0.000
#> DRR006377 2 0.971 0.310 0.400 0.600
#> DRR006378 2 0.000 0.977 0.000 1.000
#> DRR006379 1 0.000 0.977 1.000 0.000
#> DRR006380 2 0.000 0.977 0.000 1.000
#> DRR006381 1 0.000 0.977 1.000 0.000
#> DRR006382 2 0.000 0.977 0.000 1.000
#> DRR006383 2 0.000 0.977 0.000 1.000
#> DRR006384 2 0.000 0.977 0.000 1.000
#> DRR006385 1 0.000 0.977 1.000 0.000
#> DRR006386 2 0.000 0.977 0.000 1.000
#> DRR006387 1 0.000 0.977 1.000 0.000
#> DRR006388 1 0.000 0.977 1.000 0.000
#> DRR006389 1 0.000 0.977 1.000 0.000
#> DRR006390 2 0.000 0.977 0.000 1.000
#> DRR006391 2 0.000 0.977 0.000 1.000
#> DRR006392 1 0.000 0.977 1.000 0.000
#> DRR006393 1 0.000 0.977 1.000 0.000
#> DRR006394 2 0.000 0.977 0.000 1.000
#> DRR006395 1 0.000 0.977 1.000 0.000
#> DRR006396 1 0.000 0.977 1.000 0.000
#> DRR006397 1 0.000 0.977 1.000 0.000
#> DRR006398 1 0.000 0.977 1.000 0.000
#> DRR006399 1 0.000 0.977 1.000 0.000
#> DRR006400 1 0.000 0.977 1.000 0.000
#> DRR006401 2 0.000 0.977 0.000 1.000
#> DRR006402 2 0.000 0.977 0.000 1.000
#> DRR006403 1 0.000 0.977 1.000 0.000
#> DRR006404 1 0.000 0.977 1.000 0.000
#> DRR006405 1 0.000 0.977 1.000 0.000
#> DRR006406 1 0.000 0.977 1.000 0.000
#> DRR006407 2 0.000 0.977 0.000 1.000
#> DRR006408 2 0.000 0.977 0.000 1.000
#> DRR006409 1 0.000 0.977 1.000 0.000
#> DRR006410 1 0.000 0.977 1.000 0.000
#> DRR006411 2 0.978 0.290 0.412 0.588
#> DRR006412 2 0.000 0.977 0.000 1.000
#> DRR006413 1 0.000 0.977 1.000 0.000
#> DRR006414 1 0.000 0.977 1.000 0.000
#> DRR006415 1 0.000 0.977 1.000 0.000
#> DRR006416 1 0.000 0.977 1.000 0.000
#> DRR006417 1 0.000 0.977 1.000 0.000
#> DRR006418 1 0.000 0.977 1.000 0.000
#> DRR006419 1 0.000 0.977 1.000 0.000
#> DRR006420 1 0.000 0.977 1.000 0.000
#> DRR006421 1 0.000 0.977 1.000 0.000
#> DRR006422 2 0.000 0.977 0.000 1.000
#> DRR006423 2 0.000 0.977 0.000 1.000
#> DRR006424 1 0.000 0.977 1.000 0.000
#> DRR006425 2 0.000 0.977 0.000 1.000
#> DRR006426 1 0.000 0.977 1.000 0.000
#> DRR006427 2 0.000 0.977 0.000 1.000
#> DRR006428 1 0.000 0.977 1.000 0.000
#> DRR006429 2 0.000 0.977 0.000 1.000
#> DRR006430 1 0.000 0.977 1.000 0.000
#> DRR006431 1 0.000 0.977 1.000 0.000
#> DRR006432 1 0.000 0.977 1.000 0.000
#> DRR006433 1 0.978 0.299 0.588 0.412
#> DRR006434 2 0.000 0.977 0.000 1.000
#> DRR006435 2 0.000 0.977 0.000 1.000
#> DRR006436 2 0.000 0.977 0.000 1.000
#> DRR006437 1 0.000 0.977 1.000 0.000
#> DRR006438 1 0.000 0.977 1.000 0.000
#> DRR006439 1 0.000 0.977 1.000 0.000
#> DRR006440 2 0.000 0.977 0.000 1.000
#> DRR006441 2 0.000 0.977 0.000 1.000
#> DRR006442 1 0.000 0.977 1.000 0.000
#> DRR006443 2 0.000 0.977 0.000 1.000
#> DRR006444 2 0.000 0.977 0.000 1.000
#> DRR006445 1 0.000 0.977 1.000 0.000
#> DRR006446 2 0.000 0.977 0.000 1.000
#> DRR006447 1 0.000 0.977 1.000 0.000
#> DRR006448 1 0.000 0.977 1.000 0.000
#> DRR006449 1 0.000 0.977 1.000 0.000
#> DRR006450 1 0.000 0.977 1.000 0.000
#> DRR006451 1 0.000 0.977 1.000 0.000
#> DRR006452 1 0.000 0.977 1.000 0.000
#> DRR006453 1 0.000 0.977 1.000 0.000
#> DRR006454 1 0.971 0.313 0.600 0.400
#> DRR006455 2 0.000 0.977 0.000 1.000
#> DRR006456 1 0.978 0.299 0.588 0.412
#> DRR006457 1 0.141 0.958 0.980 0.020
#> DRR006458 1 0.000 0.977 1.000 0.000
#> DRR006459 1 0.000 0.977 1.000 0.000
#> DRR006460 2 0.000 0.977 0.000 1.000
#> DRR006461 2 0.000 0.977 0.000 1.000
#> DRR006462 1 0.000 0.977 1.000 0.000
#> DRR006463 2 0.000 0.977 0.000 1.000
#> DRR006464 2 0.000 0.977 0.000 1.000
#> DRR006465 1 0.000 0.977 1.000 0.000
#> DRR006466 2 0.000 0.977 0.000 1.000
#> DRR006467 1 0.000 0.977 1.000 0.000
#> DRR006468 2 0.000 0.977 0.000 1.000
#> DRR006469 2 0.000 0.977 0.000 1.000
#> DRR006470 1 0.000 0.977 1.000 0.000
#> DRR006471 1 0.000 0.977 1.000 0.000
#> DRR006472 2 0.605 0.812 0.148 0.852
#> DRR006473 2 0.000 0.977 0.000 1.000
#> DRR006474 2 0.000 0.977 0.000 1.000
#> DRR006475 1 0.000 0.977 1.000 0.000
#> DRR006476 2 0.000 0.977 0.000 1.000
#> DRR006477 1 0.978 0.299 0.588 0.412
#> DRR006478 1 0.000 0.977 1.000 0.000
#> DRR006479 1 0.000 0.977 1.000 0.000
#> DRR006480 1 0.000 0.977 1.000 0.000
#> DRR006481 1 0.000 0.977 1.000 0.000
#> DRR006482 1 0.000 0.977 1.000 0.000
#> DRR006483 1 0.000 0.977 1.000 0.000
#> DRR006484 1 0.000 0.977 1.000 0.000
#> DRR006485 2 0.000 0.977 0.000 1.000
#> DRR006486 1 0.000 0.977 1.000 0.000
#> DRR006487 1 0.118 0.962 0.984 0.016
#> DRR006488 2 0.000 0.977 0.000 1.000
#> DRR006489 1 0.000 0.977 1.000 0.000
#> DRR006490 1 0.000 0.977 1.000 0.000
#> DRR006491 1 0.000 0.977 1.000 0.000
#> DRR006492 1 0.000 0.977 1.000 0.000
#> DRR006493 1 0.118 0.962 0.984 0.016
#> DRR006494 1 0.000 0.977 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006375 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006376 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006377 2 0.8590 0.447 0.164 0.600 0.236
#> DRR006378 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006379 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006380 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006381 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006382 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006383 2 0.3551 0.840 0.000 0.868 0.132
#> DRR006384 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006385 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006386 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006387 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006388 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006389 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006390 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006391 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006392 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006393 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006394 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006395 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006396 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006397 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006398 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006399 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006400 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006401 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006403 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006404 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006405 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006406 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006407 2 0.0237 0.977 0.004 0.996 0.000
#> DRR006408 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006409 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006410 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006411 1 0.6126 0.340 0.600 0.400 0.000
#> DRR006412 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006413 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006414 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006415 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006416 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006417 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006418 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006419 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006420 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006421 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006422 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006423 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006424 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006425 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006426 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006427 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006428 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006429 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006430 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006431 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006432 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006433 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006434 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006435 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006437 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006438 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006439 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006440 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006441 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006442 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006443 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006444 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006445 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006446 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006447 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006448 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006449 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006450 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006451 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006452 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006453 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006454 1 0.5058 0.677 0.756 0.244 0.000
#> DRR006455 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006456 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006457 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006458 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006459 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006460 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006461 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006462 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006463 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006464 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006465 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006466 2 0.4931 0.698 0.000 0.768 0.232
#> DRR006467 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006468 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006469 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006470 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006471 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006472 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006473 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006474 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006475 1 0.2711 0.894 0.912 0.000 0.088
#> DRR006476 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006477 3 0.3752 0.826 0.000 0.144 0.856
#> DRR006478 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006479 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006480 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006481 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006482 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006483 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006484 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006485 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006486 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006487 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006488 2 0.0000 0.981 0.000 1.000 0.000
#> DRR006489 1 0.0000 0.982 1.000 0.000 0.000
#> DRR006490 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006491 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006492 1 0.3412 0.852 0.876 0.000 0.124
#> DRR006493 3 0.0000 0.994 0.000 0.000 1.000
#> DRR006494 1 0.0000 0.982 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006375 1 0.1557 0.834 0.944 0.000 0.000 0.056
#> DRR006376 4 0.0921 0.910 0.028 0.000 0.000 0.972
#> DRR006377 4 0.5355 0.433 0.020 0.360 0.000 0.620
#> DRR006378 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006379 4 0.0921 0.910 0.028 0.000 0.000 0.972
#> DRR006380 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006381 1 0.0707 0.858 0.980 0.000 0.000 0.020
#> DRR006382 2 0.0336 0.983 0.000 0.992 0.000 0.008
#> DRR006383 2 0.0524 0.980 0.000 0.988 0.004 0.008
#> DRR006384 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006385 1 0.4543 0.614 0.676 0.000 0.000 0.324
#> DRR006386 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006387 1 0.4679 0.557 0.648 0.000 0.000 0.352
#> DRR006388 1 0.4977 0.390 0.540 0.000 0.000 0.460
#> DRR006389 1 0.4977 0.390 0.540 0.000 0.000 0.460
#> DRR006390 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006391 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006392 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> DRR006393 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> DRR006394 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006395 4 0.0921 0.910 0.028 0.000 0.000 0.972
#> DRR006396 1 0.4761 0.555 0.628 0.000 0.000 0.372
#> DRR006397 1 0.4992 0.352 0.524 0.000 0.000 0.476
#> DRR006398 1 0.4992 0.352 0.524 0.000 0.000 0.476
#> DRR006399 4 0.0921 0.910 0.028 0.000 0.000 0.972
#> DRR006400 4 0.0921 0.910 0.028 0.000 0.000 0.972
#> DRR006401 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006402 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006403 4 0.0921 0.910 0.028 0.000 0.000 0.972
#> DRR006404 4 0.0921 0.910 0.028 0.000 0.000 0.972
#> DRR006405 4 0.0921 0.910 0.028 0.000 0.000 0.972
#> DRR006406 4 0.0921 0.910 0.028 0.000 0.000 0.972
#> DRR006407 4 0.0921 0.894 0.000 0.028 0.000 0.972
#> DRR006408 4 0.4817 0.371 0.000 0.388 0.000 0.612
#> DRR006409 1 0.0336 0.860 0.992 0.000 0.008 0.000
#> DRR006410 1 0.4679 0.557 0.648 0.000 0.000 0.352
#> DRR006411 4 0.2918 0.816 0.008 0.116 0.000 0.876
#> DRR006412 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006413 1 0.0707 0.858 0.980 0.000 0.000 0.020
#> DRR006414 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006415 3 0.0336 0.984 0.000 0.000 0.992 0.008
#> DRR006416 1 0.0707 0.858 0.980 0.000 0.000 0.020
#> DRR006417 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006418 1 0.1042 0.856 0.972 0.000 0.008 0.020
#> DRR006419 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006420 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006421 3 0.0188 0.985 0.000 0.000 0.996 0.004
#> DRR006422 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006423 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006424 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> DRR006425 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006426 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006427 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006428 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006429 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006430 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> DRR006432 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006433 3 0.0336 0.984 0.000 0.000 0.992 0.008
#> DRR006434 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006435 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006436 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006437 1 0.4761 0.555 0.628 0.000 0.000 0.372
#> DRR006438 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006439 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006440 2 0.0336 0.983 0.000 0.992 0.000 0.008
#> DRR006441 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006442 3 0.0336 0.984 0.000 0.000 0.992 0.008
#> DRR006443 2 0.0336 0.983 0.000 0.992 0.000 0.008
#> DRR006444 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006445 1 0.0707 0.858 0.980 0.000 0.000 0.020
#> DRR006446 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006447 1 0.0707 0.858 0.980 0.000 0.000 0.020
#> DRR006448 4 0.0921 0.910 0.028 0.000 0.000 0.972
#> DRR006449 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> DRR006450 1 0.0707 0.858 0.980 0.000 0.000 0.020
#> DRR006451 4 0.0336 0.897 0.008 0.000 0.000 0.992
#> DRR006452 1 0.0707 0.858 0.980 0.000 0.000 0.020
#> DRR006453 1 0.0707 0.858 0.980 0.000 0.000 0.020
#> DRR006454 4 0.2816 0.845 0.036 0.064 0.000 0.900
#> DRR006455 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006456 3 0.0336 0.984 0.000 0.000 0.992 0.008
#> DRR006457 3 0.0336 0.984 0.000 0.000 0.992 0.008
#> DRR006458 1 0.0336 0.860 0.992 0.000 0.008 0.000
#> DRR006459 1 0.0336 0.860 0.992 0.000 0.008 0.000
#> DRR006460 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006461 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006462 1 0.4713 0.545 0.640 0.000 0.000 0.360
#> DRR006463 2 0.0336 0.983 0.000 0.992 0.000 0.008
#> DRR006464 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006465 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> DRR006466 2 0.4746 0.546 0.000 0.688 0.304 0.008
#> DRR006467 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> DRR006468 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006469 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006470 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006471 1 0.0336 0.860 0.992 0.000 0.008 0.000
#> DRR006472 3 0.0336 0.984 0.000 0.000 0.992 0.008
#> DRR006473 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006474 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006475 1 0.0336 0.860 0.992 0.000 0.008 0.000
#> DRR006476 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006477 3 0.5483 0.678 0.000 0.136 0.736 0.128
#> DRR006478 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> DRR006479 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006480 1 0.0336 0.860 0.992 0.000 0.008 0.000
#> DRR006481 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006482 1 0.4761 0.555 0.628 0.000 0.000 0.372
#> DRR006483 1 0.0336 0.860 0.992 0.000 0.008 0.000
#> DRR006484 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006485 2 0.0336 0.983 0.000 0.992 0.000 0.008
#> DRR006486 1 0.4697 0.390 0.644 0.000 0.356 0.000
#> DRR006487 3 0.0336 0.984 0.000 0.000 0.992 0.008
#> DRR006488 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> DRR006489 1 0.0000 0.861 1.000 0.000 0.000 0.000
#> DRR006490 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006491 3 0.0000 0.986 0.000 0.000 1.000 0.000
#> DRR006492 1 0.0336 0.860 0.992 0.000 0.008 0.000
#> DRR006493 3 0.0336 0.984 0.000 0.000 0.992 0.008
#> DRR006494 1 0.0336 0.860 0.992 0.000 0.008 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006375 1 0.1197 0.887 0.952 0.000 0.000 0.048 0.000
#> DRR006376 4 0.0404 0.886 0.012 0.000 0.000 0.988 0.000
#> DRR006377 4 0.4214 0.677 0.004 0.088 0.000 0.788 0.120
#> DRR006378 2 0.0290 0.958 0.000 0.992 0.000 0.000 0.008
#> DRR006379 4 0.0404 0.886 0.012 0.000 0.000 0.988 0.000
#> DRR006380 2 0.1792 0.918 0.000 0.916 0.000 0.000 0.084
#> DRR006381 1 0.0609 0.913 0.980 0.000 0.000 0.000 0.020
#> DRR006382 2 0.1965 0.911 0.000 0.904 0.000 0.000 0.096
#> DRR006383 2 0.4164 0.795 0.000 0.784 0.120 0.000 0.096
#> DRR006384 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006385 5 0.5307 0.851 0.156 0.000 0.000 0.168 0.676
#> DRR006386 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006387 1 0.3863 0.654 0.772 0.000 0.000 0.200 0.028
#> DRR006388 5 0.4766 0.900 0.072 0.000 0.000 0.220 0.708
#> DRR006389 5 0.4766 0.900 0.072 0.000 0.000 0.220 0.708
#> DRR006390 2 0.0290 0.958 0.000 0.992 0.000 0.000 0.008
#> DRR006391 2 0.0290 0.958 0.000 0.992 0.000 0.000 0.008
#> DRR006392 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000
#> DRR006393 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000
#> DRR006394 2 0.0404 0.956 0.000 0.988 0.000 0.000 0.012
#> DRR006395 4 0.0404 0.886 0.012 0.000 0.000 0.988 0.000
#> DRR006396 1 0.6206 0.137 0.548 0.000 0.000 0.200 0.252
#> DRR006397 5 0.4737 0.898 0.068 0.000 0.000 0.224 0.708
#> DRR006398 5 0.4737 0.898 0.068 0.000 0.000 0.224 0.708
#> DRR006399 4 0.0404 0.886 0.012 0.000 0.000 0.988 0.000
#> DRR006400 4 0.0404 0.886 0.012 0.000 0.000 0.988 0.000
#> DRR006401 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006403 4 0.0404 0.886 0.012 0.000 0.000 0.988 0.000
#> DRR006404 4 0.0404 0.886 0.012 0.000 0.000 0.988 0.000
#> DRR006405 4 0.0404 0.886 0.012 0.000 0.000 0.988 0.000
#> DRR006406 4 0.0404 0.886 0.012 0.000 0.000 0.988 0.000
#> DRR006407 4 0.0807 0.866 0.000 0.012 0.000 0.976 0.012
#> DRR006408 4 0.3861 0.518 0.000 0.284 0.000 0.712 0.004
#> DRR006409 1 0.0290 0.919 0.992 0.000 0.008 0.000 0.000
#> DRR006410 1 0.3231 0.688 0.800 0.000 0.000 0.196 0.004
#> DRR006411 5 0.3177 0.786 0.000 0.000 0.000 0.208 0.792
#> DRR006412 2 0.0290 0.958 0.000 0.992 0.000 0.000 0.008
#> DRR006413 1 0.1792 0.871 0.916 0.000 0.000 0.000 0.084
#> DRR006414 3 0.0000 0.925 0.000 0.000 1.000 0.000 0.000
#> DRR006415 3 0.0290 0.924 0.000 0.000 0.992 0.000 0.008
#> DRR006416 1 0.1270 0.898 0.948 0.000 0.000 0.000 0.052
#> DRR006417 3 0.3462 0.849 0.000 0.000 0.792 0.012 0.196
#> DRR006418 1 0.3935 0.690 0.760 0.000 0.008 0.012 0.220
#> DRR006419 3 0.3618 0.847 0.004 0.000 0.788 0.012 0.196
#> DRR006420 3 0.2853 0.886 0.052 0.000 0.876 0.000 0.072
#> DRR006421 3 0.0162 0.925 0.000 0.000 0.996 0.000 0.004
#> DRR006422 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006423 2 0.0290 0.958 0.000 0.992 0.000 0.000 0.008
#> DRR006424 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000
#> DRR006425 2 0.0290 0.958 0.000 0.992 0.000 0.000 0.008
#> DRR006426 3 0.3783 0.833 0.004 0.000 0.768 0.012 0.216
#> DRR006427 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006428 3 0.0000 0.925 0.000 0.000 1.000 0.000 0.000
#> DRR006429 2 0.0404 0.956 0.000 0.988 0.000 0.000 0.012
#> DRR006430 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000
#> DRR006432 3 0.3783 0.833 0.004 0.000 0.768 0.012 0.216
#> DRR006433 3 0.1965 0.868 0.000 0.000 0.904 0.000 0.096
#> DRR006434 2 0.1732 0.920 0.000 0.920 0.000 0.000 0.080
#> DRR006435 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006437 5 0.5253 0.878 0.124 0.000 0.000 0.200 0.676
#> DRR006438 3 0.1502 0.915 0.004 0.000 0.940 0.000 0.056
#> DRR006439 3 0.0000 0.925 0.000 0.000 1.000 0.000 0.000
#> DRR006440 2 0.1965 0.911 0.000 0.904 0.000 0.000 0.096
#> DRR006441 2 0.0290 0.958 0.000 0.992 0.000 0.000 0.008
#> DRR006442 3 0.0162 0.925 0.000 0.000 0.996 0.000 0.004
#> DRR006443 2 0.1965 0.911 0.000 0.904 0.000 0.000 0.096
#> DRR006444 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006445 1 0.1851 0.868 0.912 0.000 0.000 0.000 0.088
#> DRR006446 2 0.0290 0.958 0.000 0.992 0.000 0.000 0.008
#> DRR006447 5 0.3730 0.637 0.288 0.000 0.000 0.000 0.712
#> DRR006448 4 0.0404 0.886 0.012 0.000 0.000 0.988 0.000
#> DRR006449 1 0.0290 0.918 0.992 0.000 0.000 0.000 0.008
#> DRR006450 1 0.1851 0.868 0.912 0.000 0.000 0.000 0.088
#> DRR006451 4 0.0404 0.871 0.000 0.000 0.000 0.988 0.012
#> DRR006452 1 0.1851 0.868 0.912 0.000 0.000 0.000 0.088
#> DRR006453 1 0.0510 0.914 0.984 0.000 0.000 0.000 0.016
#> DRR006454 5 0.4584 0.885 0.056 0.000 0.000 0.228 0.716
#> DRR006455 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006456 3 0.1908 0.870 0.000 0.000 0.908 0.000 0.092
#> DRR006457 3 0.0290 0.924 0.000 0.000 0.992 0.000 0.008
#> DRR006458 1 0.0162 0.920 0.996 0.000 0.004 0.000 0.000
#> DRR006459 1 0.0290 0.919 0.992 0.000 0.008 0.000 0.000
#> DRR006460 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006461 2 0.1851 0.916 0.000 0.912 0.000 0.000 0.088
#> DRR006462 1 0.6150 0.161 0.560 0.000 0.000 0.204 0.236
#> DRR006463 2 0.1965 0.911 0.000 0.904 0.000 0.000 0.096
#> DRR006464 2 0.3628 0.745 0.000 0.772 0.000 0.012 0.216
#> DRR006465 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000
#> DRR006466 2 0.5404 0.575 0.000 0.636 0.264 0.000 0.100
#> DRR006467 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000
#> DRR006468 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006469 2 0.0290 0.958 0.000 0.992 0.000 0.000 0.008
#> DRR006470 3 0.3719 0.839 0.004 0.000 0.776 0.012 0.208
#> DRR006471 1 0.0290 0.919 0.992 0.000 0.008 0.000 0.000
#> DRR006472 3 0.3318 0.864 0.000 0.000 0.808 0.012 0.180
#> DRR006473 2 0.0290 0.958 0.000 0.992 0.000 0.000 0.008
#> DRR006474 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006475 1 0.0290 0.919 0.992 0.000 0.008 0.000 0.000
#> DRR006476 2 0.0162 0.958 0.000 0.996 0.000 0.000 0.004
#> DRR006477 4 0.6775 0.213 0.000 0.044 0.388 0.468 0.100
#> DRR006478 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000
#> DRR006479 3 0.2171 0.889 0.064 0.000 0.912 0.000 0.024
#> DRR006480 1 0.0290 0.919 0.992 0.000 0.008 0.000 0.000
#> DRR006481 3 0.2017 0.906 0.000 0.000 0.912 0.008 0.080
#> DRR006482 5 0.5240 0.880 0.120 0.000 0.000 0.204 0.676
#> DRR006483 1 0.0290 0.919 0.992 0.000 0.008 0.000 0.000
#> DRR006484 3 0.0162 0.925 0.000 0.000 0.996 0.000 0.004
#> DRR006485 2 0.1965 0.911 0.000 0.904 0.000 0.000 0.096
#> DRR006486 1 0.0880 0.904 0.968 0.000 0.032 0.000 0.000
#> DRR006487 3 0.0290 0.924 0.000 0.000 0.992 0.000 0.008
#> DRR006488 2 0.0000 0.959 0.000 1.000 0.000 0.000 0.000
#> DRR006489 1 0.0000 0.920 1.000 0.000 0.000 0.000 0.000
#> DRR006490 3 0.0000 0.925 0.000 0.000 1.000 0.000 0.000
#> DRR006491 3 0.0000 0.925 0.000 0.000 1.000 0.000 0.000
#> DRR006492 1 0.0880 0.906 0.968 0.000 0.032 0.000 0.000
#> DRR006493 3 0.0290 0.924 0.000 0.000 0.992 0.000 0.008
#> DRR006494 1 0.0290 0.919 0.992 0.000 0.008 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006375 1 0.0692 0.9105 0.976 0.000 0.000 0.004 0.000 0.020
#> DRR006376 4 0.0713 0.9498 0.000 0.000 0.000 0.972 0.000 0.028
#> DRR006377 4 0.2172 0.8681 0.000 0.020 0.024 0.912 0.044 0.000
#> DRR006378 2 0.0146 0.9408 0.000 0.996 0.000 0.004 0.000 0.000
#> DRR006379 4 0.0790 0.9505 0.000 0.000 0.000 0.968 0.000 0.032
#> DRR006380 2 0.3130 0.8264 0.000 0.824 0.144 0.004 0.000 0.028
#> DRR006381 1 0.1863 0.8623 0.896 0.000 0.000 0.000 0.000 0.104
#> DRR006382 2 0.4136 0.7486 0.000 0.732 0.216 0.004 0.004 0.044
#> DRR006383 3 0.3262 0.4399 0.000 0.116 0.832 0.004 0.004 0.044
#> DRR006384 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006385 6 0.1575 0.8446 0.032 0.000 0.000 0.032 0.000 0.936
#> DRR006386 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006387 1 0.3807 0.7192 0.756 0.000 0.000 0.052 0.000 0.192
#> DRR006388 6 0.1296 0.8463 0.012 0.000 0.000 0.032 0.004 0.952
#> DRR006389 6 0.1296 0.8463 0.012 0.000 0.000 0.032 0.004 0.952
#> DRR006390 2 0.0146 0.9408 0.000 0.996 0.000 0.004 0.000 0.000
#> DRR006391 2 0.0146 0.9408 0.000 0.996 0.000 0.004 0.000 0.000
#> DRR006392 1 0.0146 0.9187 0.996 0.000 0.000 0.000 0.000 0.004
#> DRR006393 1 0.0146 0.9187 0.996 0.000 0.000 0.000 0.000 0.004
#> DRR006394 2 0.0363 0.9375 0.000 0.988 0.000 0.012 0.000 0.000
#> DRR006395 4 0.0790 0.9505 0.000 0.000 0.000 0.968 0.000 0.032
#> DRR006396 6 0.4526 0.0783 0.456 0.000 0.000 0.032 0.000 0.512
#> DRR006397 6 0.1296 0.8463 0.012 0.000 0.000 0.032 0.004 0.952
#> DRR006398 6 0.1296 0.8463 0.012 0.000 0.000 0.032 0.004 0.952
#> DRR006399 4 0.0790 0.9505 0.000 0.000 0.000 0.968 0.000 0.032
#> DRR006400 4 0.0790 0.9505 0.000 0.000 0.000 0.968 0.000 0.032
#> DRR006401 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006403 4 0.0790 0.9505 0.000 0.000 0.000 0.968 0.000 0.032
#> DRR006404 4 0.0713 0.9498 0.000 0.000 0.000 0.972 0.000 0.028
#> DRR006405 4 0.0692 0.9462 0.004 0.000 0.000 0.976 0.000 0.020
#> DRR006406 4 0.0692 0.9462 0.004 0.000 0.000 0.976 0.000 0.020
#> DRR006407 4 0.0777 0.9193 0.000 0.000 0.024 0.972 0.004 0.000
#> DRR006408 4 0.4165 0.5125 0.000 0.308 0.024 0.664 0.000 0.004
#> DRR006409 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006410 1 0.2325 0.8516 0.892 0.000 0.000 0.048 0.000 0.060
#> DRR006411 6 0.2972 0.7136 0.000 0.000 0.000 0.036 0.128 0.836
#> DRR006412 2 0.0146 0.9408 0.000 0.996 0.000 0.004 0.000 0.000
#> DRR006413 1 0.3266 0.6689 0.728 0.000 0.000 0.000 0.000 0.272
#> DRR006414 3 0.3298 0.7751 0.008 0.000 0.756 0.000 0.236 0.000
#> DRR006415 3 0.3050 0.7748 0.000 0.000 0.764 0.000 0.236 0.000
#> DRR006416 1 0.2633 0.8468 0.864 0.000 0.000 0.004 0.020 0.112
#> DRR006417 5 0.0291 0.7068 0.000 0.000 0.004 0.000 0.992 0.004
#> DRR006418 5 0.3974 0.4653 0.188 0.000 0.000 0.004 0.752 0.056
#> DRR006419 5 0.0935 0.6936 0.000 0.000 0.032 0.000 0.964 0.004
#> DRR006420 5 0.5556 0.1772 0.188 0.000 0.264 0.000 0.548 0.000
#> DRR006421 3 0.3288 0.7514 0.000 0.000 0.724 0.000 0.276 0.000
#> DRR006422 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006423 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006424 1 0.0260 0.9171 0.992 0.000 0.000 0.000 0.000 0.008
#> DRR006425 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006426 5 0.0964 0.7031 0.000 0.000 0.004 0.012 0.968 0.016
#> DRR006427 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006428 3 0.3323 0.7739 0.008 0.000 0.752 0.000 0.240 0.000
#> DRR006429 2 0.0405 0.9374 0.000 0.988 0.000 0.008 0.004 0.000
#> DRR006430 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006432 5 0.0820 0.7016 0.000 0.000 0.000 0.012 0.972 0.016
#> DRR006433 3 0.2632 0.6139 0.000 0.000 0.880 0.012 0.076 0.032
#> DRR006434 2 0.3196 0.8352 0.000 0.836 0.116 0.004 0.004 0.040
#> DRR006435 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006437 6 0.1649 0.8428 0.036 0.000 0.000 0.032 0.000 0.932
#> DRR006438 5 0.4381 -0.1922 0.024 0.000 0.440 0.000 0.536 0.000
#> DRR006439 3 0.3541 0.7603 0.012 0.000 0.728 0.000 0.260 0.000
#> DRR006440 2 0.4351 0.7417 0.000 0.724 0.216 0.008 0.008 0.044
#> DRR006441 2 0.0146 0.9408 0.000 0.996 0.000 0.004 0.000 0.000
#> DRR006442 3 0.3298 0.7751 0.008 0.000 0.756 0.000 0.236 0.000
#> DRR006443 2 0.4351 0.7417 0.000 0.724 0.216 0.008 0.008 0.044
#> DRR006444 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006445 1 0.3351 0.6409 0.712 0.000 0.000 0.000 0.000 0.288
#> DRR006446 2 0.0146 0.9408 0.000 0.996 0.000 0.004 0.000 0.000
#> DRR006447 6 0.1588 0.7999 0.072 0.000 0.000 0.000 0.004 0.924
#> DRR006448 4 0.0790 0.9505 0.000 0.000 0.000 0.968 0.000 0.032
#> DRR006449 1 0.2442 0.8263 0.852 0.000 0.000 0.004 0.000 0.144
#> DRR006450 1 0.3244 0.6755 0.732 0.000 0.000 0.000 0.000 0.268
#> DRR006451 4 0.0790 0.9505 0.000 0.000 0.000 0.968 0.000 0.032
#> DRR006452 1 0.3244 0.6750 0.732 0.000 0.000 0.000 0.000 0.268
#> DRR006453 1 0.1910 0.8602 0.892 0.000 0.000 0.000 0.000 0.108
#> DRR006454 6 0.1225 0.8384 0.004 0.004 0.000 0.032 0.004 0.956
#> DRR006455 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006456 3 0.2420 0.6395 0.000 0.000 0.888 0.004 0.076 0.032
#> DRR006457 3 0.3244 0.7560 0.000 0.000 0.732 0.000 0.268 0.000
#> DRR006458 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006460 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006461 2 0.4080 0.7560 0.000 0.740 0.208 0.004 0.004 0.044
#> DRR006462 6 0.4855 0.0416 0.460 0.000 0.000 0.056 0.000 0.484
#> DRR006463 2 0.4351 0.7417 0.000 0.724 0.216 0.008 0.008 0.044
#> DRR006464 5 0.4303 0.3746 0.000 0.332 0.000 0.016 0.640 0.012
#> DRR006465 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006466 3 0.5268 0.0447 0.000 0.356 0.572 0.012 0.016 0.044
#> DRR006467 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006468 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006469 2 0.0146 0.9408 0.000 0.996 0.000 0.004 0.000 0.000
#> DRR006470 5 0.0291 0.7068 0.000 0.000 0.004 0.000 0.992 0.004
#> DRR006471 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006472 5 0.0547 0.6997 0.000 0.000 0.020 0.000 0.980 0.000
#> DRR006473 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006474 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006475 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006476 2 0.0713 0.9246 0.000 0.972 0.028 0.000 0.000 0.000
#> DRR006477 3 0.1155 0.5665 0.000 0.004 0.956 0.004 0.000 0.036
#> DRR006478 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006479 3 0.6057 0.1534 0.264 0.000 0.396 0.000 0.340 0.000
#> DRR006480 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006481 5 0.3634 0.1215 0.000 0.000 0.356 0.000 0.644 0.000
#> DRR006482 6 0.1498 0.8450 0.028 0.000 0.000 0.032 0.000 0.940
#> DRR006483 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006484 3 0.3528 0.7273 0.004 0.000 0.700 0.000 0.296 0.000
#> DRR006485 2 0.4351 0.7417 0.000 0.724 0.216 0.008 0.008 0.044
#> DRR006486 1 0.0935 0.8927 0.964 0.000 0.004 0.000 0.032 0.000
#> DRR006487 3 0.3050 0.7748 0.000 0.000 0.764 0.000 0.236 0.000
#> DRR006488 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006489 1 0.0146 0.9187 0.996 0.000 0.000 0.000 0.000 0.004
#> DRR006490 3 0.3298 0.7751 0.008 0.000 0.756 0.000 0.236 0.000
#> DRR006491 3 0.3298 0.7751 0.008 0.000 0.756 0.000 0.236 0.000
#> DRR006492 1 0.0632 0.9045 0.976 0.000 0.024 0.000 0.000 0.000
#> DRR006493 3 0.3050 0.7748 0.000 0.000 0.764 0.000 0.236 0.000
#> DRR006494 1 0.0000 0.9195 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16187 rows and 121 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 1.000 0.984 0.993 0.4391 0.560 0.560
#> 3 3 0.813 0.764 0.912 0.5202 0.759 0.574
#> 4 4 0.735 0.725 0.850 0.0699 0.850 0.615
#> 5 5 0.760 0.713 0.879 0.0818 0.848 0.546
#> 6 6 0.914 0.894 0.938 0.0578 0.929 0.698
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DRR006374 2 0.000 0.985 0.000 1.000
#> DRR006375 1 0.000 0.997 1.000 0.000
#> DRR006376 1 0.000 0.997 1.000 0.000
#> DRR006377 1 0.000 0.997 1.000 0.000
#> DRR006378 2 0.000 0.985 0.000 1.000
#> DRR006379 1 0.000 0.997 1.000 0.000
#> DRR006380 2 0.000 0.985 0.000 1.000
#> DRR006381 1 0.000 0.997 1.000 0.000
#> DRR006382 2 0.000 0.985 0.000 1.000
#> DRR006383 2 0.895 0.550 0.312 0.688
#> DRR006384 2 0.000 0.985 0.000 1.000
#> DRR006385 1 0.000 0.997 1.000 0.000
#> DRR006386 2 0.000 0.985 0.000 1.000
#> DRR006387 1 0.000 0.997 1.000 0.000
#> DRR006388 1 0.000 0.997 1.000 0.000
#> DRR006389 1 0.000 0.997 1.000 0.000
#> DRR006390 2 0.000 0.985 0.000 1.000
#> DRR006391 2 0.000 0.985 0.000 1.000
#> DRR006392 1 0.000 0.997 1.000 0.000
#> DRR006393 1 0.000 0.997 1.000 0.000
#> DRR006394 2 0.000 0.985 0.000 1.000
#> DRR006395 1 0.000 0.997 1.000 0.000
#> DRR006396 1 0.000 0.997 1.000 0.000
#> DRR006397 1 0.000 0.997 1.000 0.000
#> DRR006398 1 0.000 0.997 1.000 0.000
#> DRR006399 1 0.000 0.997 1.000 0.000
#> DRR006400 1 0.000 0.997 1.000 0.000
#> DRR006401 2 0.000 0.985 0.000 1.000
#> DRR006402 2 0.000 0.985 0.000 1.000
#> DRR006403 1 0.000 0.997 1.000 0.000
#> DRR006404 1 0.000 0.997 1.000 0.000
#> DRR006405 1 0.000 0.997 1.000 0.000
#> DRR006406 1 0.000 0.997 1.000 0.000
#> DRR006407 1 0.000 0.997 1.000 0.000
#> DRR006408 2 0.000 0.985 0.000 1.000
#> DRR006409 1 0.000 0.997 1.000 0.000
#> DRR006410 1 0.000 0.997 1.000 0.000
#> DRR006411 1 0.000 0.997 1.000 0.000
#> DRR006412 2 0.000 0.985 0.000 1.000
#> DRR006413 1 0.000 0.997 1.000 0.000
#> DRR006414 1 0.000 0.997 1.000 0.000
#> DRR006415 1 0.000 0.997 1.000 0.000
#> DRR006416 1 0.000 0.997 1.000 0.000
#> DRR006417 1 0.000 0.997 1.000 0.000
#> DRR006418 1 0.000 0.997 1.000 0.000
#> DRR006419 1 0.000 0.997 1.000 0.000
#> DRR006420 1 0.000 0.997 1.000 0.000
#> DRR006421 1 0.000 0.997 1.000 0.000
#> DRR006422 2 0.000 0.985 0.000 1.000
#> DRR006423 2 0.000 0.985 0.000 1.000
#> DRR006424 1 0.000 0.997 1.000 0.000
#> DRR006425 2 0.000 0.985 0.000 1.000
#> DRR006426 1 0.000 0.997 1.000 0.000
#> DRR006427 2 0.000 0.985 0.000 1.000
#> DRR006428 1 0.000 0.997 1.000 0.000
#> DRR006429 2 0.000 0.985 0.000 1.000
#> DRR006430 1 0.000 0.997 1.000 0.000
#> DRR006431 1 0.000 0.997 1.000 0.000
#> DRR006432 1 0.000 0.997 1.000 0.000
#> DRR006433 1 0.000 0.997 1.000 0.000
#> DRR006434 2 0.000 0.985 0.000 1.000
#> DRR006435 2 0.000 0.985 0.000 1.000
#> DRR006436 2 0.000 0.985 0.000 1.000
#> DRR006437 1 0.000 0.997 1.000 0.000
#> DRR006438 1 0.000 0.997 1.000 0.000
#> DRR006439 1 0.000 0.997 1.000 0.000
#> DRR006440 2 0.000 0.985 0.000 1.000
#> DRR006441 2 0.000 0.985 0.000 1.000
#> DRR006442 1 0.000 0.997 1.000 0.000
#> DRR006443 2 0.000 0.985 0.000 1.000
#> DRR006444 2 0.000 0.985 0.000 1.000
#> DRR006445 1 0.000 0.997 1.000 0.000
#> DRR006446 2 0.000 0.985 0.000 1.000
#> DRR006447 1 0.000 0.997 1.000 0.000
#> DRR006448 1 0.000 0.997 1.000 0.000
#> DRR006449 1 0.000 0.997 1.000 0.000
#> DRR006450 1 0.000 0.997 1.000 0.000
#> DRR006451 1 0.000 0.997 1.000 0.000
#> DRR006452 1 0.000 0.997 1.000 0.000
#> DRR006453 1 0.000 0.997 1.000 0.000
#> DRR006454 1 0.000 0.997 1.000 0.000
#> DRR006455 2 0.000 0.985 0.000 1.000
#> DRR006456 1 0.000 0.997 1.000 0.000
#> DRR006457 1 0.000 0.997 1.000 0.000
#> DRR006458 1 0.000 0.997 1.000 0.000
#> DRR006459 1 0.000 0.997 1.000 0.000
#> DRR006460 2 0.000 0.985 0.000 1.000
#> DRR006461 2 0.000 0.985 0.000 1.000
#> DRR006462 1 0.000 0.997 1.000 0.000
#> DRR006463 2 0.000 0.985 0.000 1.000
#> DRR006464 2 0.814 0.667 0.252 0.748
#> DRR006465 1 0.000 0.997 1.000 0.000
#> DRR006466 1 0.808 0.664 0.752 0.248
#> DRR006467 1 0.000 0.997 1.000 0.000
#> DRR006468 2 0.000 0.985 0.000 1.000
#> DRR006469 2 0.000 0.985 0.000 1.000
#> DRR006470 1 0.000 0.997 1.000 0.000
#> DRR006471 1 0.000 0.997 1.000 0.000
#> DRR006472 1 0.000 0.997 1.000 0.000
#> DRR006473 2 0.000 0.985 0.000 1.000
#> DRR006474 2 0.000 0.985 0.000 1.000
#> DRR006475 1 0.000 0.997 1.000 0.000
#> DRR006476 2 0.000 0.985 0.000 1.000
#> DRR006477 1 0.000 0.997 1.000 0.000
#> DRR006478 1 0.000 0.997 1.000 0.000
#> DRR006479 1 0.000 0.997 1.000 0.000
#> DRR006480 1 0.000 0.997 1.000 0.000
#> DRR006481 1 0.000 0.997 1.000 0.000
#> DRR006482 1 0.000 0.997 1.000 0.000
#> DRR006483 1 0.000 0.997 1.000 0.000
#> DRR006484 1 0.000 0.997 1.000 0.000
#> DRR006485 2 0.000 0.985 0.000 1.000
#> DRR006486 1 0.000 0.997 1.000 0.000
#> DRR006487 1 0.000 0.997 1.000 0.000
#> DRR006488 2 0.000 0.985 0.000 1.000
#> DRR006489 1 0.000 0.997 1.000 0.000
#> DRR006490 1 0.000 0.997 1.000 0.000
#> DRR006491 1 0.000 0.997 1.000 0.000
#> DRR006492 1 0.000 0.997 1.000 0.000
#> DRR006493 1 0.000 0.997 1.000 0.000
#> DRR006494 1 0.000 0.997 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006375 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006376 1 0.0747 0.8747 0.984 0.000 0.016
#> DRR006377 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006378 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006379 1 0.0892 0.8723 0.980 0.000 0.020
#> DRR006380 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006381 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006382 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006383 3 0.1643 0.7992 0.000 0.044 0.956
#> DRR006384 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006385 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006386 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006387 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006388 1 0.2066 0.8436 0.940 0.000 0.060
#> DRR006389 1 0.1964 0.8470 0.944 0.000 0.056
#> DRR006390 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006391 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006392 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006393 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006394 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006395 3 0.6299 0.1937 0.476 0.000 0.524
#> DRR006396 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006397 1 0.2878 0.8086 0.904 0.000 0.096
#> DRR006398 1 0.2878 0.8086 0.904 0.000 0.096
#> DRR006399 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006400 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006401 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006403 1 0.3816 0.7285 0.852 0.000 0.148
#> DRR006404 1 0.5706 0.4315 0.680 0.000 0.320
#> DRR006405 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006406 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006407 3 0.0892 0.8213 0.020 0.000 0.980
#> DRR006408 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006409 3 0.6309 0.1615 0.496 0.000 0.504
#> DRR006410 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006411 1 0.6309 0.0742 0.504 0.000 0.496
#> DRR006412 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006413 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006414 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006415 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006416 1 0.0892 0.8723 0.980 0.000 0.020
#> DRR006417 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006418 1 0.6192 0.2427 0.580 0.000 0.420
#> DRR006419 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006420 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006421 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006422 2 0.4291 0.7661 0.000 0.820 0.180
#> DRR006423 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006424 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006425 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006426 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006427 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006428 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006429 2 0.6309 0.1931 0.000 0.504 0.496
#> DRR006430 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006431 3 0.6309 0.1615 0.496 0.000 0.504
#> DRR006432 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006433 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006434 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006435 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006437 1 0.0237 0.8813 0.996 0.000 0.004
#> DRR006438 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006439 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006440 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006441 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006442 3 0.1163 0.8176 0.028 0.000 0.972
#> DRR006443 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006444 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006445 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006446 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006447 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006448 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006449 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006450 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006451 1 0.2165 0.8397 0.936 0.000 0.064
#> DRR006452 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006453 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006454 1 0.6309 0.0742 0.504 0.000 0.496
#> DRR006455 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006456 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006457 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006458 3 0.6309 0.1615 0.496 0.000 0.504
#> DRR006459 3 0.6309 0.1615 0.496 0.000 0.504
#> DRR006460 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006461 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006462 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006463 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006464 2 0.6309 0.1931 0.000 0.504 0.496
#> DRR006465 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006466 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006467 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006468 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006469 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006470 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006471 1 0.5591 0.4469 0.696 0.000 0.304
#> DRR006472 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006473 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006474 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006475 3 0.6309 0.1615 0.496 0.000 0.504
#> DRR006476 2 0.6309 0.1931 0.000 0.504 0.496
#> DRR006477 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006478 1 0.5785 0.3779 0.668 0.000 0.332
#> DRR006479 3 0.0892 0.8230 0.020 0.000 0.980
#> DRR006480 3 0.6309 0.1615 0.496 0.000 0.504
#> DRR006481 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006482 1 0.6309 0.0633 0.500 0.000 0.500
#> DRR006483 1 0.6309 -0.1892 0.500 0.000 0.500
#> DRR006484 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006485 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006486 3 0.6309 0.1615 0.496 0.000 0.504
#> DRR006487 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006488 2 0.0000 0.9537 0.000 1.000 0.000
#> DRR006489 1 0.0000 0.8833 1.000 0.000 0.000
#> DRR006490 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006491 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006492 3 0.6302 0.1877 0.480 0.000 0.520
#> DRR006493 3 0.0000 0.8365 0.000 0.000 1.000
#> DRR006494 3 0.6309 0.1615 0.496 0.000 0.504
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006375 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006376 1 0.6119 0.696 0.680 0.152 0.168 0.000
#> DRR006377 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006378 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006379 1 0.5085 0.735 0.676 0.304 0.020 0.000
#> DRR006380 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006381 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006382 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006383 3 0.1302 0.818 0.000 0.044 0.956 0.000
#> DRR006384 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006385 1 0.4431 0.744 0.696 0.304 0.000 0.000
#> DRR006386 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006387 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006388 1 0.5905 0.705 0.636 0.304 0.060 0.000
#> DRR006389 1 0.5836 0.709 0.640 0.304 0.056 0.000
#> DRR006390 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006391 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006392 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006393 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006394 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006395 1 0.6990 0.340 0.476 0.116 0.408 0.000
#> DRR006396 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006397 1 0.6445 0.665 0.600 0.304 0.096 0.000
#> DRR006398 1 0.6445 0.665 0.600 0.304 0.096 0.000
#> DRR006399 1 0.4431 0.744 0.696 0.304 0.000 0.000
#> DRR006400 1 0.4431 0.744 0.696 0.304 0.000 0.000
#> DRR006401 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006402 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006403 1 0.5387 0.635 0.696 0.048 0.256 0.000
#> DRR006404 1 0.5085 0.735 0.676 0.304 0.020 0.000
#> DRR006405 1 0.4431 0.744 0.696 0.304 0.000 0.000
#> DRR006406 1 0.4431 0.744 0.696 0.304 0.000 0.000
#> DRR006407 3 0.5085 0.585 0.020 0.304 0.676 0.000
#> DRR006408 2 0.0000 0.380 0.000 1.000 0.000 0.000
#> DRR006409 3 0.5000 -0.215 0.496 0.000 0.504 0.000
#> DRR006410 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006411 3 0.7442 0.320 0.200 0.304 0.496 0.000
#> DRR006412 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006413 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006414 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006415 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006416 1 0.5085 0.735 0.676 0.304 0.020 0.000
#> DRR006417 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006418 3 0.7802 0.138 0.276 0.304 0.420 0.000
#> DRR006419 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006420 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006421 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006422 2 0.3969 0.208 0.000 0.804 0.180 0.016
#> DRR006423 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006424 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006425 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006426 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006427 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006428 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006429 2 0.5000 -0.368 0.000 0.504 0.496 0.000
#> DRR006430 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006431 1 0.3610 0.651 0.800 0.000 0.200 0.000
#> DRR006432 3 0.4431 0.603 0.000 0.304 0.696 0.000
#> DRR006433 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006434 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006435 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006436 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006437 1 0.4509 0.748 0.708 0.288 0.004 0.000
#> DRR006438 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006439 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006440 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006441 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006442 3 0.0921 0.835 0.028 0.000 0.972 0.000
#> DRR006443 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006444 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006445 1 0.4431 0.744 0.696 0.304 0.000 0.000
#> DRR006446 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006447 1 0.4431 0.744 0.696 0.304 0.000 0.000
#> DRR006448 1 0.4431 0.744 0.696 0.304 0.000 0.000
#> DRR006449 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006450 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006451 1 0.5972 0.702 0.632 0.304 0.064 0.000
#> DRR006452 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006453 1 0.4431 0.744 0.696 0.304 0.000 0.000
#> DRR006454 3 0.7442 0.320 0.200 0.304 0.496 0.000
#> DRR006455 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006456 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006457 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006458 1 0.3610 0.651 0.800 0.000 0.200 0.000
#> DRR006459 1 0.3610 0.651 0.800 0.000 0.200 0.000
#> DRR006460 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006461 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006462 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006463 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006464 2 0.5000 -0.368 0.000 0.504 0.496 0.000
#> DRR006465 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006466 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006467 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006468 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006469 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006470 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006471 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006472 3 0.2216 0.792 0.000 0.092 0.908 0.000
#> DRR006473 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006474 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006475 1 0.3610 0.651 0.800 0.000 0.200 0.000
#> DRR006476 2 0.5000 -0.368 0.000 0.504 0.496 0.000
#> DRR006477 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006478 1 0.0921 0.780 0.972 0.000 0.028 0.000
#> DRR006479 3 0.0707 0.841 0.020 0.000 0.980 0.000
#> DRR006480 1 0.3610 0.651 0.800 0.000 0.200 0.000
#> DRR006481 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006482 3 0.7415 0.327 0.196 0.304 0.500 0.000
#> DRR006483 1 0.3569 0.655 0.804 0.000 0.196 0.000
#> DRR006484 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006485 2 0.4431 0.812 0.000 0.696 0.000 0.304
#> DRR006486 1 0.3610 0.651 0.800 0.000 0.200 0.000
#> DRR006487 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006488 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006489 1 0.0000 0.792 1.000 0.000 0.000 0.000
#> DRR006490 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006491 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006492 3 0.4994 -0.175 0.480 0.000 0.520 0.000
#> DRR006493 3 0.0000 0.859 0.000 0.000 1.000 0.000
#> DRR006494 1 0.3610 0.651 0.800 0.000 0.200 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006375 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006376 4 0.5025 0.42270 0.124 0.000 0.172 0.704 0
#> DRR006377 3 0.4201 0.24066 0.000 0.000 0.592 0.408 0
#> DRR006378 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006379 4 0.0000 0.59286 0.000 0.000 0.000 1.000 0
#> DRR006380 2 0.0703 0.93307 0.000 0.976 0.024 0.000 0
#> DRR006381 1 0.3395 0.74325 0.764 0.000 0.000 0.236 0
#> DRR006382 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006383 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006384 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006385 4 0.4201 0.17881 0.408 0.000 0.000 0.592 0
#> DRR006386 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006387 1 0.3395 0.74325 0.764 0.000 0.000 0.236 0
#> DRR006388 4 0.4201 0.37832 0.000 0.000 0.408 0.592 0
#> DRR006389 4 0.4201 0.37832 0.000 0.000 0.408 0.592 0
#> DRR006390 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006391 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006392 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006393 1 0.3210 0.75553 0.788 0.000 0.000 0.212 0
#> DRR006394 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006395 4 0.6133 0.27521 0.216 0.000 0.220 0.564 0
#> DRR006396 1 0.3395 0.74325 0.764 0.000 0.000 0.236 0
#> DRR006397 4 0.4201 0.37832 0.000 0.000 0.408 0.592 0
#> DRR006398 4 0.4201 0.37832 0.000 0.000 0.408 0.592 0
#> DRR006399 4 0.0000 0.59286 0.000 0.000 0.000 1.000 0
#> DRR006400 4 0.0000 0.59286 0.000 0.000 0.000 1.000 0
#> DRR006401 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006402 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006403 4 0.5049 0.31501 0.060 0.000 0.296 0.644 0
#> DRR006404 4 0.2516 0.51494 0.140 0.000 0.000 0.860 0
#> DRR006405 4 0.0000 0.59286 0.000 0.000 0.000 1.000 0
#> DRR006406 4 0.0000 0.59286 0.000 0.000 0.000 1.000 0
#> DRR006407 4 0.3395 0.42784 0.000 0.000 0.236 0.764 0
#> DRR006408 4 0.3395 0.38186 0.000 0.236 0.000 0.764 0
#> DRR006409 1 0.4045 0.38788 0.644 0.000 0.356 0.000 0
#> DRR006410 1 0.3395 0.74325 0.764 0.000 0.000 0.236 0
#> DRR006411 4 0.4297 0.22167 0.000 0.000 0.472 0.528 0
#> DRR006412 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006413 1 0.3395 0.74325 0.764 0.000 0.000 0.236 0
#> DRR006414 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006415 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006416 4 0.4201 0.37832 0.000 0.000 0.408 0.592 0
#> DRR006417 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006418 4 0.4210 0.37042 0.000 0.000 0.412 0.588 0
#> DRR006419 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006420 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006421 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006422 2 0.6024 0.13048 0.000 0.532 0.132 0.336 0
#> DRR006423 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006424 1 0.3395 0.74325 0.764 0.000 0.000 0.236 0
#> DRR006425 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006426 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006427 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006428 3 0.3366 0.59350 0.232 0.000 0.768 0.000 0
#> DRR006429 3 0.4196 0.28468 0.000 0.004 0.640 0.356 0
#> DRR006430 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006431 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006432 3 0.4045 0.29036 0.000 0.000 0.644 0.356 0
#> DRR006433 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006434 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006435 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006436 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006437 4 0.4242 0.11689 0.428 0.000 0.000 0.572 0
#> DRR006438 3 0.2732 0.68566 0.160 0.000 0.840 0.000 0
#> DRR006439 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006440 2 0.3039 0.72142 0.000 0.808 0.192 0.000 0
#> DRR006441 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006442 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006443 2 0.2732 0.76681 0.000 0.840 0.160 0.000 0
#> DRR006444 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006445 4 0.4201 0.17881 0.408 0.000 0.000 0.592 0
#> DRR006446 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006447 4 0.4201 0.17881 0.408 0.000 0.000 0.592 0
#> DRR006448 4 0.0000 0.59286 0.000 0.000 0.000 1.000 0
#> DRR006449 1 0.3395 0.74325 0.764 0.000 0.000 0.236 0
#> DRR006450 1 0.3395 0.74325 0.764 0.000 0.000 0.236 0
#> DRR006451 4 0.0000 0.59286 0.000 0.000 0.000 1.000 0
#> DRR006452 1 0.3395 0.74325 0.764 0.000 0.000 0.236 0
#> DRR006453 4 0.4201 0.17881 0.408 0.000 0.000 0.592 0
#> DRR006454 3 0.4060 0.27998 0.000 0.000 0.640 0.360 0
#> DRR006455 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006456 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006457 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006458 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006459 1 0.0162 0.83935 0.996 0.000 0.004 0.000 0
#> DRR006460 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006461 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006462 1 0.3395 0.74325 0.764 0.000 0.000 0.236 0
#> DRR006463 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006464 3 0.4196 0.28468 0.000 0.004 0.640 0.356 0
#> DRR006465 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006466 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006467 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006468 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006469 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006470 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006471 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006472 3 0.1908 0.75492 0.000 0.000 0.908 0.092 0
#> DRR006473 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006474 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006475 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006476 3 0.6342 -0.02091 0.000 0.168 0.476 0.356 0
#> DRR006477 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006478 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006479 1 0.4060 0.38328 0.640 0.000 0.360 0.000 0
#> DRR006480 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006481 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006482 4 0.4201 0.37832 0.000 0.000 0.408 0.592 0
#> DRR006483 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006484 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006485 2 0.0000 0.95758 0.000 1.000 0.000 0.000 0
#> DRR006486 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
#> DRR006487 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006488 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006489 1 0.0162 0.84115 0.996 0.000 0.000 0.004 0
#> DRR006490 3 0.0609 0.83278 0.020 0.000 0.980 0.000 0
#> DRR006491 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006492 3 0.4307 -0.00949 0.500 0.000 0.500 0.000 0
#> DRR006493 3 0.0000 0.84904 0.000 0.000 1.000 0.000 0
#> DRR006494 1 0.0000 0.84222 1.000 0.000 0.000 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006375 1 0.2491 0.8565 0.836 0.000 0.000 0.164 0 0.000
#> DRR006376 4 0.1327 0.8454 0.064 0.000 0.000 0.936 0 0.000
#> DRR006377 4 0.3649 0.7913 0.004 0.000 0.112 0.800 0 0.084
#> DRR006378 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006379 4 0.0146 0.8667 0.000 0.000 0.000 0.996 0 0.004
#> DRR006380 2 0.0146 0.9721 0.000 0.996 0.004 0.000 0 0.000
#> DRR006381 1 0.3927 0.8354 0.756 0.000 0.000 0.172 0 0.072
#> DRR006382 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006383 3 0.0790 0.9441 0.000 0.032 0.968 0.000 0 0.000
#> DRR006384 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006385 6 0.1327 0.8522 0.000 0.000 0.000 0.064 0 0.936
#> DRR006386 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006387 1 0.3927 0.8354 0.756 0.000 0.000 0.172 0 0.072
#> DRR006388 6 0.0000 0.8859 0.000 0.000 0.000 0.000 0 1.000
#> DRR006389 6 0.0000 0.8859 0.000 0.000 0.000 0.000 0 1.000
#> DRR006390 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006391 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006392 1 0.1007 0.8751 0.956 0.000 0.000 0.044 0 0.000
#> DRR006393 1 0.3017 0.8610 0.840 0.000 0.000 0.108 0 0.052
#> DRR006394 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006395 4 0.3210 0.8292 0.028 0.000 0.000 0.804 0 0.168
#> DRR006396 1 0.3927 0.8354 0.756 0.000 0.000 0.172 0 0.072
#> DRR006397 6 0.0000 0.8859 0.000 0.000 0.000 0.000 0 1.000
#> DRR006398 6 0.0000 0.8859 0.000 0.000 0.000 0.000 0 1.000
#> DRR006399 4 0.0000 0.8656 0.000 0.000 0.000 1.000 0 0.000
#> DRR006400 4 0.0000 0.8656 0.000 0.000 0.000 1.000 0 0.000
#> DRR006401 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006402 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006403 4 0.0000 0.8656 0.000 0.000 0.000 1.000 0 0.000
#> DRR006404 4 0.1327 0.8714 0.000 0.000 0.000 0.936 0 0.064
#> DRR006405 4 0.2631 0.8416 0.000 0.000 0.000 0.820 0 0.180
#> DRR006406 4 0.2491 0.8517 0.000 0.000 0.000 0.836 0 0.164
#> DRR006407 4 0.3245 0.8211 0.000 0.000 0.028 0.800 0 0.172
#> DRR006408 4 0.3563 0.7911 0.000 0.108 0.000 0.800 0 0.092
#> DRR006409 3 0.3101 0.7142 0.244 0.000 0.756 0.000 0 0.000
#> DRR006410 1 0.3927 0.8354 0.756 0.000 0.000 0.172 0 0.072
#> DRR006411 6 0.0865 0.8708 0.000 0.000 0.036 0.000 0 0.964
#> DRR006412 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006413 1 0.3907 0.7928 0.756 0.000 0.000 0.068 0 0.176
#> DRR006414 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006415 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006416 6 0.0000 0.8859 0.000 0.000 0.000 0.000 0 1.000
#> DRR006417 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006418 6 0.0000 0.8859 0.000 0.000 0.000 0.000 0 1.000
#> DRR006419 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006420 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006421 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006422 2 0.4326 0.2159 0.000 0.572 0.024 0.000 0 0.404
#> DRR006423 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006424 1 0.3860 0.8386 0.764 0.000 0.000 0.164 0 0.072
#> DRR006425 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006426 3 0.1075 0.9325 0.000 0.000 0.952 0.000 0 0.048
#> DRR006427 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006428 3 0.1501 0.9127 0.076 0.000 0.924 0.000 0 0.000
#> DRR006429 6 0.1951 0.8396 0.000 0.016 0.076 0.000 0 0.908
#> DRR006430 1 0.0000 0.8746 1.000 0.000 0.000 0.000 0 0.000
#> DRR006431 1 0.0000 0.8746 1.000 0.000 0.000 0.000 0 0.000
#> DRR006432 6 0.1610 0.8379 0.000 0.000 0.084 0.000 0 0.916
#> DRR006433 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006434 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006435 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006436 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006437 6 0.5709 -0.0107 0.364 0.000 0.000 0.168 0 0.468
#> DRR006438 3 0.1007 0.9410 0.044 0.000 0.956 0.000 0 0.000
#> DRR006439 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006440 2 0.0937 0.9324 0.000 0.960 0.040 0.000 0 0.000
#> DRR006441 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006442 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006443 2 0.0937 0.9324 0.000 0.960 0.040 0.000 0 0.000
#> DRR006444 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006445 6 0.2416 0.7639 0.000 0.000 0.000 0.156 0 0.844
#> DRR006446 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006447 6 0.0000 0.8859 0.000 0.000 0.000 0.000 0 1.000
#> DRR006448 4 0.1007 0.8376 0.000 0.000 0.000 0.956 0 0.044
#> DRR006449 1 0.3894 0.8372 0.760 0.000 0.000 0.168 0 0.072
#> DRR006450 1 0.3927 0.8354 0.756 0.000 0.000 0.172 0 0.072
#> DRR006451 6 0.3076 0.6646 0.000 0.000 0.000 0.240 0 0.760
#> DRR006452 1 0.3927 0.8354 0.756 0.000 0.000 0.172 0 0.072
#> DRR006453 6 0.0000 0.8859 0.000 0.000 0.000 0.000 0 1.000
#> DRR006454 6 0.1327 0.8530 0.000 0.000 0.064 0.000 0 0.936
#> DRR006455 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006456 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006457 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006458 1 0.0000 0.8746 1.000 0.000 0.000 0.000 0 0.000
#> DRR006459 1 0.1910 0.7796 0.892 0.000 0.108 0.000 0 0.000
#> DRR006460 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006461 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006462 1 0.3744 0.8305 0.756 0.000 0.000 0.200 0 0.044
#> DRR006463 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006464 6 0.1584 0.8494 0.000 0.008 0.064 0.000 0 0.928
#> DRR006465 1 0.0000 0.8746 1.000 0.000 0.000 0.000 0 0.000
#> DRR006466 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006467 1 0.0000 0.8746 1.000 0.000 0.000 0.000 0 0.000
#> DRR006468 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006469 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006470 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006471 1 0.0000 0.8746 1.000 0.000 0.000 0.000 0 0.000
#> DRR006472 3 0.1663 0.8855 0.000 0.000 0.912 0.000 0 0.088
#> DRR006473 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006474 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006475 1 0.0000 0.8746 1.000 0.000 0.000 0.000 0 0.000
#> DRR006476 6 0.3551 0.6914 0.000 0.192 0.036 0.000 0 0.772
#> DRR006477 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006478 1 0.0000 0.8746 1.000 0.000 0.000 0.000 0 0.000
#> DRR006479 3 0.1501 0.9127 0.076 0.000 0.924 0.000 0 0.000
#> DRR006480 1 0.0000 0.8746 1.000 0.000 0.000 0.000 0 0.000
#> DRR006481 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006482 6 0.1471 0.8533 0.000 0.000 0.004 0.064 0 0.932
#> DRR006483 1 0.0000 0.8746 1.000 0.000 0.000 0.000 0 0.000
#> DRR006484 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006485 2 0.0000 0.9761 0.000 1.000 0.000 0.000 0 0.000
#> DRR006486 1 0.0713 0.8579 0.972 0.000 0.028 0.000 0 0.000
#> DRR006487 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006488 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006489 1 0.2006 0.8677 0.892 0.000 0.000 0.104 0 0.004
#> DRR006490 3 0.0458 0.9614 0.016 0.000 0.984 0.000 0 0.000
#> DRR006491 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006492 3 0.1471 0.9219 0.064 0.000 0.932 0.000 0 0.004
#> DRR006493 3 0.0000 0.9711 0.000 0.000 1.000 0.000 0 0.000
#> DRR006494 1 0.1663 0.8030 0.912 0.000 0.088 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)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.289 0.720 0.836 0.4270 0.496 0.496
#> 3 3 0.216 0.312 0.653 0.2667 0.638 0.481
#> 4 4 0.332 0.582 0.717 0.2395 0.681 0.456
#> 5 5 0.494 0.549 0.733 0.0828 0.897 0.711
#> 6 6 0.537 0.442 0.659 0.0469 0.898 0.670
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
#> DRR006374 2 0.5408 0.7939 0.124 0.876
#> DRR006375 1 0.0000 0.8773 1.000 0.000
#> DRR006376 1 0.0000 0.8773 1.000 0.000
#> DRR006377 1 0.9686 0.0708 0.604 0.396
#> DRR006378 2 0.5946 0.7959 0.144 0.856
#> DRR006379 1 0.0000 0.8773 1.000 0.000
#> DRR006380 2 0.5946 0.7969 0.144 0.856
#> DRR006381 1 0.0376 0.8749 0.996 0.004
#> DRR006382 2 0.5408 0.7939 0.124 0.876
#> DRR006383 2 0.9710 0.6075 0.400 0.600
#> DRR006384 2 0.6048 0.7967 0.148 0.852
#> DRR006385 1 0.0376 0.8749 0.996 0.004
#> DRR006386 2 0.1633 0.7055 0.024 0.976
#> DRR006387 1 0.0000 0.8773 1.000 0.000
#> DRR006388 1 0.3274 0.8384 0.940 0.060
#> DRR006389 1 0.3274 0.8384 0.940 0.060
#> DRR006390 2 0.5946 0.7959 0.144 0.856
#> DRR006391 2 0.5946 0.7959 0.144 0.856
#> DRR006392 1 0.0000 0.8773 1.000 0.000
#> DRR006393 1 0.0000 0.8773 1.000 0.000
#> DRR006394 2 0.5946 0.7959 0.144 0.856
#> DRR006395 1 0.0000 0.8773 1.000 0.000
#> DRR006396 1 0.0000 0.8773 1.000 0.000
#> DRR006397 1 0.2778 0.8486 0.952 0.048
#> DRR006398 1 0.2778 0.8486 0.952 0.048
#> DRR006399 1 0.0000 0.8773 1.000 0.000
#> DRR006400 1 0.0000 0.8773 1.000 0.000
#> DRR006401 2 0.6048 0.7967 0.148 0.852
#> DRR006402 2 0.6048 0.7967 0.148 0.852
#> DRR006403 1 0.0000 0.8773 1.000 0.000
#> DRR006404 1 0.0000 0.8773 1.000 0.000
#> DRR006405 1 0.0000 0.8773 1.000 0.000
#> DRR006406 1 0.0000 0.8773 1.000 0.000
#> DRR006407 1 0.4298 0.8063 0.912 0.088
#> DRR006408 2 0.9286 0.6761 0.344 0.656
#> DRR006409 1 0.7139 0.6696 0.804 0.196
#> DRR006410 1 0.0000 0.8773 1.000 0.000
#> DRR006411 1 0.8555 0.4539 0.720 0.280
#> DRR006412 2 0.5946 0.7959 0.144 0.856
#> DRR006413 1 0.0000 0.8773 1.000 0.000
#> DRR006414 2 0.9710 0.6075 0.400 0.600
#> DRR006415 2 0.9710 0.6075 0.400 0.600
#> DRR006416 1 0.3584 0.8307 0.932 0.068
#> DRR006417 1 0.9815 0.0099 0.580 0.420
#> DRR006418 1 0.6247 0.7151 0.844 0.156
#> DRR006419 1 0.9732 0.0893 0.596 0.404
#> DRR006420 1 0.9732 0.0893 0.596 0.404
#> DRR006421 2 0.9710 0.6075 0.400 0.600
#> DRR006422 2 0.6247 0.7949 0.156 0.844
#> DRR006423 2 0.5946 0.7959 0.144 0.856
#> DRR006424 1 0.0376 0.8749 0.996 0.004
#> DRR006425 2 0.6048 0.7967 0.148 0.852
#> DRR006426 1 0.9732 0.0893 0.596 0.404
#> DRR006427 2 0.5178 0.7784 0.116 0.884
#> DRR006428 2 0.9710 0.6075 0.400 0.600
#> DRR006429 2 0.6343 0.7947 0.160 0.840
#> DRR006430 1 0.0000 0.8773 1.000 0.000
#> DRR006431 1 0.0938 0.8703 0.988 0.012
#> DRR006432 1 0.9661 0.1054 0.608 0.392
#> DRR006433 2 0.9710 0.6075 0.400 0.600
#> DRR006434 2 0.5408 0.7939 0.124 0.876
#> DRR006435 2 0.1633 0.7055 0.024 0.976
#> DRR006436 2 0.1633 0.7055 0.024 0.976
#> DRR006437 1 0.0000 0.8773 1.000 0.000
#> DRR006438 2 0.9970 0.4333 0.468 0.532
#> DRR006439 2 0.9710 0.6075 0.400 0.600
#> DRR006440 2 0.5408 0.7939 0.124 0.876
#> DRR006441 2 0.5946 0.7959 0.144 0.856
#> DRR006442 2 0.9710 0.6075 0.400 0.600
#> DRR006443 2 0.5408 0.7939 0.124 0.876
#> DRR006444 2 0.1633 0.7055 0.024 0.976
#> DRR006445 1 0.0000 0.8773 1.000 0.000
#> DRR006446 2 0.5946 0.7959 0.144 0.856
#> DRR006447 1 0.0672 0.8741 0.992 0.008
#> DRR006448 1 0.0000 0.8773 1.000 0.000
#> DRR006449 1 0.0000 0.8773 1.000 0.000
#> DRR006450 1 0.0376 0.8749 0.996 0.004
#> DRR006451 1 0.0000 0.8773 1.000 0.000
#> DRR006452 1 0.0000 0.8773 1.000 0.000
#> DRR006453 1 0.0376 0.8749 0.996 0.004
#> DRR006454 1 0.6887 0.6794 0.816 0.184
#> DRR006455 2 0.1633 0.7055 0.024 0.976
#> DRR006456 2 0.9710 0.6075 0.400 0.600
#> DRR006457 2 0.9710 0.6075 0.400 0.600
#> DRR006458 1 0.1633 0.8609 0.976 0.024
#> DRR006459 1 0.1633 0.8609 0.976 0.024
#> DRR006460 2 0.6048 0.7967 0.148 0.852
#> DRR006461 2 0.5408 0.7939 0.124 0.876
#> DRR006462 1 0.0000 0.8773 1.000 0.000
#> DRR006463 2 0.5408 0.7939 0.124 0.876
#> DRR006464 2 0.9970 0.4311 0.468 0.532
#> DRR006465 1 0.0000 0.8773 1.000 0.000
#> DRR006466 2 0.9710 0.6075 0.400 0.600
#> DRR006467 1 0.0376 0.8749 0.996 0.004
#> DRR006468 2 0.1633 0.7055 0.024 0.976
#> DRR006469 2 0.5946 0.7959 0.144 0.856
#> DRR006470 1 0.9732 0.0893 0.596 0.404
#> DRR006471 1 0.0938 0.8703 0.988 0.012
#> DRR006472 2 0.9710 0.6075 0.400 0.600
#> DRR006473 2 0.6048 0.7967 0.148 0.852
#> DRR006474 2 0.6048 0.7967 0.148 0.852
#> DRR006475 1 0.7745 0.6040 0.772 0.228
#> DRR006476 2 0.6247 0.7955 0.156 0.844
#> DRR006477 2 0.9710 0.6075 0.400 0.600
#> DRR006478 1 0.0376 0.8753 0.996 0.004
#> DRR006479 2 1.0000 0.3400 0.496 0.504
#> DRR006480 1 0.6148 0.7361 0.848 0.152
#> DRR006481 2 0.9710 0.6075 0.400 0.600
#> DRR006482 1 0.0938 0.8731 0.988 0.012
#> DRR006483 1 0.1414 0.8642 0.980 0.020
#> DRR006484 2 0.9710 0.6075 0.400 0.600
#> DRR006485 2 0.5408 0.7939 0.124 0.876
#> DRR006486 1 0.9732 0.0893 0.596 0.404
#> DRR006487 2 0.9710 0.6075 0.400 0.600
#> DRR006488 2 0.1633 0.7055 0.024 0.976
#> DRR006489 1 0.0376 0.8749 0.996 0.004
#> DRR006490 2 0.9710 0.6075 0.400 0.600
#> DRR006491 2 0.9710 0.6075 0.400 0.600
#> DRR006492 2 0.9922 0.4931 0.448 0.552
#> DRR006493 2 0.9710 0.6075 0.400 0.600
#> DRR006494 1 0.7056 0.6731 0.808 0.192
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 3 0.8111 0.63733 0.264 0.112 0.624
#> DRR006375 1 0.5254 0.53206 0.736 0.000 0.264
#> DRR006376 1 0.4062 0.55940 0.836 0.000 0.164
#> DRR006377 1 0.6372 0.36661 0.756 0.176 0.068
#> DRR006378 2 0.8535 0.17740 0.404 0.500 0.096
#> DRR006379 1 0.0424 0.55942 0.992 0.000 0.008
#> DRR006380 3 0.9092 0.60312 0.304 0.168 0.528
#> DRR006381 1 0.7844 0.45916 0.624 0.084 0.292
#> DRR006382 3 0.5896 0.73972 0.292 0.008 0.700
#> DRR006383 1 0.9259 -0.47781 0.440 0.156 0.404
#> DRR006384 3 0.9018 0.58669 0.276 0.176 0.548
#> DRR006385 1 0.6193 0.50959 0.692 0.016 0.292
#> DRR006386 2 0.6200 0.18285 0.012 0.676 0.312
#> DRR006387 1 0.4346 0.55439 0.816 0.000 0.184
#> DRR006388 1 0.6974 0.44507 0.728 0.168 0.104
#> DRR006389 1 0.6974 0.44507 0.728 0.168 0.104
#> DRR006390 2 0.7642 0.17117 0.248 0.660 0.092
#> DRR006391 2 0.7642 0.17117 0.248 0.660 0.092
#> DRR006392 1 0.5722 0.51592 0.704 0.004 0.292
#> DRR006393 1 0.5058 0.53543 0.756 0.000 0.244
#> DRR006394 2 0.8535 0.17740 0.404 0.500 0.096
#> DRR006395 1 0.2261 0.56928 0.932 0.000 0.068
#> DRR006396 1 0.5178 0.53558 0.744 0.000 0.256
#> DRR006397 1 0.7309 0.44096 0.708 0.168 0.124
#> DRR006398 1 0.7113 0.44396 0.720 0.168 0.112
#> DRR006399 1 0.1753 0.54455 0.952 0.048 0.000
#> DRR006400 1 0.1753 0.54455 0.952 0.048 0.000
#> DRR006401 2 0.9681 -0.31889 0.284 0.460 0.256
#> DRR006402 2 0.9681 -0.31889 0.284 0.460 0.256
#> DRR006403 1 0.0424 0.55912 0.992 0.000 0.008
#> DRR006404 1 0.1411 0.56545 0.964 0.000 0.036
#> DRR006405 1 0.4346 0.55428 0.816 0.000 0.184
#> DRR006406 1 0.4346 0.55428 0.816 0.000 0.184
#> DRR006407 1 0.5466 0.42753 0.800 0.160 0.040
#> DRR006408 1 0.9066 -0.13845 0.540 0.284 0.176
#> DRR006409 1 0.1163 0.54707 0.972 0.000 0.028
#> DRR006410 1 0.3941 0.56172 0.844 0.000 0.156
#> DRR006411 1 0.7775 0.41025 0.676 0.168 0.156
#> DRR006412 2 0.7931 0.15652 0.284 0.624 0.092
#> DRR006413 1 0.3267 0.57025 0.884 0.000 0.116
#> DRR006414 1 0.9488 -0.19070 0.480 0.312 0.208
#> DRR006415 1 0.9424 -0.36061 0.472 0.188 0.340
#> DRR006416 1 0.6719 0.44207 0.744 0.160 0.096
#> DRR006417 1 0.6171 0.38079 0.776 0.144 0.080
#> DRR006418 1 0.6792 0.42822 0.744 0.132 0.124
#> DRR006419 1 0.6979 0.41528 0.732 0.140 0.128
#> DRR006420 1 0.7524 0.36934 0.692 0.180 0.128
#> DRR006421 1 0.9046 -0.08282 0.528 0.312 0.160
#> DRR006422 2 0.8190 0.14714 0.432 0.496 0.072
#> DRR006423 2 0.7642 0.17117 0.248 0.660 0.092
#> DRR006424 1 0.5497 0.51718 0.708 0.000 0.292
#> DRR006425 2 0.8474 0.17924 0.404 0.504 0.092
#> DRR006426 1 0.6714 0.40846 0.748 0.140 0.112
#> DRR006427 2 0.8414 0.04437 0.092 0.528 0.380
#> DRR006428 1 0.9517 -0.19962 0.476 0.312 0.212
#> DRR006429 2 0.8595 0.17280 0.404 0.496 0.100
#> DRR006430 1 0.5291 0.52895 0.732 0.000 0.268
#> DRR006431 1 0.3412 0.56700 0.876 0.000 0.124
#> DRR006432 1 0.6714 0.40808 0.748 0.140 0.112
#> DRR006433 1 0.9546 -0.20854 0.472 0.312 0.216
#> DRR006434 3 0.6161 0.73249 0.288 0.016 0.696
#> DRR006435 2 0.6200 0.18285 0.012 0.676 0.312
#> DRR006436 2 0.6200 0.18285 0.012 0.676 0.312
#> DRR006437 1 0.5497 0.51718 0.708 0.000 0.292
#> DRR006438 1 0.6349 0.37483 0.768 0.140 0.092
#> DRR006439 1 0.9392 -0.16430 0.492 0.312 0.196
#> DRR006440 3 0.8896 0.61101 0.292 0.156 0.552
#> DRR006441 2 0.8535 0.17740 0.404 0.500 0.096
#> DRR006442 1 0.9546 -0.20854 0.472 0.312 0.216
#> DRR006443 3 0.8849 0.61612 0.292 0.152 0.556
#> DRR006444 2 0.6200 0.18285 0.012 0.676 0.312
#> DRR006445 1 0.5178 0.53558 0.744 0.000 0.256
#> DRR006446 2 0.7931 0.15652 0.284 0.624 0.092
#> DRR006447 1 0.2878 0.56261 0.904 0.000 0.096
#> DRR006448 1 0.1753 0.54455 0.952 0.048 0.000
#> DRR006449 1 0.4654 0.54627 0.792 0.000 0.208
#> DRR006450 1 0.6193 0.50959 0.692 0.016 0.292
#> DRR006451 1 0.1031 0.56313 0.976 0.000 0.024
#> DRR006452 1 0.5397 0.52462 0.720 0.000 0.280
#> DRR006453 1 0.7916 0.45532 0.620 0.088 0.292
#> DRR006454 1 0.8561 0.00718 0.528 0.368 0.104
#> DRR006455 2 0.6200 0.18285 0.012 0.676 0.312
#> DRR006456 3 0.7583 0.45691 0.468 0.040 0.492
#> DRR006457 1 0.9546 -0.20854 0.472 0.312 0.216
#> DRR006458 1 0.2448 0.56966 0.924 0.000 0.076
#> DRR006459 1 0.2165 0.56890 0.936 0.000 0.064
#> DRR006460 3 0.8976 0.58430 0.276 0.172 0.552
#> DRR006461 3 0.5722 0.73945 0.292 0.004 0.704
#> DRR006462 1 0.4654 0.54627 0.792 0.000 0.208
#> DRR006463 3 0.5497 0.73979 0.292 0.000 0.708
#> DRR006464 1 0.7607 0.22238 0.644 0.280 0.076
#> DRR006465 1 0.5138 0.53364 0.748 0.000 0.252
#> DRR006466 1 0.9836 -0.33579 0.420 0.312 0.268
#> DRR006467 1 0.8896 0.38537 0.552 0.156 0.292
#> DRR006468 2 0.6200 0.18285 0.012 0.676 0.312
#> DRR006469 2 0.8519 0.18193 0.396 0.508 0.096
#> DRR006470 1 0.6500 0.40080 0.760 0.140 0.100
#> DRR006471 1 0.2959 0.54983 0.900 0.000 0.100
#> DRR006472 1 0.8181 0.07034 0.592 0.312 0.096
#> DRR006473 2 0.8089 0.14466 0.308 0.600 0.092
#> DRR006474 3 0.9886 0.49980 0.276 0.320 0.404
#> DRR006475 1 0.2448 0.54820 0.924 0.000 0.076
#> DRR006476 1 0.9724 -0.36388 0.412 0.364 0.224
#> DRR006477 1 0.9180 -0.40158 0.472 0.152 0.376
#> DRR006478 1 0.4887 0.54335 0.772 0.000 0.228
#> DRR006479 1 0.6271 0.37884 0.772 0.140 0.088
#> DRR006480 1 0.0237 0.55543 0.996 0.000 0.004
#> DRR006481 1 0.9392 -0.16430 0.492 0.312 0.196
#> DRR006482 1 0.7003 0.51317 0.692 0.060 0.248
#> DRR006483 1 0.3267 0.56212 0.884 0.000 0.116
#> DRR006484 1 0.9488 -0.19070 0.480 0.312 0.208
#> DRR006485 3 0.5497 0.73979 0.292 0.000 0.708
#> DRR006486 1 0.6918 0.41990 0.736 0.136 0.128
#> DRR006487 1 0.9212 -0.39695 0.472 0.156 0.372
#> DRR006488 2 0.6200 0.18285 0.012 0.676 0.312
#> DRR006489 1 0.8752 0.39833 0.564 0.144 0.292
#> DRR006490 1 0.9488 -0.19070 0.480 0.312 0.208
#> DRR006491 1 0.9546 -0.20854 0.472 0.312 0.216
#> DRR006492 1 0.7306 0.24408 0.684 0.236 0.080
#> DRR006493 1 0.9212 -0.39695 0.472 0.156 0.372
#> DRR006494 1 0.0237 0.55543 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.5353 0.3328 0.000 0.556 0.432 0.012
#> DRR006375 1 0.0000 0.7942 1.000 0.000 0.000 0.000
#> DRR006376 1 0.4605 0.7540 0.796 0.132 0.072 0.000
#> DRR006377 3 0.8376 0.3536 0.160 0.364 0.432 0.044
#> DRR006378 2 0.7407 0.6878 0.160 0.632 0.052 0.156
#> DRR006379 1 0.5186 0.7267 0.752 0.164 0.084 0.000
#> DRR006380 2 0.4831 0.4652 0.012 0.740 0.236 0.012
#> DRR006381 1 0.0927 0.7934 0.976 0.000 0.008 0.016
#> DRR006382 2 0.5257 0.3142 0.000 0.548 0.444 0.008
#> DRR006383 3 0.5393 0.3214 0.028 0.272 0.692 0.008
#> DRR006384 2 0.4434 0.4782 0.000 0.756 0.228 0.016
#> DRR006385 1 0.2275 0.7853 0.928 0.004 0.020 0.048
#> DRR006386 4 0.0000 0.9851 0.000 0.000 0.000 1.000
#> DRR006387 1 0.3862 0.7354 0.824 0.152 0.024 0.000
#> DRR006388 1 0.6177 0.6558 0.712 0.020 0.116 0.152
#> DRR006389 1 0.6177 0.6558 0.712 0.020 0.116 0.152
#> DRR006390 2 0.6298 0.6557 0.156 0.676 0.004 0.164
#> DRR006391 2 0.6298 0.6557 0.156 0.676 0.004 0.164
#> DRR006392 1 0.0188 0.7942 0.996 0.004 0.000 0.000
#> DRR006393 1 0.0707 0.7993 0.980 0.000 0.020 0.000
#> DRR006394 2 0.7407 0.6878 0.160 0.632 0.052 0.156
#> DRR006395 1 0.5537 0.6717 0.688 0.256 0.056 0.000
#> DRR006396 1 0.0895 0.7996 0.976 0.004 0.020 0.000
#> DRR006397 1 0.6499 0.6452 0.700 0.036 0.112 0.152
#> DRR006398 1 0.6583 0.6413 0.696 0.040 0.112 0.152
#> DRR006399 1 0.6281 0.6597 0.660 0.232 0.104 0.004
#> DRR006400 1 0.6281 0.6597 0.660 0.232 0.104 0.004
#> DRR006401 2 0.3758 0.5408 0.000 0.848 0.104 0.048
#> DRR006402 2 0.3758 0.5408 0.000 0.848 0.104 0.048
#> DRR006403 1 0.6104 0.6620 0.664 0.232 0.104 0.000
#> DRR006404 1 0.5938 0.6739 0.676 0.232 0.092 0.000
#> DRR006405 1 0.2125 0.7838 0.920 0.004 0.076 0.000
#> DRR006406 1 0.3617 0.7821 0.860 0.064 0.076 0.000
#> DRR006407 3 0.8274 0.1138 0.308 0.312 0.368 0.012
#> DRR006408 2 0.4524 0.5055 0.052 0.816 0.120 0.012
#> DRR006409 1 0.7227 0.5054 0.544 0.256 0.200 0.000
#> DRR006410 1 0.3910 0.7323 0.820 0.156 0.024 0.000
#> DRR006411 1 0.8816 -0.1281 0.404 0.096 0.372 0.128
#> DRR006412 2 0.6394 0.6608 0.156 0.676 0.008 0.160
#> DRR006413 1 0.1486 0.7998 0.960 0.008 0.024 0.008
#> DRR006414 3 0.3013 0.5850 0.080 0.032 0.888 0.000
#> DRR006415 3 0.2623 0.5730 0.028 0.064 0.908 0.000
#> DRR006416 1 0.8140 0.1942 0.484 0.040 0.328 0.148
#> DRR006417 3 0.6990 0.4739 0.304 0.144 0.552 0.000
#> DRR006418 3 0.6979 0.4283 0.344 0.128 0.528 0.000
#> DRR006419 3 0.6951 0.4741 0.304 0.140 0.556 0.000
#> DRR006420 3 0.6806 0.4501 0.344 0.112 0.544 0.000
#> DRR006421 3 0.4731 0.5751 0.060 0.160 0.780 0.000
#> DRR006422 2 0.8919 0.5702 0.184 0.504 0.156 0.156
#> DRR006423 2 0.7446 0.6883 0.156 0.628 0.052 0.164
#> DRR006424 1 0.0707 0.7993 0.980 0.000 0.020 0.000
#> DRR006425 2 0.7546 0.6868 0.160 0.624 0.060 0.156
#> DRR006426 3 0.7007 0.4689 0.308 0.144 0.548 0.000
#> DRR006427 4 0.2197 0.8863 0.004 0.080 0.000 0.916
#> DRR006428 3 0.1798 0.5846 0.040 0.016 0.944 0.000
#> DRR006429 2 0.8326 0.6122 0.160 0.568 0.116 0.156
#> DRR006430 1 0.0336 0.7942 0.992 0.008 0.000 0.000
#> DRR006431 1 0.4100 0.7293 0.816 0.148 0.036 0.000
#> DRR006432 3 0.7007 0.4689 0.308 0.144 0.548 0.000
#> DRR006433 3 0.4057 0.5565 0.028 0.160 0.812 0.000
#> DRR006434 3 0.5295 -0.3060 0.000 0.488 0.504 0.008
#> DRR006435 4 0.0000 0.9851 0.000 0.000 0.000 1.000
#> DRR006436 4 0.0000 0.9851 0.000 0.000 0.000 1.000
#> DRR006437 1 0.0707 0.7993 0.980 0.000 0.020 0.000
#> DRR006438 3 0.6401 0.5415 0.176 0.172 0.652 0.000
#> DRR006439 3 0.4163 0.5864 0.076 0.096 0.828 0.000
#> DRR006440 3 0.5295 -0.3060 0.000 0.488 0.504 0.008
#> DRR006441 2 0.7478 0.6867 0.160 0.628 0.056 0.156
#> DRR006442 3 0.1118 0.5782 0.036 0.000 0.964 0.000
#> DRR006443 3 0.5294 -0.3029 0.000 0.484 0.508 0.008
#> DRR006444 4 0.0000 0.9851 0.000 0.000 0.000 1.000
#> DRR006445 1 0.0817 0.7996 0.976 0.000 0.024 0.000
#> DRR006446 2 0.6627 0.6698 0.156 0.668 0.016 0.160
#> DRR006447 1 0.4694 0.7474 0.824 0.044 0.084 0.048
#> DRR006448 1 0.5706 0.7082 0.724 0.168 0.104 0.004
#> DRR006449 1 0.0895 0.7996 0.976 0.004 0.020 0.000
#> DRR006450 1 0.1082 0.7991 0.972 0.004 0.020 0.004
#> DRR006451 1 0.2402 0.7839 0.912 0.012 0.076 0.000
#> DRR006452 1 0.0707 0.7993 0.980 0.000 0.020 0.000
#> DRR006453 1 0.2674 0.7725 0.908 0.004 0.020 0.068
#> DRR006454 3 0.9229 0.1779 0.348 0.120 0.376 0.156
#> DRR006455 4 0.0000 0.9851 0.000 0.000 0.000 1.000
#> DRR006456 3 0.0921 0.5760 0.028 0.000 0.972 0.000
#> DRR006457 3 0.1624 0.5826 0.028 0.020 0.952 0.000
#> DRR006458 1 0.3821 0.7542 0.840 0.120 0.040 0.000
#> DRR006459 1 0.4290 0.7285 0.800 0.164 0.036 0.000
#> DRR006460 2 0.4434 0.4782 0.000 0.756 0.228 0.016
#> DRR006461 3 0.5294 -0.3029 0.000 0.484 0.508 0.008
#> DRR006462 1 0.0895 0.7996 0.976 0.004 0.020 0.000
#> DRR006463 3 0.5294 -0.3029 0.000 0.484 0.508 0.008
#> DRR006464 3 0.9139 0.3084 0.244 0.176 0.456 0.124
#> DRR006465 1 0.0895 0.7994 0.976 0.004 0.020 0.000
#> DRR006466 3 0.3367 0.5743 0.028 0.108 0.864 0.000
#> DRR006467 1 0.3032 0.7212 0.868 0.008 0.000 0.124
#> DRR006468 4 0.0000 0.9851 0.000 0.000 0.000 1.000
#> DRR006469 2 0.7373 0.6875 0.160 0.632 0.048 0.160
#> DRR006470 3 0.6990 0.4739 0.304 0.144 0.552 0.000
#> DRR006471 1 0.3498 0.7098 0.832 0.008 0.160 0.000
#> DRR006472 3 0.6889 0.4970 0.232 0.176 0.592 0.000
#> DRR006473 2 0.7478 0.6877 0.156 0.628 0.056 0.160
#> DRR006474 2 0.7556 0.5535 0.060 0.592 0.256 0.092
#> DRR006475 1 0.6523 0.0884 0.564 0.088 0.348 0.000
#> DRR006476 2 0.5219 0.5139 0.060 0.780 0.136 0.024
#> DRR006477 3 0.5026 0.4632 0.028 0.224 0.740 0.008
#> DRR006478 1 0.1302 0.7966 0.956 0.000 0.044 0.000
#> DRR006479 3 0.6675 0.5232 0.228 0.156 0.616 0.000
#> DRR006480 1 0.5440 0.6718 0.736 0.160 0.104 0.000
#> DRR006481 3 0.3810 0.5861 0.060 0.092 0.848 0.000
#> DRR006482 1 0.6464 0.6490 0.720 0.096 0.072 0.112
#> DRR006483 1 0.2530 0.7684 0.896 0.004 0.100 0.000
#> DRR006484 3 0.3082 0.5836 0.032 0.084 0.884 0.000
#> DRR006485 3 0.5294 -0.3029 0.000 0.484 0.508 0.008
#> DRR006486 3 0.6738 0.4362 0.352 0.104 0.544 0.000
#> DRR006487 3 0.2623 0.5730 0.028 0.064 0.908 0.000
#> DRR006488 4 0.0000 0.9851 0.000 0.000 0.000 1.000
#> DRR006489 1 0.2053 0.7603 0.924 0.004 0.000 0.072
#> DRR006490 3 0.1677 0.5835 0.040 0.012 0.948 0.000
#> DRR006491 3 0.1211 0.5787 0.040 0.000 0.960 0.000
#> DRR006492 1 0.7889 0.0897 0.380 0.316 0.304 0.000
#> DRR006493 3 0.0921 0.5760 0.028 0.000 0.972 0.000
#> DRR006494 1 0.6075 0.6144 0.680 0.192 0.128 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.4251 0.7973 0.000 0.672 0.316 0.012 0.000
#> DRR006375 1 0.3857 0.3187 0.688 0.000 0.000 0.312 0.000
#> DRR006376 1 0.3426 0.5419 0.860 0.032 0.040 0.068 0.000
#> DRR006377 3 0.7379 0.2956 0.124 0.120 0.528 0.228 0.000
#> DRR006378 2 0.3978 0.7583 0.000 0.804 0.144 0.036 0.016
#> DRR006379 1 0.4267 0.5440 0.800 0.116 0.024 0.060 0.000
#> DRR006380 2 0.4405 0.8028 0.004 0.696 0.280 0.020 0.000
#> DRR006381 1 0.4705 0.2528 0.504 0.008 0.004 0.484 0.000
#> DRR006382 2 0.4473 0.7897 0.000 0.656 0.324 0.020 0.000
#> DRR006383 3 0.3630 0.5347 0.000 0.204 0.780 0.016 0.000
#> DRR006384 2 0.4650 0.8024 0.004 0.684 0.280 0.032 0.000
#> DRR006385 1 0.3283 0.5732 0.848 0.008 0.028 0.116 0.000
#> DRR006386 5 0.0162 0.9290 0.000 0.004 0.000 0.000 0.996
#> DRR006387 1 0.1792 0.5668 0.916 0.000 0.000 0.084 0.000
#> DRR006388 1 0.5944 0.3031 0.564 0.044 0.040 0.352 0.000
#> DRR006389 1 0.5944 0.3031 0.564 0.044 0.040 0.352 0.000
#> DRR006390 2 0.4381 0.7435 0.000 0.788 0.128 0.064 0.020
#> DRR006391 2 0.4381 0.7435 0.000 0.788 0.128 0.064 0.020
#> DRR006392 1 0.3857 0.3187 0.688 0.000 0.000 0.312 0.000
#> DRR006393 1 0.2193 0.5676 0.900 0.000 0.008 0.092 0.000
#> DRR006394 2 0.4540 0.7301 0.016 0.772 0.140 0.072 0.000
#> DRR006395 1 0.3962 0.4748 0.800 0.000 0.112 0.088 0.000
#> DRR006396 1 0.0693 0.5827 0.980 0.008 0.000 0.012 0.000
#> DRR006397 1 0.6088 0.2959 0.556 0.064 0.032 0.348 0.000
#> DRR006398 1 0.6141 0.2915 0.552 0.068 0.032 0.348 0.000
#> DRR006399 1 0.6026 0.4673 0.672 0.108 0.044 0.172 0.004
#> DRR006400 1 0.6026 0.4673 0.672 0.108 0.044 0.172 0.004
#> DRR006401 2 0.5038 0.8037 0.004 0.656 0.288 0.052 0.000
#> DRR006402 2 0.5038 0.8037 0.004 0.656 0.288 0.052 0.000
#> DRR006403 1 0.5814 0.4760 0.700 0.108 0.076 0.116 0.000
#> DRR006404 1 0.5031 0.5304 0.752 0.108 0.036 0.104 0.000
#> DRR006405 1 0.2374 0.5589 0.912 0.016 0.020 0.052 0.000
#> DRR006406 1 0.2208 0.5626 0.916 0.012 0.012 0.060 0.000
#> DRR006407 1 0.8183 -0.2635 0.380 0.124 0.264 0.232 0.000
#> DRR006408 2 0.4911 0.6840 0.012 0.740 0.144 0.104 0.000
#> DRR006409 1 0.6417 -0.3778 0.424 0.000 0.404 0.172 0.000
#> DRR006410 1 0.1608 0.5717 0.928 0.000 0.000 0.072 0.000
#> DRR006411 3 0.7330 0.3483 0.112 0.140 0.540 0.208 0.000
#> DRR006412 2 0.4381 0.7435 0.000 0.788 0.128 0.064 0.020
#> DRR006413 1 0.6031 -0.1518 0.580 0.012 0.300 0.108 0.000
#> DRR006414 3 0.1251 0.7098 0.000 0.008 0.956 0.036 0.000
#> DRR006415 3 0.1121 0.7031 0.000 0.044 0.956 0.000 0.000
#> DRR006416 3 0.8154 -0.2293 0.292 0.100 0.324 0.284 0.000
#> DRR006417 3 0.4588 0.6287 0.000 0.136 0.748 0.116 0.000
#> DRR006418 3 0.5895 0.5678 0.064 0.140 0.688 0.108 0.000
#> DRR006419 3 0.4588 0.6287 0.000 0.136 0.748 0.116 0.000
#> DRR006420 3 0.4683 0.6331 0.016 0.068 0.756 0.160 0.000
#> DRR006421 3 0.2408 0.7018 0.000 0.016 0.892 0.092 0.000
#> DRR006422 2 0.6621 0.6746 0.036 0.556 0.280 0.128 0.000
#> DRR006423 2 0.3902 0.7755 0.000 0.804 0.152 0.028 0.016
#> DRR006424 1 0.4446 0.3178 0.592 0.008 0.000 0.400 0.000
#> DRR006425 2 0.4174 0.7761 0.016 0.776 0.180 0.028 0.000
#> DRR006426 3 0.4648 0.6289 0.004 0.136 0.752 0.108 0.000
#> DRR006427 5 0.5433 0.2513 0.000 0.288 0.092 0.000 0.620
#> DRR006428 3 0.1124 0.7133 0.004 0.000 0.960 0.036 0.000
#> DRR006429 2 0.5814 0.6190 0.016 0.652 0.196 0.136 0.000
#> DRR006430 1 0.3913 0.3140 0.676 0.000 0.000 0.324 0.000
#> DRR006431 1 0.5484 0.1282 0.540 0.000 0.068 0.392 0.000
#> DRR006432 3 0.4648 0.6289 0.004 0.136 0.752 0.108 0.000
#> DRR006433 3 0.1364 0.7191 0.012 0.000 0.952 0.036 0.000
#> DRR006434 2 0.4437 0.7942 0.000 0.664 0.316 0.020 0.000
#> DRR006435 5 0.0162 0.9290 0.000 0.004 0.000 0.000 0.996
#> DRR006436 5 0.0162 0.9290 0.000 0.004 0.000 0.000 0.996
#> DRR006437 1 0.2462 0.5773 0.880 0.008 0.000 0.112 0.000
#> DRR006438 3 0.3608 0.6705 0.000 0.040 0.812 0.148 0.000
#> DRR006439 3 0.1026 0.7148 0.004 0.004 0.968 0.024 0.000
#> DRR006440 2 0.4558 0.7887 0.000 0.652 0.324 0.024 0.000
#> DRR006441 2 0.4255 0.7814 0.000 0.772 0.180 0.032 0.016
#> DRR006442 3 0.1251 0.7098 0.000 0.008 0.956 0.036 0.000
#> DRR006443 2 0.4558 0.7887 0.000 0.652 0.324 0.024 0.000
#> DRR006444 5 0.0162 0.9290 0.000 0.004 0.000 0.000 0.996
#> DRR006445 1 0.2193 0.5783 0.900 0.008 0.000 0.092 0.000
#> DRR006446 2 0.3972 0.7557 0.000 0.812 0.128 0.040 0.020
#> DRR006447 1 0.7089 0.2206 0.568 0.140 0.096 0.196 0.000
#> DRR006448 1 0.6026 0.4673 0.672 0.108 0.044 0.172 0.004
#> DRR006449 1 0.1121 0.5743 0.956 0.000 0.000 0.044 0.000
#> DRR006450 1 0.3402 0.5516 0.804 0.008 0.004 0.184 0.000
#> DRR006451 1 0.4182 0.4715 0.808 0.024 0.104 0.064 0.000
#> DRR006452 1 0.3282 0.5507 0.804 0.008 0.000 0.188 0.000
#> DRR006453 1 0.2789 0.5760 0.880 0.008 0.020 0.092 0.000
#> DRR006454 3 0.7003 0.1190 0.212 0.044 0.536 0.208 0.000
#> DRR006455 5 0.0162 0.9290 0.000 0.004 0.000 0.000 0.996
#> DRR006456 3 0.1121 0.7031 0.000 0.044 0.956 0.000 0.000
#> DRR006457 3 0.0794 0.7160 0.000 0.000 0.972 0.028 0.000
#> DRR006458 1 0.5849 0.0384 0.508 0.000 0.100 0.392 0.000
#> DRR006459 1 0.5843 0.0479 0.512 0.000 0.100 0.388 0.000
#> DRR006460 2 0.4573 0.8028 0.004 0.688 0.280 0.028 0.000
#> DRR006461 2 0.4437 0.7942 0.000 0.664 0.316 0.020 0.000
#> DRR006462 1 0.0000 0.5796 1.000 0.000 0.000 0.000 0.000
#> DRR006463 2 0.4558 0.7887 0.000 0.652 0.324 0.024 0.000
#> DRR006464 3 0.5943 0.5267 0.012 0.192 0.632 0.164 0.000
#> DRR006465 1 0.1205 0.5807 0.956 0.000 0.004 0.040 0.000
#> DRR006466 3 0.1836 0.7061 0.008 0.040 0.936 0.016 0.000
#> DRR006467 1 0.5717 0.1451 0.592 0.004 0.096 0.308 0.000
#> DRR006468 5 0.0162 0.9290 0.000 0.004 0.000 0.000 0.996
#> DRR006469 2 0.3967 0.7529 0.000 0.808 0.136 0.040 0.016
#> DRR006470 3 0.4696 0.6271 0.004 0.136 0.748 0.112 0.000
#> DRR006471 4 0.8084 0.5816 0.316 0.108 0.212 0.364 0.000
#> DRR006472 3 0.3849 0.6746 0.004 0.052 0.808 0.136 0.000
#> DRR006473 2 0.3902 0.7755 0.000 0.804 0.152 0.028 0.016
#> DRR006474 2 0.4624 0.8047 0.000 0.676 0.296 0.012 0.016
#> DRR006475 4 0.7506 0.6425 0.208 0.048 0.368 0.376 0.000
#> DRR006476 2 0.6249 0.7320 0.024 0.588 0.272 0.116 0.000
#> DRR006477 3 0.4052 0.5812 0.016 0.176 0.784 0.024 0.000
#> DRR006478 1 0.3925 0.5030 0.820 0.012 0.088 0.080 0.000
#> DRR006479 3 0.3904 0.6489 0.044 0.016 0.816 0.124 0.000
#> DRR006480 1 0.6458 -0.2243 0.424 0.000 0.180 0.396 0.000
#> DRR006481 3 0.3237 0.6943 0.000 0.048 0.848 0.104 0.000
#> DRR006482 1 0.5104 0.3386 0.704 0.008 0.200 0.088 0.000
#> DRR006483 1 0.7301 -0.3436 0.440 0.068 0.128 0.364 0.000
#> DRR006484 3 0.0609 0.7194 0.000 0.000 0.980 0.020 0.000
#> DRR006485 2 0.4558 0.7887 0.000 0.652 0.324 0.024 0.000
#> DRR006486 3 0.4180 0.6401 0.044 0.024 0.800 0.132 0.000
#> DRR006487 3 0.1121 0.7031 0.000 0.044 0.956 0.000 0.000
#> DRR006488 5 0.0162 0.9290 0.000 0.004 0.000 0.000 0.996
#> DRR006489 1 0.4446 0.3178 0.592 0.008 0.000 0.400 0.000
#> DRR006490 3 0.0963 0.7133 0.000 0.000 0.964 0.036 0.000
#> DRR006491 3 0.1251 0.7098 0.000 0.008 0.956 0.036 0.000
#> DRR006492 3 0.5150 0.0157 0.272 0.000 0.652 0.076 0.000
#> DRR006493 3 0.1121 0.7031 0.000 0.044 0.956 0.000 0.000
#> DRR006494 1 0.6711 -0.3326 0.396 0.004 0.204 0.396 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.4911 0.5765 0.000 0.548 0.384 0.068 0.000 0.000
#> DRR006375 1 0.0508 0.6294 0.984 0.000 0.012 0.000 0.000 0.004
#> DRR006376 1 0.5829 0.2860 0.600 0.000 0.096 0.244 0.000 0.060
#> DRR006377 6 0.7257 0.2766 0.228 0.040 0.172 0.068 0.000 0.492
#> DRR006378 2 0.1088 0.5769 0.000 0.960 0.016 0.000 0.000 0.024
#> DRR006379 1 0.6059 0.1830 0.552 0.000 0.100 0.288 0.000 0.060
#> DRR006380 2 0.5566 0.5653 0.000 0.528 0.120 0.344 0.000 0.008
#> DRR006381 1 0.2263 0.6309 0.884 0.000 0.016 0.000 0.000 0.100
#> DRR006382 2 0.4946 0.5740 0.000 0.528 0.404 0.068 0.000 0.000
#> DRR006383 3 0.6636 0.3226 0.000 0.240 0.440 0.040 0.000 0.280
#> DRR006384 2 0.5193 0.5727 0.000 0.552 0.104 0.344 0.000 0.000
#> DRR006385 1 0.3618 0.5808 0.768 0.000 0.000 0.040 0.000 0.192
#> DRR006386 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006387 1 0.2118 0.6120 0.888 0.000 0.000 0.104 0.000 0.008
#> DRR006388 1 0.7486 0.1297 0.348 0.004 0.324 0.140 0.000 0.184
#> DRR006389 1 0.7486 0.1297 0.348 0.004 0.324 0.140 0.000 0.184
#> DRR006390 2 0.3500 0.4444 0.000 0.816 0.052 0.120 0.000 0.012
#> DRR006391 2 0.3500 0.4444 0.000 0.816 0.052 0.120 0.000 0.012
#> DRR006392 1 0.0363 0.6291 0.988 0.000 0.012 0.000 0.000 0.000
#> DRR006393 1 0.1663 0.6216 0.912 0.000 0.000 0.088 0.000 0.000
#> DRR006394 2 0.3613 0.5399 0.084 0.832 0.032 0.008 0.000 0.044
#> DRR006395 1 0.6680 0.0109 0.508 0.000 0.100 0.252 0.000 0.140
#> DRR006396 1 0.2826 0.6321 0.856 0.000 0.000 0.092 0.000 0.052
#> DRR006397 1 0.7486 0.1272 0.348 0.004 0.324 0.140 0.000 0.184
#> DRR006398 1 0.7486 0.1272 0.348 0.004 0.324 0.140 0.000 0.184
#> DRR006399 4 0.3386 0.7970 0.188 0.000 0.016 0.788 0.000 0.008
#> DRR006400 4 0.3386 0.7970 0.188 0.000 0.016 0.788 0.000 0.008
#> DRR006401 2 0.5193 0.5727 0.000 0.552 0.104 0.344 0.000 0.000
#> DRR006402 2 0.5193 0.5727 0.000 0.552 0.104 0.344 0.000 0.000
#> DRR006403 4 0.5053 0.6182 0.368 0.000 0.056 0.564 0.000 0.012
#> DRR006404 4 0.6178 0.4685 0.356 0.000 0.104 0.488 0.000 0.052
#> DRR006405 1 0.5591 0.3263 0.632 0.000 0.096 0.220 0.000 0.052
#> DRR006406 1 0.5591 0.3263 0.632 0.000 0.096 0.220 0.000 0.052
#> DRR006407 6 0.8463 -0.1069 0.192 0.056 0.224 0.256 0.000 0.272
#> DRR006408 2 0.5631 0.5616 0.000 0.520 0.128 0.344 0.000 0.008
#> DRR006409 1 0.5907 -0.1731 0.560 0.000 0.212 0.208 0.000 0.020
#> DRR006410 1 0.2118 0.6117 0.888 0.000 0.000 0.104 0.000 0.008
#> DRR006411 6 0.4273 0.3628 0.008 0.012 0.176 0.052 0.000 0.752
#> DRR006412 2 0.3500 0.4444 0.000 0.816 0.052 0.120 0.000 0.012
#> DRR006413 1 0.3592 0.3735 0.656 0.000 0.000 0.000 0.000 0.344
#> DRR006414 3 0.4086 0.7903 0.000 0.000 0.528 0.008 0.000 0.464
#> DRR006415 3 0.3854 0.7955 0.000 0.000 0.536 0.000 0.000 0.464
#> DRR006416 6 0.6864 0.0146 0.308 0.004 0.180 0.064 0.000 0.444
#> DRR006417 6 0.0508 0.3743 0.000 0.004 0.012 0.000 0.000 0.984
#> DRR006418 6 0.0405 0.3837 0.000 0.004 0.000 0.008 0.000 0.988
#> DRR006419 6 0.0777 0.3683 0.000 0.004 0.024 0.000 0.000 0.972
#> DRR006420 6 0.4917 0.2348 0.228 0.000 0.112 0.004 0.000 0.656
#> DRR006421 6 0.4297 -0.6556 0.012 0.000 0.452 0.004 0.000 0.532
#> DRR006422 2 0.7951 0.2541 0.248 0.396 0.128 0.044 0.000 0.184
#> DRR006423 2 0.0820 0.5804 0.000 0.972 0.016 0.000 0.000 0.012
#> DRR006424 1 0.1814 0.6333 0.900 0.000 0.000 0.000 0.000 0.100
#> DRR006425 2 0.5413 0.4940 0.208 0.652 0.112 0.016 0.000 0.012
#> DRR006426 6 0.0146 0.3800 0.000 0.004 0.000 0.000 0.000 0.996
#> DRR006427 5 0.2946 0.7334 0.000 0.176 0.000 0.012 0.812 0.000
#> DRR006428 3 0.4318 0.7838 0.000 0.000 0.532 0.020 0.000 0.448
#> DRR006429 2 0.7251 0.3520 0.212 0.488 0.136 0.016 0.000 0.148
#> DRR006430 1 0.0363 0.6291 0.988 0.000 0.012 0.000 0.000 0.000
#> DRR006431 1 0.1065 0.6266 0.964 0.000 0.020 0.008 0.000 0.008
#> DRR006432 6 0.0405 0.3837 0.000 0.004 0.000 0.008 0.000 0.988
#> DRR006433 6 0.5350 -0.6546 0.000 0.000 0.416 0.108 0.000 0.476
#> DRR006434 2 0.4933 0.5748 0.000 0.536 0.396 0.068 0.000 0.000
#> DRR006435 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006436 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006437 1 0.2448 0.6451 0.884 0.000 0.000 0.052 0.000 0.064
#> DRR006438 6 0.5035 -0.5082 0.068 0.000 0.380 0.004 0.000 0.548
#> DRR006439 3 0.4536 0.7337 0.024 0.000 0.496 0.004 0.000 0.476
#> DRR006440 2 0.5200 0.5640 0.000 0.500 0.424 0.068 0.000 0.008
#> DRR006441 2 0.5251 0.4951 0.204 0.668 0.100 0.008 0.000 0.020
#> DRR006442 3 0.3971 0.7927 0.000 0.000 0.548 0.004 0.000 0.448
#> DRR006443 2 0.5097 0.5667 0.000 0.508 0.420 0.068 0.000 0.004
#> DRR006444 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006445 1 0.3703 0.6238 0.796 0.000 0.004 0.092 0.000 0.108
#> DRR006446 2 0.2462 0.5122 0.000 0.892 0.032 0.064 0.000 0.012
#> DRR006447 6 0.5898 0.0661 0.192 0.008 0.104 0.064 0.000 0.632
#> DRR006448 4 0.3678 0.7819 0.228 0.000 0.016 0.748 0.000 0.008
#> DRR006449 1 0.1265 0.6323 0.948 0.000 0.000 0.044 0.000 0.008
#> DRR006450 1 0.1814 0.6333 0.900 0.000 0.000 0.000 0.000 0.100
#> DRR006451 1 0.5591 0.3255 0.632 0.000 0.096 0.220 0.000 0.052
#> DRR006452 1 0.1910 0.6342 0.892 0.000 0.000 0.000 0.000 0.108
#> DRR006453 1 0.3807 0.6226 0.800 0.000 0.016 0.080 0.000 0.104
#> DRR006454 1 0.6700 0.0366 0.384 0.000 0.184 0.052 0.000 0.380
#> DRR006455 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006456 3 0.5121 0.7073 0.000 0.004 0.464 0.068 0.000 0.464
#> DRR006457 3 0.4181 0.7786 0.000 0.000 0.512 0.012 0.000 0.476
#> DRR006458 1 0.1230 0.6231 0.956 0.000 0.028 0.008 0.000 0.008
#> DRR006459 1 0.1340 0.6237 0.948 0.000 0.040 0.004 0.000 0.008
#> DRR006460 2 0.5193 0.5727 0.000 0.552 0.104 0.344 0.000 0.000
#> DRR006461 2 0.4946 0.5740 0.000 0.528 0.404 0.068 0.000 0.000
#> DRR006462 1 0.1970 0.6172 0.900 0.000 0.000 0.092 0.000 0.008
#> DRR006463 2 0.4967 0.5671 0.000 0.512 0.420 0.068 0.000 0.000
#> DRR006464 6 0.2619 0.3647 0.004 0.072 0.032 0.008 0.000 0.884
#> DRR006465 1 0.1204 0.6309 0.944 0.000 0.000 0.056 0.000 0.000
#> DRR006466 6 0.5908 -0.5865 0.000 0.056 0.416 0.064 0.000 0.464
#> DRR006467 1 0.2170 0.6309 0.888 0.000 0.012 0.000 0.000 0.100
#> DRR006468 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006469 2 0.1168 0.5756 0.000 0.956 0.016 0.000 0.000 0.028
#> DRR006470 6 0.0405 0.3765 0.000 0.004 0.008 0.000 0.000 0.988
#> DRR006471 6 0.4521 -0.0909 0.400 0.004 0.028 0.000 0.000 0.568
#> DRR006472 6 0.5279 -0.3684 0.096 0.000 0.356 0.004 0.000 0.544
#> DRR006473 2 0.0820 0.5804 0.000 0.972 0.016 0.000 0.000 0.012
#> DRR006474 2 0.4985 0.5821 0.000 0.548 0.376 0.076 0.000 0.000
#> DRR006475 1 0.5353 0.1730 0.504 0.000 0.096 0.004 0.000 0.396
#> DRR006476 2 0.5855 0.5800 0.000 0.500 0.364 0.112 0.000 0.024
#> DRR006477 6 0.7255 -0.0854 0.000 0.156 0.140 0.344 0.000 0.360
#> DRR006478 1 0.2066 0.6346 0.908 0.000 0.000 0.052 0.000 0.040
#> DRR006479 6 0.5366 -0.4924 0.100 0.000 0.380 0.004 0.000 0.516
#> DRR006480 1 0.2773 0.4768 0.836 0.000 0.152 0.004 0.000 0.008
#> DRR006481 6 0.4319 -0.5365 0.024 0.000 0.400 0.000 0.000 0.576
#> DRR006482 1 0.4866 0.4730 0.692 0.000 0.020 0.092 0.000 0.196
#> DRR006483 1 0.4254 0.4345 0.656 0.004 0.028 0.000 0.000 0.312
#> DRR006484 6 0.4098 -0.7419 0.008 0.000 0.496 0.000 0.000 0.496
#> DRR006485 2 0.5097 0.5667 0.000 0.508 0.420 0.068 0.000 0.004
#> DRR006486 6 0.4559 0.2054 0.128 0.000 0.156 0.004 0.000 0.712
#> DRR006487 3 0.4089 0.7897 0.000 0.000 0.524 0.008 0.000 0.468
#> DRR006488 5 0.0000 0.9646 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006489 1 0.2170 0.6309 0.888 0.000 0.012 0.000 0.000 0.100
#> DRR006490 3 0.3971 0.7927 0.000 0.000 0.548 0.004 0.000 0.448
#> DRR006491 3 0.3971 0.7927 0.000 0.000 0.548 0.004 0.000 0.448
#> DRR006492 3 0.6017 0.0908 0.352 0.000 0.436 0.004 0.000 0.208
#> DRR006493 3 0.3854 0.7955 0.000 0.000 0.536 0.000 0.000 0.464
#> DRR006494 1 0.3341 0.4050 0.776 0.000 0.208 0.004 0.000 0.012
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16187 rows and 121 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 0.868 0.941 0.973 0.4706 0.529 0.529
#> 3 3 0.476 0.581 0.809 0.3239 0.742 0.564
#> 4 4 0.575 0.595 0.752 0.1774 0.796 0.521
#> 5 5 0.794 0.807 0.894 0.0657 0.877 0.585
#> 6 6 0.765 0.664 0.830 0.0505 0.874 0.517
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
#> DRR006374 2 0.0000 0.961 0.000 1.000
#> DRR006375 1 0.0000 0.977 1.000 0.000
#> DRR006376 1 0.0000 0.977 1.000 0.000
#> DRR006377 1 0.0000 0.977 1.000 0.000
#> DRR006378 1 0.0000 0.977 1.000 0.000
#> DRR006379 1 0.0000 0.977 1.000 0.000
#> DRR006380 1 0.0000 0.977 1.000 0.000
#> DRR006381 1 0.0000 0.977 1.000 0.000
#> DRR006382 2 0.0000 0.961 0.000 1.000
#> DRR006383 2 0.0000 0.961 0.000 1.000
#> DRR006384 1 0.6973 0.775 0.812 0.188
#> DRR006385 1 0.0000 0.977 1.000 0.000
#> DRR006386 1 0.4815 0.879 0.896 0.104
#> DRR006387 1 0.0000 0.977 1.000 0.000
#> DRR006388 1 0.0000 0.977 1.000 0.000
#> DRR006389 1 0.0000 0.977 1.000 0.000
#> DRR006390 1 0.0000 0.977 1.000 0.000
#> DRR006391 1 0.0000 0.977 1.000 0.000
#> DRR006392 1 0.0000 0.977 1.000 0.000
#> DRR006393 1 0.0000 0.977 1.000 0.000
#> DRR006394 1 0.0000 0.977 1.000 0.000
#> DRR006395 1 0.0000 0.977 1.000 0.000
#> DRR006396 1 0.0000 0.977 1.000 0.000
#> DRR006397 1 0.0000 0.977 1.000 0.000
#> DRR006398 1 0.0000 0.977 1.000 0.000
#> DRR006399 1 0.0000 0.977 1.000 0.000
#> DRR006400 1 0.0000 0.977 1.000 0.000
#> DRR006401 1 0.0000 0.977 1.000 0.000
#> DRR006402 1 0.0000 0.977 1.000 0.000
#> DRR006403 1 0.0000 0.977 1.000 0.000
#> DRR006404 1 0.0000 0.977 1.000 0.000
#> DRR006405 1 0.0000 0.977 1.000 0.000
#> DRR006406 1 0.0000 0.977 1.000 0.000
#> DRR006407 1 0.0000 0.977 1.000 0.000
#> DRR006408 1 0.0000 0.977 1.000 0.000
#> DRR006409 1 0.0000 0.977 1.000 0.000
#> DRR006410 1 0.0000 0.977 1.000 0.000
#> DRR006411 1 0.0000 0.977 1.000 0.000
#> DRR006412 1 0.0672 0.971 0.992 0.008
#> DRR006413 1 0.0000 0.977 1.000 0.000
#> DRR006414 2 0.4161 0.898 0.084 0.916
#> DRR006415 2 0.0000 0.961 0.000 1.000
#> DRR006416 1 0.0000 0.977 1.000 0.000
#> DRR006417 2 0.0000 0.961 0.000 1.000
#> DRR006418 1 0.4939 0.870 0.892 0.108
#> DRR006419 2 0.1414 0.949 0.020 0.980
#> DRR006420 2 0.8327 0.676 0.264 0.736
#> DRR006421 2 0.0000 0.961 0.000 1.000
#> DRR006422 1 0.0000 0.977 1.000 0.000
#> DRR006423 2 0.9044 0.546 0.320 0.680
#> DRR006424 1 0.0000 0.977 1.000 0.000
#> DRR006425 1 0.0000 0.977 1.000 0.000
#> DRR006426 2 0.1184 0.952 0.016 0.984
#> DRR006427 2 0.0000 0.961 0.000 1.000
#> DRR006428 2 0.0000 0.961 0.000 1.000
#> DRR006429 1 0.7745 0.703 0.772 0.228
#> DRR006430 1 0.0000 0.977 1.000 0.000
#> DRR006431 1 0.0000 0.977 1.000 0.000
#> DRR006432 2 0.3274 0.919 0.060 0.940
#> DRR006433 2 0.1184 0.951 0.016 0.984
#> DRR006434 2 0.0000 0.961 0.000 1.000
#> DRR006435 2 0.0000 0.961 0.000 1.000
#> DRR006436 2 0.0000 0.961 0.000 1.000
#> DRR006437 1 0.0000 0.977 1.000 0.000
#> DRR006438 2 0.1843 0.943 0.028 0.972
#> DRR006439 2 0.6148 0.830 0.152 0.848
#> DRR006440 2 0.0000 0.961 0.000 1.000
#> DRR006441 1 0.0000 0.977 1.000 0.000
#> DRR006442 2 0.0000 0.961 0.000 1.000
#> DRR006443 2 0.0000 0.961 0.000 1.000
#> DRR006444 2 0.0000 0.961 0.000 1.000
#> DRR006445 1 0.0000 0.977 1.000 0.000
#> DRR006446 1 0.0000 0.977 1.000 0.000
#> DRR006447 1 0.0000 0.977 1.000 0.000
#> DRR006448 1 0.0000 0.977 1.000 0.000
#> DRR006449 1 0.0000 0.977 1.000 0.000
#> DRR006450 1 0.0000 0.977 1.000 0.000
#> DRR006451 1 0.0000 0.977 1.000 0.000
#> DRR006452 1 0.0000 0.977 1.000 0.000
#> DRR006453 1 0.0000 0.977 1.000 0.000
#> DRR006454 1 0.0000 0.977 1.000 0.000
#> DRR006455 1 0.9044 0.548 0.680 0.320
#> DRR006456 2 0.0000 0.961 0.000 1.000
#> DRR006457 2 0.0000 0.961 0.000 1.000
#> DRR006458 1 0.0000 0.977 1.000 0.000
#> DRR006459 1 0.0000 0.977 1.000 0.000
#> DRR006460 1 0.3114 0.928 0.944 0.056
#> DRR006461 2 0.0000 0.961 0.000 1.000
#> DRR006462 1 0.0000 0.977 1.000 0.000
#> DRR006463 2 0.0000 0.961 0.000 1.000
#> DRR006464 2 0.9248 0.531 0.340 0.660
#> DRR006465 1 0.0000 0.977 1.000 0.000
#> DRR006466 2 0.0000 0.961 0.000 1.000
#> DRR006467 1 0.0000 0.977 1.000 0.000
#> DRR006468 2 0.0000 0.961 0.000 1.000
#> DRR006469 1 0.0672 0.971 0.992 0.008
#> DRR006470 2 0.0000 0.961 0.000 1.000
#> DRR006471 1 0.1633 0.957 0.976 0.024
#> DRR006472 2 0.0000 0.961 0.000 1.000
#> DRR006473 2 0.0000 0.961 0.000 1.000
#> DRR006474 2 0.0000 0.961 0.000 1.000
#> DRR006475 1 0.7056 0.757 0.808 0.192
#> DRR006476 1 0.5408 0.856 0.876 0.124
#> DRR006477 1 0.8327 0.660 0.736 0.264
#> DRR006478 1 0.0000 0.977 1.000 0.000
#> DRR006479 2 0.7056 0.782 0.192 0.808
#> DRR006480 1 0.0000 0.977 1.000 0.000
#> DRR006481 2 0.0000 0.961 0.000 1.000
#> DRR006482 1 0.0000 0.977 1.000 0.000
#> DRR006483 1 0.0000 0.977 1.000 0.000
#> DRR006484 2 0.0000 0.961 0.000 1.000
#> DRR006485 2 0.0000 0.961 0.000 1.000
#> DRR006486 2 0.7056 0.781 0.192 0.808
#> DRR006487 2 0.0000 0.961 0.000 1.000
#> DRR006488 2 0.0000 0.961 0.000 1.000
#> DRR006489 1 0.0000 0.977 1.000 0.000
#> DRR006490 2 0.0000 0.961 0.000 1.000
#> DRR006491 2 0.0000 0.961 0.000 1.000
#> DRR006492 1 0.0000 0.977 1.000 0.000
#> DRR006493 2 0.0000 0.961 0.000 1.000
#> DRR006494 1 0.0376 0.974 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 3 0.5760 0.5750 0.000 0.328 0.672
#> DRR006375 1 0.1529 0.7629 0.960 0.040 0.000
#> DRR006376 1 0.6299 -0.0721 0.524 0.476 0.000
#> DRR006377 2 0.6026 0.3229 0.376 0.624 0.000
#> DRR006378 1 0.5254 0.5752 0.736 0.264 0.000
#> DRR006379 2 0.6260 0.2701 0.448 0.552 0.000
#> DRR006380 2 0.3896 0.6584 0.128 0.864 0.008
#> DRR006381 1 0.2187 0.7593 0.948 0.024 0.028
#> DRR006382 3 0.1015 0.8149 0.008 0.012 0.980
#> DRR006383 3 0.2356 0.7860 0.072 0.000 0.928
#> DRR006384 2 0.3359 0.6562 0.084 0.900 0.016
#> DRR006385 1 0.0592 0.7655 0.988 0.012 0.000
#> DRR006386 2 0.2356 0.6542 0.072 0.928 0.000
#> DRR006387 1 0.6168 0.1941 0.588 0.412 0.000
#> DRR006388 1 0.1289 0.7647 0.968 0.032 0.000
#> DRR006389 1 0.1289 0.7647 0.968 0.032 0.000
#> DRR006390 1 0.6809 0.1861 0.524 0.464 0.012
#> DRR006391 1 0.6809 0.1861 0.524 0.464 0.012
#> DRR006392 1 0.1163 0.7654 0.972 0.028 0.000
#> DRR006393 1 0.2165 0.7563 0.936 0.064 0.000
#> DRR006394 1 0.6062 0.4142 0.616 0.384 0.000
#> DRR006395 2 0.6026 0.4682 0.376 0.624 0.000
#> DRR006396 1 0.3038 0.7335 0.896 0.104 0.000
#> DRR006397 1 0.1860 0.7606 0.948 0.052 0.000
#> DRR006398 1 0.1411 0.7644 0.964 0.036 0.000
#> DRR006399 2 0.6045 0.4596 0.380 0.620 0.000
#> DRR006400 2 0.6008 0.4756 0.372 0.628 0.000
#> DRR006401 2 0.3619 0.6566 0.136 0.864 0.000
#> DRR006402 2 0.3482 0.6576 0.128 0.872 0.000
#> DRR006403 2 0.5905 0.5072 0.352 0.648 0.000
#> DRR006404 2 0.5968 0.4891 0.364 0.636 0.000
#> DRR006405 1 0.5591 0.4562 0.696 0.304 0.000
#> DRR006406 1 0.6062 0.2639 0.616 0.384 0.000
#> DRR006407 2 0.5363 0.5482 0.276 0.724 0.000
#> DRR006408 2 0.5431 0.5806 0.284 0.716 0.000
#> DRR006409 1 0.6931 0.0127 0.528 0.456 0.016
#> DRR006410 1 0.6140 0.2186 0.596 0.404 0.000
#> DRR006411 1 0.2261 0.7577 0.932 0.068 0.000
#> DRR006412 1 0.4702 0.6682 0.788 0.212 0.000
#> DRR006413 1 0.1950 0.7534 0.952 0.008 0.040
#> DRR006414 3 0.4555 0.6660 0.200 0.000 0.800
#> DRR006415 3 0.1289 0.8083 0.032 0.000 0.968
#> DRR006416 1 0.0237 0.7647 0.996 0.004 0.000
#> DRR006417 3 0.4784 0.6586 0.200 0.004 0.796
#> DRR006418 1 0.2280 0.7439 0.940 0.008 0.052
#> DRR006419 1 0.6235 0.1392 0.564 0.000 0.436
#> DRR006420 1 0.5859 0.3884 0.656 0.000 0.344
#> DRR006421 3 0.1031 0.8132 0.024 0.000 0.976
#> DRR006422 1 0.1964 0.7533 0.944 0.056 0.000
#> DRR006423 1 0.8518 0.3488 0.592 0.272 0.136
#> DRR006424 1 0.0424 0.7651 0.992 0.008 0.000
#> DRR006425 1 0.5882 0.3591 0.652 0.348 0.000
#> DRR006426 1 0.8310 0.0415 0.500 0.080 0.420
#> DRR006427 3 0.5882 0.5681 0.000 0.348 0.652
#> DRR006428 3 0.0424 0.8156 0.008 0.000 0.992
#> DRR006429 1 0.5377 0.6818 0.820 0.112 0.068
#> DRR006430 1 0.1031 0.7659 0.976 0.024 0.000
#> DRR006431 1 0.2261 0.7552 0.932 0.068 0.000
#> DRR006432 1 0.8765 0.0714 0.504 0.116 0.380
#> DRR006433 3 0.5363 0.6229 0.000 0.276 0.724
#> DRR006434 3 0.2711 0.7985 0.000 0.088 0.912
#> DRR006435 2 0.6299 -0.3011 0.000 0.524 0.476
#> DRR006436 3 0.6307 0.3279 0.000 0.488 0.512
#> DRR006437 1 0.1860 0.7605 0.948 0.052 0.000
#> DRR006438 3 0.4974 0.6155 0.236 0.000 0.764
#> DRR006439 3 0.1163 0.8132 0.028 0.000 0.972
#> DRR006440 3 0.3116 0.7857 0.000 0.108 0.892
#> DRR006441 1 0.6577 0.2854 0.572 0.420 0.008
#> DRR006442 3 0.0237 0.8148 0.004 0.000 0.996
#> DRR006443 3 0.2066 0.8076 0.000 0.060 0.940
#> DRR006444 2 0.6307 -0.3261 0.000 0.512 0.488
#> DRR006445 1 0.1031 0.7662 0.976 0.024 0.000
#> DRR006446 1 0.3879 0.7153 0.848 0.152 0.000
#> DRR006447 1 0.0237 0.7647 0.996 0.004 0.000
#> DRR006448 1 0.6299 -0.0415 0.524 0.476 0.000
#> DRR006449 1 0.3038 0.7352 0.896 0.104 0.000
#> DRR006450 1 0.0237 0.7652 0.996 0.004 0.000
#> DRR006451 1 0.6180 0.1716 0.584 0.416 0.000
#> DRR006452 1 0.0424 0.7651 0.992 0.008 0.000
#> DRR006453 1 0.0000 0.7649 1.000 0.000 0.000
#> DRR006454 1 0.2625 0.7468 0.916 0.084 0.000
#> DRR006455 2 0.2261 0.5309 0.000 0.932 0.068
#> DRR006456 3 0.0237 0.8148 0.000 0.004 0.996
#> DRR006457 3 0.1031 0.8142 0.000 0.024 0.976
#> DRR006458 1 0.1905 0.7598 0.956 0.016 0.028
#> DRR006459 1 0.2063 0.7659 0.948 0.044 0.008
#> DRR006460 2 0.2165 0.6530 0.064 0.936 0.000
#> DRR006461 3 0.3619 0.7656 0.000 0.136 0.864
#> DRR006462 1 0.3816 0.6976 0.852 0.148 0.000
#> DRR006463 3 0.2066 0.8078 0.000 0.060 0.940
#> DRR006464 1 0.5377 0.6544 0.820 0.112 0.068
#> DRR006465 1 0.1753 0.7613 0.952 0.048 0.000
#> DRR006466 3 0.2625 0.8002 0.000 0.084 0.916
#> DRR006467 1 0.0592 0.7630 0.988 0.000 0.012
#> DRR006468 2 0.6299 -0.3011 0.000 0.524 0.476
#> DRR006469 1 0.5058 0.6752 0.820 0.148 0.032
#> DRR006470 3 0.7059 0.1362 0.460 0.020 0.520
#> DRR006471 1 0.2200 0.7429 0.940 0.004 0.056
#> DRR006472 3 0.1525 0.8141 0.004 0.032 0.964
#> DRR006473 3 0.8976 0.1856 0.416 0.128 0.456
#> DRR006474 3 0.5926 0.5165 0.000 0.356 0.644
#> DRR006475 1 0.4178 0.6360 0.828 0.000 0.172
#> DRR006476 2 0.7228 0.6067 0.188 0.708 0.104
#> DRR006477 2 0.7979 0.4463 0.100 0.628 0.272
#> DRR006478 1 0.1411 0.7642 0.964 0.036 0.000
#> DRR006479 3 0.6168 0.3110 0.412 0.000 0.588
#> DRR006480 1 0.2383 0.7514 0.940 0.016 0.044
#> DRR006481 3 0.2774 0.8055 0.008 0.072 0.920
#> DRR006482 1 0.3482 0.7061 0.872 0.128 0.000
#> DRR006483 1 0.1525 0.7554 0.964 0.004 0.032
#> DRR006484 3 0.0829 0.8161 0.012 0.004 0.984
#> DRR006485 3 0.2261 0.8054 0.000 0.068 0.932
#> DRR006486 1 0.6062 0.2957 0.616 0.000 0.384
#> DRR006487 3 0.0592 0.8138 0.012 0.000 0.988
#> DRR006488 3 0.6235 0.4318 0.000 0.436 0.564
#> DRR006489 1 0.0592 0.7630 0.988 0.000 0.012
#> DRR006490 3 0.1643 0.8045 0.044 0.000 0.956
#> DRR006491 3 0.1411 0.8075 0.036 0.000 0.964
#> DRR006492 1 0.7143 0.2098 0.576 0.396 0.028
#> DRR006493 3 0.0000 0.8146 0.000 0.000 1.000
#> DRR006494 1 0.2280 0.7481 0.940 0.008 0.052
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 3 0.3636 0.7536 0.000 0.172 0.820 0.008
#> DRR006375 1 0.5088 0.0956 0.572 0.004 0.000 0.424
#> DRR006376 4 0.6212 0.4544 0.060 0.380 0.000 0.560
#> DRR006377 4 0.3725 0.5874 0.000 0.180 0.008 0.812
#> DRR006378 4 0.1867 0.7564 0.072 0.000 0.000 0.928
#> DRR006379 2 0.6443 0.4098 0.400 0.528 0.000 0.072
#> DRR006380 2 0.4830 0.4926 0.392 0.608 0.000 0.000
#> DRR006381 1 0.0524 0.6822 0.988 0.000 0.008 0.004
#> DRR006382 3 0.1557 0.8722 0.056 0.000 0.944 0.000
#> DRR006383 3 0.4356 0.5938 0.292 0.000 0.708 0.000
#> DRR006384 2 0.2011 0.5745 0.080 0.920 0.000 0.000
#> DRR006385 1 0.2760 0.6707 0.872 0.000 0.000 0.128
#> DRR006386 2 0.2412 0.5420 0.008 0.908 0.000 0.084
#> DRR006387 1 0.4800 0.1446 0.656 0.340 0.000 0.004
#> DRR006388 4 0.4250 0.6420 0.276 0.000 0.000 0.724
#> DRR006389 4 0.4277 0.6378 0.280 0.000 0.000 0.720
#> DRR006390 4 0.2345 0.6651 0.000 0.100 0.000 0.900
#> DRR006391 4 0.2408 0.6614 0.000 0.104 0.000 0.896
#> DRR006392 1 0.0817 0.6910 0.976 0.000 0.000 0.024
#> DRR006393 1 0.0524 0.6848 0.988 0.004 0.000 0.008
#> DRR006394 4 0.1389 0.7144 0.000 0.048 0.000 0.952
#> DRR006395 2 0.5039 0.4757 0.404 0.592 0.000 0.004
#> DRR006396 1 0.1174 0.6793 0.968 0.020 0.000 0.012
#> DRR006397 4 0.3942 0.6782 0.236 0.000 0.000 0.764
#> DRR006398 4 0.3942 0.6782 0.236 0.000 0.000 0.764
#> DRR006399 2 0.4961 0.4093 0.448 0.552 0.000 0.000
#> DRR006400 2 0.4955 0.4169 0.444 0.556 0.000 0.000
#> DRR006401 2 0.4103 0.5598 0.256 0.744 0.000 0.000
#> DRR006402 2 0.4072 0.5609 0.252 0.748 0.000 0.000
#> DRR006403 2 0.4888 0.4691 0.412 0.588 0.000 0.000
#> DRR006404 2 0.5204 0.4984 0.376 0.612 0.000 0.012
#> DRR006405 4 0.6251 0.6347 0.140 0.196 0.000 0.664
#> DRR006406 4 0.6674 0.5435 0.116 0.300 0.000 0.584
#> DRR006407 4 0.5070 0.2284 0.004 0.416 0.000 0.580
#> DRR006408 2 0.4830 0.4921 0.392 0.608 0.000 0.000
#> DRR006409 1 0.4761 0.0289 0.628 0.372 0.000 0.000
#> DRR006410 1 0.4936 0.1397 0.652 0.340 0.000 0.008
#> DRR006411 4 0.2408 0.7499 0.104 0.000 0.000 0.896
#> DRR006412 4 0.0469 0.7448 0.012 0.000 0.000 0.988
#> DRR006413 1 0.3032 0.6703 0.868 0.000 0.008 0.124
#> DRR006414 3 0.4543 0.5337 0.324 0.000 0.676 0.000
#> DRR006415 3 0.1637 0.8700 0.060 0.000 0.940 0.000
#> DRR006416 4 0.4985 0.2682 0.468 0.000 0.000 0.532
#> DRR006417 3 0.2021 0.8755 0.024 0.000 0.936 0.040
#> DRR006418 4 0.3975 0.6755 0.240 0.000 0.000 0.760
#> DRR006419 1 0.7806 0.0913 0.392 0.000 0.356 0.252
#> DRR006420 1 0.6752 0.0845 0.468 0.000 0.440 0.092
#> DRR006421 3 0.0376 0.8881 0.004 0.000 0.992 0.004
#> DRR006422 1 0.0188 0.6850 0.996 0.000 0.000 0.004
#> DRR006423 4 0.1767 0.7107 0.000 0.044 0.012 0.944
#> DRR006424 1 0.3266 0.6301 0.832 0.000 0.000 0.168
#> DRR006425 4 0.6616 0.6143 0.156 0.220 0.000 0.624
#> DRR006426 4 0.1938 0.7555 0.052 0.000 0.012 0.936
#> DRR006427 2 0.7679 0.0843 0.000 0.424 0.356 0.220
#> DRR006428 3 0.0592 0.8872 0.016 0.000 0.984 0.000
#> DRR006429 4 0.1970 0.7570 0.060 0.000 0.008 0.932
#> DRR006430 1 0.0921 0.6919 0.972 0.000 0.000 0.028
#> DRR006431 1 0.1474 0.6442 0.948 0.052 0.000 0.000
#> DRR006432 4 0.1545 0.7535 0.040 0.000 0.008 0.952
#> DRR006433 3 0.4868 0.5251 0.000 0.304 0.684 0.012
#> DRR006434 3 0.2197 0.8653 0.000 0.048 0.928 0.024
#> DRR006435 2 0.7088 0.3665 0.000 0.568 0.204 0.228
#> DRR006436 2 0.7173 0.3530 0.000 0.556 0.216 0.228
#> DRR006437 1 0.0592 0.6883 0.984 0.000 0.000 0.016
#> DRR006438 3 0.1109 0.8870 0.028 0.000 0.968 0.004
#> DRR006439 3 0.0921 0.8853 0.028 0.000 0.972 0.000
#> DRR006440 3 0.2363 0.8595 0.000 0.024 0.920 0.056
#> DRR006441 4 0.3311 0.6043 0.000 0.172 0.000 0.828
#> DRR006442 3 0.0921 0.8851 0.028 0.000 0.972 0.000
#> DRR006443 3 0.1406 0.8808 0.000 0.016 0.960 0.024
#> DRR006444 2 0.7120 0.3588 0.000 0.564 0.212 0.224
#> DRR006445 1 0.4277 0.4753 0.720 0.000 0.000 0.280
#> DRR006446 4 0.2081 0.7547 0.084 0.000 0.000 0.916
#> DRR006447 4 0.4477 0.5957 0.312 0.000 0.000 0.688
#> DRR006448 1 0.5193 -0.0985 0.580 0.412 0.000 0.008
#> DRR006449 1 0.1902 0.6417 0.932 0.064 0.000 0.004
#> DRR006450 1 0.4040 0.5307 0.752 0.000 0.000 0.248
#> DRR006451 4 0.7746 0.2696 0.272 0.288 0.000 0.440
#> DRR006452 1 0.2216 0.6921 0.908 0.000 0.000 0.092
#> DRR006453 1 0.4746 0.2484 0.632 0.000 0.000 0.368
#> DRR006454 1 0.5349 0.3572 0.640 0.024 0.000 0.336
#> DRR006455 2 0.3806 0.4944 0.000 0.824 0.020 0.156
#> DRR006456 3 0.0188 0.8873 0.004 0.000 0.996 0.000
#> DRR006457 3 0.0336 0.8863 0.000 0.000 0.992 0.008
#> DRR006458 1 0.0712 0.6798 0.984 0.004 0.008 0.004
#> DRR006459 1 0.1909 0.6935 0.940 0.008 0.004 0.048
#> DRR006460 2 0.2921 0.5762 0.140 0.860 0.000 0.000
#> DRR006461 3 0.1042 0.8838 0.000 0.020 0.972 0.008
#> DRR006462 1 0.4880 0.4799 0.760 0.188 0.000 0.052
#> DRR006463 3 0.1284 0.8819 0.000 0.012 0.964 0.024
#> DRR006464 4 0.1970 0.7569 0.060 0.000 0.008 0.932
#> DRR006465 1 0.4088 0.5590 0.764 0.004 0.000 0.232
#> DRR006466 3 0.2060 0.8675 0.000 0.052 0.932 0.016
#> DRR006467 1 0.4040 0.5224 0.752 0.000 0.000 0.248
#> DRR006468 2 0.7088 0.3665 0.000 0.568 0.204 0.228
#> DRR006469 4 0.0564 0.7362 0.004 0.004 0.004 0.988
#> DRR006470 4 0.3793 0.7379 0.112 0.000 0.044 0.844
#> DRR006471 4 0.4948 0.3461 0.440 0.000 0.000 0.560
#> DRR006472 3 0.0921 0.8841 0.000 0.000 0.972 0.028
#> DRR006473 4 0.1174 0.7458 0.020 0.000 0.012 0.968
#> DRR006474 3 0.3400 0.7512 0.000 0.180 0.820 0.000
#> DRR006475 1 0.6867 0.2162 0.508 0.000 0.384 0.108
#> DRR006476 2 0.6058 0.4855 0.032 0.704 0.212 0.052
#> DRR006477 2 0.6139 0.4710 0.404 0.544 0.052 0.000
#> DRR006478 4 0.4304 0.6335 0.284 0.000 0.000 0.716
#> DRR006479 3 0.3444 0.7502 0.184 0.000 0.816 0.000
#> DRR006480 1 0.1743 0.6664 0.940 0.000 0.056 0.004
#> DRR006481 3 0.1398 0.8789 0.000 0.004 0.956 0.040
#> DRR006482 1 0.1356 0.6562 0.960 0.032 0.008 0.000
#> DRR006483 4 0.4746 0.5042 0.368 0.000 0.000 0.632
#> DRR006484 3 0.0188 0.8867 0.000 0.000 0.996 0.004
#> DRR006485 3 0.1584 0.8778 0.000 0.012 0.952 0.036
#> DRR006486 3 0.5137 0.1717 0.452 0.000 0.544 0.004
#> DRR006487 3 0.0336 0.8872 0.008 0.000 0.992 0.000
#> DRR006488 2 0.7450 0.2779 0.000 0.508 0.264 0.228
#> DRR006489 1 0.4331 0.4578 0.712 0.000 0.000 0.288
#> DRR006490 3 0.1940 0.8584 0.076 0.000 0.924 0.000
#> DRR006491 3 0.2149 0.8483 0.088 0.000 0.912 0.000
#> DRR006492 1 0.5877 0.2373 0.656 0.276 0.068 0.000
#> DRR006493 3 0.0592 0.8872 0.016 0.000 0.984 0.000
#> DRR006494 1 0.4313 0.5164 0.736 0.000 0.260 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 3 0.0609 0.9451 0.000 0.000 0.980 0.000 0.020
#> DRR006375 2 0.5759 0.3940 0.356 0.560 0.000 0.076 0.008
#> DRR006376 4 0.3480 0.6076 0.000 0.248 0.000 0.752 0.000
#> DRR006377 2 0.3916 0.6573 0.000 0.732 0.000 0.256 0.012
#> DRR006378 2 0.0324 0.8620 0.000 0.992 0.000 0.004 0.004
#> DRR006379 4 0.0404 0.8181 0.000 0.012 0.000 0.988 0.000
#> DRR006380 4 0.3323 0.7716 0.036 0.000 0.004 0.844 0.116
#> DRR006381 1 0.1116 0.8728 0.964 0.004 0.000 0.004 0.028
#> DRR006382 3 0.2248 0.8721 0.088 0.000 0.900 0.000 0.012
#> DRR006383 1 0.2284 0.8357 0.896 0.000 0.096 0.004 0.004
#> DRR006384 4 0.3585 0.6928 0.004 0.000 0.004 0.772 0.220
#> DRR006385 1 0.1569 0.8671 0.944 0.004 0.000 0.008 0.044
#> DRR006386 5 0.0912 0.9115 0.012 0.000 0.000 0.016 0.972
#> DRR006387 4 0.4527 0.6308 0.260 0.000 0.000 0.700 0.040
#> DRR006388 2 0.2249 0.8315 0.096 0.896 0.000 0.000 0.008
#> DRR006389 2 0.2249 0.8315 0.096 0.896 0.000 0.000 0.008
#> DRR006390 2 0.0510 0.8584 0.000 0.984 0.000 0.000 0.016
#> DRR006391 2 0.0510 0.8584 0.000 0.984 0.000 0.000 0.016
#> DRR006392 1 0.1143 0.8748 0.968 0.008 0.008 0.012 0.004
#> DRR006393 1 0.1680 0.8736 0.948 0.012 0.008 0.024 0.008
#> DRR006394 2 0.0290 0.8607 0.000 0.992 0.000 0.000 0.008
#> DRR006395 4 0.0693 0.8174 0.000 0.012 0.000 0.980 0.008
#> DRR006396 1 0.2153 0.8593 0.916 0.000 0.000 0.044 0.040
#> DRR006397 2 0.1408 0.8570 0.044 0.948 0.000 0.000 0.008
#> DRR006398 2 0.1408 0.8570 0.044 0.948 0.000 0.000 0.008
#> DRR006399 4 0.0671 0.8205 0.004 0.000 0.000 0.980 0.016
#> DRR006400 4 0.0671 0.8205 0.004 0.000 0.000 0.980 0.016
#> DRR006401 4 0.1124 0.8179 0.000 0.004 0.000 0.960 0.036
#> DRR006402 4 0.1124 0.8179 0.000 0.004 0.000 0.960 0.036
#> DRR006403 4 0.0451 0.8199 0.000 0.004 0.000 0.988 0.008
#> DRR006404 4 0.0693 0.8167 0.000 0.012 0.000 0.980 0.008
#> DRR006405 2 0.3928 0.6024 0.004 0.700 0.000 0.296 0.000
#> DRR006406 2 0.4452 0.0998 0.004 0.500 0.000 0.496 0.000
#> DRR006407 4 0.2723 0.7440 0.000 0.124 0.000 0.864 0.012
#> DRR006408 4 0.1357 0.8150 0.004 0.000 0.000 0.948 0.048
#> DRR006409 4 0.0833 0.8174 0.016 0.004 0.000 0.976 0.004
#> DRR006410 4 0.3001 0.7553 0.144 0.004 0.000 0.844 0.008
#> DRR006411 2 0.0451 0.8624 0.004 0.988 0.000 0.000 0.008
#> DRR006412 2 0.0510 0.8584 0.000 0.984 0.000 0.000 0.016
#> DRR006413 1 0.0833 0.8753 0.976 0.004 0.004 0.000 0.016
#> DRR006414 1 0.2408 0.8398 0.892 0.000 0.096 0.004 0.008
#> DRR006415 3 0.1124 0.9330 0.036 0.000 0.960 0.000 0.004
#> DRR006416 1 0.4049 0.6018 0.724 0.264 0.004 0.004 0.004
#> DRR006417 3 0.2012 0.8928 0.020 0.060 0.920 0.000 0.000
#> DRR006418 2 0.1197 0.8588 0.048 0.952 0.000 0.000 0.000
#> DRR006419 3 0.6567 0.0826 0.160 0.372 0.460 0.000 0.008
#> DRR006420 1 0.4348 0.6835 0.744 0.032 0.216 0.000 0.008
#> DRR006421 3 0.0000 0.9499 0.000 0.000 1.000 0.000 0.000
#> DRR006422 1 0.1657 0.8722 0.948 0.008 0.008 0.028 0.008
#> DRR006423 2 0.0404 0.8601 0.000 0.988 0.000 0.000 0.012
#> DRR006424 1 0.0854 0.8755 0.976 0.008 0.000 0.004 0.012
#> DRR006425 2 0.4054 0.7005 0.028 0.748 0.000 0.224 0.000
#> DRR006426 2 0.0510 0.8630 0.016 0.984 0.000 0.000 0.000
#> DRR006427 5 0.2448 0.9240 0.000 0.020 0.088 0.000 0.892
#> DRR006428 3 0.0671 0.9459 0.016 0.000 0.980 0.000 0.004
#> DRR006429 2 0.1814 0.8548 0.028 0.940 0.004 0.024 0.004
#> DRR006430 1 0.1856 0.8724 0.940 0.012 0.008 0.032 0.008
#> DRR006431 1 0.3787 0.7303 0.784 0.004 0.008 0.196 0.008
#> DRR006432 2 0.0404 0.8628 0.012 0.988 0.000 0.000 0.000
#> DRR006433 4 0.4698 0.1197 0.000 0.004 0.468 0.520 0.008
#> DRR006434 3 0.0404 0.9487 0.000 0.000 0.988 0.000 0.012
#> DRR006435 5 0.1830 0.9690 0.000 0.028 0.040 0.000 0.932
#> DRR006436 5 0.1830 0.9690 0.000 0.028 0.040 0.000 0.932
#> DRR006437 1 0.1992 0.8600 0.924 0.000 0.000 0.032 0.044
#> DRR006438 3 0.0609 0.9457 0.020 0.000 0.980 0.000 0.000
#> DRR006439 3 0.0771 0.9439 0.020 0.000 0.976 0.000 0.004
#> DRR006440 3 0.0404 0.9487 0.000 0.000 0.988 0.000 0.012
#> DRR006441 2 0.0404 0.8601 0.000 0.988 0.000 0.000 0.012
#> DRR006442 3 0.0566 0.9475 0.012 0.000 0.984 0.000 0.004
#> DRR006443 3 0.0404 0.9487 0.000 0.000 0.988 0.000 0.012
#> DRR006444 5 0.1661 0.9669 0.000 0.024 0.036 0.000 0.940
#> DRR006445 1 0.1442 0.8720 0.952 0.012 0.000 0.004 0.032
#> DRR006446 2 0.0290 0.8611 0.000 0.992 0.000 0.000 0.008
#> DRR006447 2 0.2953 0.7926 0.144 0.844 0.000 0.000 0.012
#> DRR006448 4 0.5122 0.5263 0.312 0.000 0.000 0.628 0.060
#> DRR006449 1 0.3446 0.8052 0.840 0.008 0.000 0.116 0.036
#> DRR006450 1 0.1329 0.8708 0.956 0.004 0.000 0.008 0.032
#> DRR006451 2 0.6261 0.0766 0.044 0.476 0.000 0.428 0.052
#> DRR006452 1 0.1202 0.8718 0.960 0.004 0.000 0.004 0.032
#> DRR006453 1 0.1018 0.8748 0.968 0.016 0.000 0.000 0.016
#> DRR006454 1 0.5197 0.6761 0.712 0.188 0.000 0.020 0.080
#> DRR006455 5 0.0898 0.9439 0.000 0.000 0.020 0.008 0.972
#> DRR006456 3 0.0290 0.9493 0.000 0.000 0.992 0.000 0.008
#> DRR006457 3 0.0324 0.9492 0.004 0.000 0.992 0.000 0.004
#> DRR006458 1 0.1905 0.8689 0.936 0.008 0.008 0.040 0.008
#> DRR006459 4 0.5677 0.5988 0.232 0.028 0.060 0.672 0.008
#> DRR006460 4 0.1502 0.8116 0.004 0.000 0.000 0.940 0.056
#> DRR006461 3 0.0290 0.9493 0.000 0.000 0.992 0.000 0.008
#> DRR006462 4 0.6278 0.5411 0.248 0.116 0.000 0.604 0.032
#> DRR006463 3 0.0404 0.9487 0.000 0.000 0.988 0.000 0.012
#> DRR006464 2 0.0290 0.8624 0.008 0.992 0.000 0.000 0.000
#> DRR006465 1 0.5724 0.5887 0.648 0.232 0.004 0.108 0.008
#> DRR006466 3 0.0912 0.9399 0.000 0.000 0.972 0.016 0.012
#> DRR006467 1 0.0902 0.8737 0.976 0.008 0.008 0.004 0.004
#> DRR006468 5 0.1830 0.9690 0.000 0.028 0.040 0.000 0.932
#> DRR006469 2 0.0404 0.8601 0.000 0.988 0.000 0.000 0.012
#> DRR006470 2 0.1568 0.8562 0.036 0.944 0.020 0.000 0.000
#> DRR006471 2 0.4783 0.4428 0.372 0.608 0.008 0.004 0.008
#> DRR006472 3 0.0960 0.9419 0.016 0.004 0.972 0.000 0.008
#> DRR006473 2 0.1695 0.8558 0.044 0.940 0.008 0.000 0.008
#> DRR006474 3 0.2267 0.9033 0.008 0.000 0.916 0.028 0.048
#> DRR006475 1 0.5267 0.3532 0.572 0.028 0.388 0.004 0.008
#> DRR006476 4 0.2630 0.7781 0.000 0.012 0.080 0.892 0.016
#> DRR006477 4 0.2708 0.7820 0.000 0.000 0.072 0.884 0.044
#> DRR006478 2 0.3428 0.8186 0.060 0.860 0.008 0.064 0.008
#> DRR006479 3 0.1043 0.9331 0.040 0.000 0.960 0.000 0.000
#> DRR006480 1 0.1588 0.8720 0.948 0.000 0.028 0.016 0.008
#> DRR006481 3 0.0290 0.9493 0.000 0.000 0.992 0.000 0.008
#> DRR006482 1 0.2922 0.8314 0.872 0.000 0.000 0.056 0.072
#> DRR006483 2 0.3682 0.7774 0.160 0.812 0.008 0.012 0.008
#> DRR006484 3 0.0162 0.9495 0.004 0.000 0.996 0.000 0.000
#> DRR006485 3 0.0404 0.9487 0.000 0.000 0.988 0.000 0.012
#> DRR006486 1 0.3421 0.7181 0.788 0.000 0.204 0.000 0.008
#> DRR006487 3 0.0162 0.9498 0.000 0.000 0.996 0.000 0.004
#> DRR006488 5 0.1907 0.9669 0.000 0.028 0.044 0.000 0.928
#> DRR006489 1 0.0960 0.8739 0.972 0.016 0.008 0.004 0.000
#> DRR006490 3 0.0703 0.9439 0.024 0.000 0.976 0.000 0.000
#> DRR006491 3 0.0955 0.9398 0.028 0.000 0.968 0.000 0.004
#> DRR006492 4 0.5831 0.6067 0.152 0.000 0.168 0.660 0.020
#> DRR006493 3 0.0290 0.9493 0.000 0.000 0.992 0.000 0.008
#> DRR006494 1 0.3721 0.7809 0.816 0.004 0.148 0.024 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 3 0.1636 0.89441 0.024 0.000 0.936 0.004 0.036 0.000
#> DRR006375 1 0.7220 -0.00473 0.340 0.340 0.000 0.096 0.000 0.224
#> DRR006376 4 0.3104 0.62946 0.016 0.184 0.000 0.800 0.000 0.000
#> DRR006377 4 0.4043 0.58396 0.128 0.116 0.000 0.756 0.000 0.000
#> DRR006378 2 0.0622 0.87856 0.008 0.980 0.000 0.012 0.000 0.000
#> DRR006379 4 0.4035 0.61676 0.004 0.052 0.000 0.740 0.000 0.204
#> DRR006380 6 0.3468 0.27182 0.000 0.000 0.008 0.264 0.000 0.728
#> DRR006381 6 0.3828 0.24671 0.440 0.000 0.000 0.000 0.000 0.560
#> DRR006382 3 0.2821 0.83175 0.096 0.000 0.860 0.000 0.004 0.040
#> DRR006383 1 0.3394 0.55832 0.776 0.000 0.024 0.000 0.000 0.200
#> DRR006384 6 0.4894 -0.07210 0.000 0.000 0.000 0.376 0.068 0.556
#> DRR006385 6 0.3102 0.60316 0.156 0.028 0.000 0.000 0.000 0.816
#> DRR006386 5 0.0000 0.99792 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006387 6 0.4388 0.34748 0.056 0.000 0.000 0.276 0.000 0.668
#> DRR006388 2 0.2121 0.84508 0.012 0.892 0.000 0.000 0.000 0.096
#> DRR006389 2 0.2121 0.84508 0.012 0.892 0.000 0.000 0.000 0.096
#> DRR006390 2 0.0146 0.87959 0.000 0.996 0.000 0.000 0.000 0.004
#> DRR006391 2 0.0146 0.87959 0.000 0.996 0.000 0.000 0.000 0.004
#> DRR006392 1 0.1003 0.71792 0.964 0.004 0.000 0.004 0.000 0.028
#> DRR006393 1 0.0622 0.71979 0.980 0.000 0.000 0.008 0.000 0.012
#> DRR006394 2 0.0260 0.87879 0.000 0.992 0.000 0.008 0.000 0.000
#> DRR006395 4 0.3634 0.57592 0.000 0.000 0.008 0.696 0.000 0.296
#> DRR006396 6 0.3330 0.50375 0.284 0.000 0.000 0.000 0.000 0.716
#> DRR006397 2 0.2300 0.79318 0.000 0.856 0.000 0.000 0.000 0.144
#> DRR006398 2 0.2260 0.79761 0.000 0.860 0.000 0.000 0.000 0.140
#> DRR006399 4 0.3867 0.29654 0.000 0.000 0.000 0.512 0.000 0.488
#> DRR006400 4 0.3866 0.30221 0.000 0.000 0.000 0.516 0.000 0.484
#> DRR006401 4 0.2048 0.67137 0.000 0.000 0.000 0.880 0.000 0.120
#> DRR006402 4 0.1910 0.67410 0.000 0.000 0.000 0.892 0.000 0.108
#> DRR006403 4 0.3244 0.59315 0.000 0.000 0.000 0.732 0.000 0.268
#> DRR006404 4 0.0622 0.67955 0.008 0.000 0.000 0.980 0.000 0.012
#> DRR006405 4 0.4229 0.56848 0.068 0.220 0.000 0.712 0.000 0.000
#> DRR006406 4 0.3408 0.62711 0.048 0.152 0.000 0.800 0.000 0.000
#> DRR006407 4 0.1010 0.68131 0.000 0.036 0.000 0.960 0.000 0.004
#> DRR006408 4 0.3737 0.43017 0.000 0.000 0.000 0.608 0.000 0.392
#> DRR006409 4 0.1644 0.66078 0.076 0.000 0.004 0.920 0.000 0.000
#> DRR006410 4 0.2842 0.64984 0.044 0.000 0.000 0.852 0.000 0.104
#> DRR006411 2 0.1531 0.85683 0.000 0.928 0.004 0.000 0.000 0.068
#> DRR006412 2 0.0260 0.87921 0.000 0.992 0.000 0.000 0.000 0.008
#> DRR006413 1 0.3578 0.33294 0.660 0.000 0.000 0.000 0.000 0.340
#> DRR006414 1 0.5714 0.01954 0.464 0.000 0.168 0.000 0.000 0.368
#> DRR006415 3 0.1168 0.90500 0.016 0.000 0.956 0.000 0.000 0.028
#> DRR006416 1 0.2113 0.70222 0.908 0.060 0.000 0.004 0.000 0.028
#> DRR006417 3 0.3171 0.69283 0.000 0.204 0.784 0.000 0.000 0.012
#> DRR006418 2 0.1643 0.86434 0.068 0.924 0.000 0.000 0.000 0.008
#> DRR006419 2 0.4243 0.58867 0.008 0.688 0.272 0.000 0.000 0.032
#> DRR006420 1 0.2056 0.68988 0.904 0.004 0.012 0.000 0.000 0.080
#> DRR006421 3 0.0000 0.92023 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006422 1 0.1812 0.70891 0.912 0.000 0.000 0.080 0.000 0.008
#> DRR006423 2 0.2513 0.79800 0.140 0.852 0.000 0.008 0.000 0.000
#> DRR006424 1 0.3052 0.56323 0.780 0.004 0.000 0.000 0.000 0.216
#> DRR006425 4 0.4723 0.50454 0.180 0.140 0.000 0.680 0.000 0.000
#> DRR006426 2 0.1584 0.85958 0.064 0.928 0.000 0.008 0.000 0.000
#> DRR006427 5 0.0260 0.98975 0.008 0.000 0.000 0.000 0.992 0.000
#> DRR006428 3 0.0405 0.91887 0.008 0.000 0.988 0.004 0.000 0.000
#> DRR006429 2 0.5836 0.42255 0.268 0.572 0.032 0.128 0.000 0.000
#> DRR006430 1 0.1644 0.70941 0.920 0.004 0.000 0.076 0.000 0.000
#> DRR006431 1 0.3601 0.49795 0.684 0.000 0.000 0.312 0.000 0.004
#> DRR006432 2 0.1584 0.85926 0.064 0.928 0.000 0.008 0.000 0.000
#> DRR006433 3 0.3867 0.06943 0.000 0.000 0.512 0.488 0.000 0.000
#> DRR006434 3 0.0146 0.91995 0.000 0.000 0.996 0.000 0.000 0.004
#> DRR006435 5 0.0000 0.99792 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006436 5 0.0000 0.99792 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006437 6 0.1320 0.61144 0.036 0.016 0.000 0.000 0.000 0.948
#> DRR006438 3 0.0000 0.92023 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006439 3 0.1152 0.90172 0.044 0.000 0.952 0.004 0.000 0.000
#> DRR006440 3 0.0291 0.91893 0.000 0.004 0.992 0.000 0.000 0.004
#> DRR006441 2 0.0790 0.87420 0.000 0.968 0.000 0.000 0.000 0.032
#> DRR006442 3 0.1387 0.88742 0.068 0.000 0.932 0.000 0.000 0.000
#> DRR006443 3 0.0146 0.91995 0.000 0.000 0.996 0.000 0.000 0.004
#> DRR006444 5 0.0000 0.99792 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006445 6 0.4338 0.08168 0.488 0.020 0.000 0.000 0.000 0.492
#> DRR006446 2 0.0665 0.88054 0.004 0.980 0.000 0.008 0.000 0.008
#> DRR006447 2 0.1858 0.85568 0.012 0.912 0.000 0.000 0.000 0.076
#> DRR006448 6 0.3770 0.54130 0.076 0.000 0.000 0.148 0.000 0.776
#> DRR006449 6 0.3320 0.57883 0.212 0.000 0.000 0.016 0.000 0.772
#> DRR006450 6 0.4051 0.24707 0.432 0.008 0.000 0.000 0.000 0.560
#> DRR006451 6 0.4855 0.33765 0.004 0.272 0.000 0.084 0.000 0.640
#> DRR006452 6 0.3979 0.20265 0.456 0.004 0.000 0.000 0.000 0.540
#> DRR006453 1 0.3349 0.52440 0.748 0.008 0.000 0.000 0.000 0.244
#> DRR006454 6 0.2320 0.55953 0.004 0.132 0.000 0.000 0.000 0.864
#> DRR006455 5 0.0146 0.99476 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006456 3 0.0146 0.92001 0.004 0.000 0.996 0.000 0.000 0.000
#> DRR006457 3 0.0508 0.91752 0.012 0.000 0.984 0.004 0.000 0.000
#> DRR006458 1 0.2520 0.66603 0.844 0.000 0.004 0.152 0.000 0.000
#> DRR006459 4 0.4067 0.00728 0.444 0.008 0.000 0.548 0.000 0.000
#> DRR006460 4 0.2697 0.63966 0.000 0.000 0.000 0.812 0.000 0.188
#> DRR006461 3 0.0146 0.91995 0.000 0.000 0.996 0.000 0.000 0.004
#> DRR006462 6 0.3611 0.51412 0.012 0.072 0.000 0.104 0.000 0.812
#> DRR006463 3 0.0146 0.91995 0.000 0.000 0.996 0.000 0.000 0.004
#> DRR006464 2 0.1124 0.87207 0.036 0.956 0.000 0.008 0.000 0.000
#> DRR006465 1 0.4039 0.41673 0.632 0.016 0.000 0.352 0.000 0.000
#> DRR006466 3 0.0146 0.91955 0.000 0.000 0.996 0.004 0.000 0.000
#> DRR006467 1 0.1010 0.71291 0.960 0.004 0.000 0.000 0.000 0.036
#> DRR006468 5 0.0000 0.99792 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006469 2 0.0458 0.87829 0.000 0.984 0.000 0.000 0.000 0.016
#> DRR006470 2 0.1346 0.87779 0.024 0.952 0.016 0.000 0.000 0.008
#> DRR006471 1 0.1148 0.71985 0.960 0.020 0.004 0.016 0.000 0.000
#> DRR006472 3 0.3463 0.66052 0.240 0.008 0.748 0.004 0.000 0.000
#> DRR006473 2 0.5035 0.16996 0.440 0.508 0.008 0.036 0.008 0.000
#> DRR006474 1 0.5714 0.04911 0.448 0.000 0.104 0.432 0.016 0.000
#> DRR006475 1 0.1578 0.70868 0.936 0.004 0.048 0.012 0.000 0.000
#> DRR006476 4 0.3193 0.63073 0.000 0.000 0.124 0.824 0.000 0.052
#> DRR006477 3 0.5879 -0.02861 0.000 0.000 0.448 0.344 0.000 0.208
#> DRR006478 1 0.5845 0.09832 0.432 0.192 0.000 0.376 0.000 0.000
#> DRR006479 3 0.0291 0.91994 0.004 0.000 0.992 0.000 0.000 0.004
#> DRR006480 1 0.0935 0.71523 0.964 0.000 0.004 0.000 0.000 0.032
#> DRR006481 3 0.0000 0.92023 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006482 6 0.0964 0.60001 0.012 0.016 0.000 0.004 0.000 0.968
#> DRR006483 1 0.3763 0.59765 0.768 0.172 0.000 0.060 0.000 0.000
#> DRR006484 3 0.0000 0.92023 0.000 0.000 1.000 0.000 0.000 0.000
#> DRR006485 3 0.0146 0.91995 0.000 0.000 0.996 0.000 0.000 0.004
#> DRR006486 1 0.1074 0.71540 0.960 0.000 0.012 0.000 0.000 0.028
#> DRR006487 3 0.0405 0.91907 0.004 0.000 0.988 0.000 0.000 0.008
#> DRR006488 5 0.0000 0.99792 0.000 0.000 0.000 0.000 1.000 0.000
#> DRR006489 1 0.3230 0.56810 0.776 0.012 0.000 0.000 0.000 0.212
#> DRR006490 3 0.0363 0.91880 0.012 0.000 0.988 0.000 0.000 0.000
#> DRR006491 3 0.1327 0.88967 0.064 0.000 0.936 0.000 0.000 0.000
#> DRR006492 4 0.6864 0.10304 0.044 0.000 0.328 0.328 0.000 0.300
#> DRR006493 3 0.0146 0.92001 0.004 0.000 0.996 0.000 0.000 0.000
#> DRR006494 1 0.1398 0.71554 0.940 0.000 0.008 0.052 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)
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 16187 rows and 121 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 0.980 0.972 0.986 0.4397 0.554 0.554
#> 3 3 0.766 0.898 0.891 0.2197 0.947 0.904
#> 4 4 0.699 0.833 0.881 0.2945 0.799 0.599
#> 5 5 0.832 0.803 0.911 0.1413 0.903 0.677
#> 6 6 0.806 0.777 0.852 0.0372 0.947 0.761
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
#> DRR006374 2 0.0000 0.965 0.000 1.000
#> DRR006375 1 0.0000 0.995 1.000 0.000
#> DRR006376 1 0.0672 0.991 0.992 0.008
#> DRR006377 1 0.0672 0.991 0.992 0.008
#> DRR006378 2 0.0000 0.965 0.000 1.000
#> DRR006379 1 0.0672 0.991 0.992 0.008
#> DRR006380 2 0.0000 0.965 0.000 1.000
#> DRR006381 1 0.0000 0.995 1.000 0.000
#> DRR006382 2 0.0000 0.965 0.000 1.000
#> DRR006383 1 0.0000 0.995 1.000 0.000
#> DRR006384 2 0.0000 0.965 0.000 1.000
#> DRR006385 1 0.0000 0.995 1.000 0.000
#> DRR006386 2 0.0000 0.965 0.000 1.000
#> DRR006387 1 0.0000 0.995 1.000 0.000
#> DRR006388 1 0.0672 0.991 0.992 0.008
#> DRR006389 1 0.0672 0.991 0.992 0.008
#> DRR006390 2 0.0000 0.965 0.000 1.000
#> DRR006391 2 0.0000 0.965 0.000 1.000
#> DRR006392 1 0.0000 0.995 1.000 0.000
#> DRR006393 1 0.0000 0.995 1.000 0.000
#> DRR006394 2 0.0000 0.965 0.000 1.000
#> DRR006395 1 0.0376 0.993 0.996 0.004
#> DRR006396 1 0.0000 0.995 1.000 0.000
#> DRR006397 1 0.0672 0.991 0.992 0.008
#> DRR006398 1 0.0672 0.991 0.992 0.008
#> DRR006399 1 0.0672 0.991 0.992 0.008
#> DRR006400 1 0.0672 0.991 0.992 0.008
#> DRR006401 2 0.0000 0.965 0.000 1.000
#> DRR006402 2 0.0000 0.965 0.000 1.000
#> DRR006403 1 0.0672 0.991 0.992 0.008
#> DRR006404 1 0.0672 0.991 0.992 0.008
#> DRR006405 1 0.0672 0.991 0.992 0.008
#> DRR006406 1 0.0672 0.991 0.992 0.008
#> DRR006407 2 0.4562 0.889 0.096 0.904
#> DRR006408 2 0.0000 0.965 0.000 1.000
#> DRR006409 1 0.0000 0.995 1.000 0.000
#> DRR006410 1 0.0000 0.995 1.000 0.000
#> DRR006411 2 0.4690 0.886 0.100 0.900
#> DRR006412 2 0.0000 0.965 0.000 1.000
#> DRR006413 1 0.0000 0.995 1.000 0.000
#> DRR006414 1 0.0000 0.995 1.000 0.000
#> DRR006415 1 0.0000 0.995 1.000 0.000
#> DRR006416 1 0.0376 0.993 0.996 0.004
#> DRR006417 1 0.0000 0.995 1.000 0.000
#> DRR006418 1 0.0000 0.995 1.000 0.000
#> DRR006419 1 0.0000 0.995 1.000 0.000
#> DRR006420 1 0.0000 0.995 1.000 0.000
#> DRR006421 1 0.0000 0.995 1.000 0.000
#> DRR006422 1 0.1184 0.984 0.984 0.016
#> DRR006423 2 0.0000 0.965 0.000 1.000
#> DRR006424 1 0.0000 0.995 1.000 0.000
#> DRR006425 2 0.5294 0.866 0.120 0.880
#> DRR006426 1 0.0000 0.995 1.000 0.000
#> DRR006427 2 0.0000 0.965 0.000 1.000
#> DRR006428 1 0.0000 0.995 1.000 0.000
#> DRR006429 2 0.8207 0.693 0.256 0.744
#> DRR006430 1 0.0000 0.995 1.000 0.000
#> DRR006431 1 0.0000 0.995 1.000 0.000
#> DRR006432 1 0.0000 0.995 1.000 0.000
#> DRR006433 1 0.1184 0.984 0.984 0.016
#> DRR006434 2 0.0000 0.965 0.000 1.000
#> DRR006435 2 0.0000 0.965 0.000 1.000
#> DRR006436 2 0.0000 0.965 0.000 1.000
#> DRR006437 1 0.0000 0.995 1.000 0.000
#> DRR006438 1 0.0000 0.995 1.000 0.000
#> DRR006439 1 0.0000 0.995 1.000 0.000
#> DRR006440 2 0.0000 0.965 0.000 1.000
#> DRR006441 2 0.0000 0.965 0.000 1.000
#> DRR006442 1 0.0000 0.995 1.000 0.000
#> DRR006443 2 0.0000 0.965 0.000 1.000
#> DRR006444 2 0.0000 0.965 0.000 1.000
#> DRR006445 1 0.0000 0.995 1.000 0.000
#> DRR006446 2 0.0000 0.965 0.000 1.000
#> DRR006447 1 0.0000 0.995 1.000 0.000
#> DRR006448 1 0.0672 0.991 0.992 0.008
#> DRR006449 1 0.0000 0.995 1.000 0.000
#> DRR006450 1 0.0000 0.995 1.000 0.000
#> DRR006451 1 0.0672 0.991 0.992 0.008
#> DRR006452 1 0.0000 0.995 1.000 0.000
#> DRR006453 1 0.0000 0.995 1.000 0.000
#> DRR006454 2 0.8267 0.686 0.260 0.740
#> DRR006455 2 0.0000 0.965 0.000 1.000
#> DRR006456 1 0.0000 0.995 1.000 0.000
#> DRR006457 1 0.0000 0.995 1.000 0.000
#> DRR006458 1 0.0000 0.995 1.000 0.000
#> DRR006459 1 0.0000 0.995 1.000 0.000
#> DRR006460 2 0.0000 0.965 0.000 1.000
#> DRR006461 2 0.0000 0.965 0.000 1.000
#> DRR006462 1 0.0000 0.995 1.000 0.000
#> DRR006463 2 0.0000 0.965 0.000 1.000
#> DRR006464 2 0.8207 0.693 0.256 0.744
#> DRR006465 1 0.0000 0.995 1.000 0.000
#> DRR006466 1 0.6531 0.792 0.832 0.168
#> DRR006467 1 0.0000 0.995 1.000 0.000
#> DRR006468 2 0.0000 0.965 0.000 1.000
#> DRR006469 2 0.0000 0.965 0.000 1.000
#> DRR006470 1 0.0000 0.995 1.000 0.000
#> DRR006471 1 0.0000 0.995 1.000 0.000
#> DRR006472 1 0.1184 0.984 0.984 0.016
#> DRR006473 2 0.0000 0.965 0.000 1.000
#> DRR006474 2 0.0000 0.965 0.000 1.000
#> DRR006475 1 0.0000 0.995 1.000 0.000
#> DRR006476 2 0.8207 0.693 0.256 0.744
#> DRR006477 1 0.1184 0.984 0.984 0.016
#> DRR006478 1 0.0000 0.995 1.000 0.000
#> DRR006479 1 0.0000 0.995 1.000 0.000
#> DRR006480 1 0.0000 0.995 1.000 0.000
#> DRR006481 1 0.0000 0.995 1.000 0.000
#> DRR006482 1 0.0000 0.995 1.000 0.000
#> DRR006483 1 0.0000 0.995 1.000 0.000
#> DRR006484 1 0.0000 0.995 1.000 0.000
#> DRR006485 2 0.0000 0.965 0.000 1.000
#> DRR006486 1 0.0000 0.995 1.000 0.000
#> DRR006487 1 0.0000 0.995 1.000 0.000
#> DRR006488 2 0.0000 0.965 0.000 1.000
#> DRR006489 1 0.0000 0.995 1.000 0.000
#> DRR006490 1 0.0000 0.995 1.000 0.000
#> DRR006491 1 0.0000 0.995 1.000 0.000
#> DRR006492 1 0.0000 0.995 1.000 0.000
#> DRR006493 1 0.0000 0.995 1.000 0.000
#> DRR006494 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
#> DRR006374 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006375 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006376 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006377 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006378 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006379 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006380 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006381 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006382 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006383 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006384 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006385 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006386 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006387 1 0.2066 0.920 0.940 0.060 0.000
#> DRR006388 1 0.3752 0.900 0.856 0.144 0.000
#> DRR006389 1 0.3752 0.900 0.856 0.144 0.000
#> DRR006390 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006391 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006392 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006393 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006394 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006395 1 0.2959 0.915 0.900 0.100 0.000
#> DRR006396 1 0.2066 0.920 0.940 0.060 0.000
#> DRR006397 1 0.3752 0.900 0.856 0.144 0.000
#> DRR006398 1 0.3752 0.900 0.856 0.144 0.000
#> DRR006399 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006400 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006401 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006402 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006403 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006404 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006405 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006406 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006407 2 0.4346 0.823 0.000 0.816 0.184
#> DRR006408 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006409 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006410 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006411 2 0.4291 0.819 0.000 0.820 0.180
#> DRR006412 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006413 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006414 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006415 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006416 1 0.3038 0.916 0.896 0.104 0.000
#> DRR006417 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006418 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006419 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006420 1 0.2711 0.920 0.912 0.088 0.000
#> DRR006421 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006422 1 0.5497 0.804 0.708 0.292 0.000
#> DRR006423 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006424 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006425 2 0.4002 0.804 0.000 0.840 0.160
#> DRR006426 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006427 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006428 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006429 2 0.1031 0.688 0.000 0.976 0.024
#> DRR006430 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006431 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006432 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006433 1 0.5497 0.804 0.708 0.292 0.000
#> DRR006434 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006435 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006436 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006437 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006438 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006439 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006440 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006441 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006442 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006443 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006444 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006445 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006446 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006447 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006448 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006449 1 0.2165 0.920 0.936 0.064 0.000
#> DRR006450 1 0.2711 0.920 0.912 0.088 0.000
#> DRR006451 1 0.5431 0.811 0.716 0.284 0.000
#> DRR006452 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006453 1 0.2711 0.920 0.912 0.088 0.000
#> DRR006454 2 0.0892 0.683 0.000 0.980 0.020
#> DRR006455 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006456 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006457 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006458 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006459 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006460 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006461 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006462 1 0.2066 0.920 0.940 0.060 0.000
#> DRR006463 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006464 2 0.1031 0.688 0.000 0.976 0.024
#> DRR006465 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006466 1 0.6252 0.584 0.556 0.444 0.000
#> DRR006467 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006468 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006469 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006470 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006471 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006472 1 0.5497 0.804 0.708 0.292 0.000
#> DRR006473 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006474 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006475 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006476 2 0.1031 0.688 0.000 0.976 0.024
#> DRR006477 1 0.5497 0.804 0.708 0.292 0.000
#> DRR006478 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006479 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006480 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006481 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006482 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006483 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006484 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006485 2 0.5397 0.900 0.000 0.720 0.280
#> DRR006486 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006487 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006488 3 0.0000 1.000 0.000 0.000 1.000
#> DRR006489 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006490 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006491 1 0.0237 0.912 0.996 0.004 0.000
#> DRR006492 1 0.2066 0.920 0.940 0.060 0.000
#> DRR006493 1 0.2796 0.919 0.908 0.092 0.000
#> DRR006494 1 0.0237 0.912 0.996 0.004 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006375 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006376 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006377 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006378 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006379 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006380 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006381 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006382 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006383 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006384 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006385 3 0.3444 0.839 0.184 0.000 0.816 0.000
#> DRR006386 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006387 3 0.4543 0.715 0.324 0.000 0.676 0.000
#> DRR006388 3 0.3948 0.827 0.136 0.036 0.828 0.000
#> DRR006389 3 0.3948 0.827 0.136 0.036 0.828 0.000
#> DRR006390 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006391 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006392 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006393 1 0.4977 -0.251 0.540 0.000 0.460 0.000
#> DRR006394 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006395 3 0.4576 0.772 0.260 0.012 0.728 0.000
#> DRR006396 3 0.4543 0.715 0.324 0.000 0.676 0.000
#> DRR006397 3 0.3948 0.827 0.136 0.036 0.828 0.000
#> DRR006398 3 0.3948 0.827 0.136 0.036 0.828 0.000
#> DRR006399 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006400 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006401 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006402 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006403 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006404 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006405 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006406 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006407 2 0.0000 0.875 0.000 1.000 0.000 0.000
#> DRR006408 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006409 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006410 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006411 2 0.0188 0.873 0.000 0.996 0.004 0.000
#> DRR006412 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006413 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006414 3 0.4933 0.542 0.432 0.000 0.568 0.000
#> DRR006415 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006416 3 0.3266 0.838 0.168 0.000 0.832 0.000
#> DRR006417 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006418 3 0.3444 0.839 0.184 0.000 0.816 0.000
#> DRR006419 3 0.3444 0.839 0.184 0.000 0.816 0.000
#> DRR006420 3 0.3649 0.829 0.204 0.000 0.796 0.000
#> DRR006421 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006422 3 0.2408 0.751 0.000 0.104 0.896 0.000
#> DRR006423 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006424 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006425 2 0.1798 0.865 0.000 0.944 0.040 0.016
#> DRR006426 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006427 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006428 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006429 2 0.3172 0.761 0.000 0.840 0.160 0.000
#> DRR006430 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006432 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006433 3 0.2408 0.751 0.000 0.104 0.896 0.000
#> DRR006434 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006435 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006436 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006437 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006438 3 0.4933 0.542 0.432 0.000 0.568 0.000
#> DRR006439 3 0.3486 0.837 0.188 0.000 0.812 0.000
#> DRR006440 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006441 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006442 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006443 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006444 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006445 3 0.3444 0.839 0.184 0.000 0.816 0.000
#> DRR006446 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006447 3 0.3444 0.839 0.184 0.000 0.816 0.000
#> DRR006448 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006449 3 0.4522 0.721 0.320 0.000 0.680 0.000
#> DRR006450 3 0.3688 0.828 0.208 0.000 0.792 0.000
#> DRR006451 3 0.2281 0.752 0.000 0.096 0.904 0.000
#> DRR006452 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006453 3 0.3569 0.833 0.196 0.000 0.804 0.000
#> DRR006454 2 0.3219 0.757 0.000 0.836 0.164 0.000
#> DRR006455 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006456 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006457 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006458 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006460 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006461 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006462 3 0.4543 0.715 0.324 0.000 0.676 0.000
#> DRR006463 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006464 2 0.3172 0.761 0.000 0.840 0.160 0.000
#> DRR006465 1 0.4977 -0.251 0.540 0.000 0.460 0.000
#> DRR006466 3 0.4103 0.570 0.000 0.256 0.744 0.000
#> DRR006467 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006468 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006469 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006470 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006471 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006472 3 0.2408 0.751 0.000 0.104 0.896 0.000
#> DRR006473 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006474 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006475 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006476 2 0.3172 0.761 0.000 0.840 0.160 0.000
#> DRR006477 3 0.2408 0.751 0.000 0.104 0.896 0.000
#> DRR006478 1 0.4977 -0.251 0.540 0.000 0.460 0.000
#> DRR006479 3 0.4933 0.542 0.432 0.000 0.568 0.000
#> DRR006480 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006481 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006482 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006483 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006484 3 0.4933 0.542 0.432 0.000 0.568 0.000
#> DRR006485 2 0.2281 0.932 0.000 0.904 0.000 0.096
#> DRR006486 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006487 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006488 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> DRR006489 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006490 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006491 1 0.0000 0.927 1.000 0.000 0.000 0.000
#> DRR006492 3 0.4454 0.735 0.308 0.000 0.692 0.000
#> DRR006493 3 0.3400 0.840 0.180 0.000 0.820 0.000
#> DRR006494 1 0.0000 0.927 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
#> DRR006374 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006375 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006376 4 0.0880 0.88930 0.000 0.000 0.032 0.968 0
#> DRR006377 4 0.0510 0.88825 0.000 0.000 0.016 0.984 0
#> DRR006378 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006379 4 0.0880 0.88930 0.000 0.000 0.032 0.968 0
#> DRR006380 2 0.0290 0.93552 0.000 0.992 0.000 0.008 0
#> DRR006381 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006382 2 0.0510 0.93276 0.000 0.984 0.000 0.016 0
#> DRR006383 3 0.0510 0.77019 0.000 0.000 0.984 0.016 0
#> DRR006384 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006385 3 0.0451 0.77148 0.004 0.000 0.988 0.008 0
#> DRR006386 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006387 3 0.3913 0.54017 0.324 0.000 0.676 0.000 0
#> DRR006388 4 0.3274 0.73300 0.000 0.000 0.220 0.780 0
#> DRR006389 4 0.3274 0.73300 0.000 0.000 0.220 0.780 0
#> DRR006390 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006391 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006392 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006393 1 0.4287 0.00597 0.540 0.000 0.460 0.000 0
#> DRR006394 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006395 4 0.6359 0.25473 0.260 0.000 0.220 0.520 0
#> DRR006396 3 0.3913 0.54017 0.324 0.000 0.676 0.000 0
#> DRR006397 4 0.3274 0.73300 0.000 0.000 0.220 0.780 0
#> DRR006398 4 0.3274 0.73300 0.000 0.000 0.220 0.780 0
#> DRR006399 4 0.0880 0.88930 0.000 0.000 0.032 0.968 0
#> DRR006400 4 0.0880 0.88930 0.000 0.000 0.032 0.968 0
#> DRR006401 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006402 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006403 4 0.0880 0.88930 0.000 0.000 0.032 0.968 0
#> DRR006404 4 0.0880 0.88930 0.000 0.000 0.032 0.968 0
#> DRR006405 4 0.0510 0.88825 0.000 0.000 0.016 0.984 0
#> DRR006406 4 0.0510 0.88825 0.000 0.000 0.016 0.984 0
#> DRR006407 2 0.2179 0.88190 0.000 0.888 0.000 0.112 0
#> DRR006408 2 0.0290 0.93552 0.000 0.992 0.000 0.008 0
#> DRR006409 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006410 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006411 2 0.2230 0.87951 0.000 0.884 0.000 0.116 0
#> DRR006412 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006413 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006414 3 0.4242 0.32080 0.428 0.000 0.572 0.000 0
#> DRR006415 3 0.0703 0.76876 0.000 0.000 0.976 0.024 0
#> DRR006416 4 0.4249 0.34206 0.000 0.000 0.432 0.568 0
#> DRR006417 3 0.2471 0.70611 0.000 0.000 0.864 0.136 0
#> DRR006418 3 0.0566 0.77139 0.004 0.000 0.984 0.012 0
#> DRR006419 3 0.0566 0.77139 0.004 0.000 0.984 0.012 0
#> DRR006420 3 0.1430 0.76590 0.052 0.000 0.944 0.004 0
#> DRR006421 3 0.2690 0.68586 0.000 0.000 0.844 0.156 0
#> DRR006422 4 0.0798 0.88763 0.000 0.008 0.016 0.976 0
#> DRR006423 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006424 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006425 2 0.2471 0.86523 0.000 0.864 0.000 0.136 0
#> DRR006426 3 0.2471 0.70611 0.000 0.000 0.864 0.136 0
#> DRR006427 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006428 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006429 2 0.3636 0.73317 0.000 0.728 0.000 0.272 0
#> DRR006430 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006431 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006432 3 0.2471 0.70611 0.000 0.000 0.864 0.136 0
#> DRR006433 4 0.0798 0.88763 0.000 0.008 0.016 0.976 0
#> DRR006434 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006435 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006436 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006437 3 0.4210 0.03655 0.000 0.000 0.588 0.412 0
#> DRR006438 3 0.4242 0.32080 0.428 0.000 0.572 0.000 0
#> DRR006439 3 0.1251 0.76759 0.008 0.000 0.956 0.036 0
#> DRR006440 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006441 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006442 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006443 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006444 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006445 3 0.0451 0.77148 0.004 0.000 0.988 0.008 0
#> DRR006446 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006447 3 0.0451 0.77148 0.004 0.000 0.988 0.008 0
#> DRR006448 4 0.0880 0.88930 0.000 0.000 0.032 0.968 0
#> DRR006449 3 0.3895 0.54561 0.320 0.000 0.680 0.000 0
#> DRR006450 3 0.3282 0.68440 0.188 0.000 0.804 0.008 0
#> DRR006451 4 0.0880 0.88930 0.000 0.000 0.032 0.968 0
#> DRR006452 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006453 3 0.1041 0.77065 0.032 0.000 0.964 0.004 0
#> DRR006454 2 0.3661 0.72766 0.000 0.724 0.000 0.276 0
#> DRR006455 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006456 3 0.0510 0.77019 0.000 0.000 0.984 0.016 0
#> DRR006457 3 0.2690 0.68586 0.000 0.000 0.844 0.156 0
#> DRR006458 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006459 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006460 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006461 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006462 3 0.3913 0.54017 0.324 0.000 0.676 0.000 0
#> DRR006463 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006464 2 0.3636 0.73317 0.000 0.728 0.000 0.272 0
#> DRR006465 1 0.4287 0.00597 0.540 0.000 0.460 0.000 0
#> DRR006466 4 0.2561 0.71691 0.000 0.144 0.000 0.856 0
#> DRR006467 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006468 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006469 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006470 3 0.1851 0.73768 0.000 0.000 0.912 0.088 0
#> DRR006471 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006472 4 0.0798 0.88763 0.000 0.008 0.016 0.976 0
#> DRR006473 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006474 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006475 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006476 2 0.3636 0.73317 0.000 0.728 0.000 0.272 0
#> DRR006477 4 0.0798 0.88763 0.000 0.008 0.016 0.976 0
#> DRR006478 1 0.4287 0.00597 0.540 0.000 0.460 0.000 0
#> DRR006479 3 0.4242 0.32080 0.428 0.000 0.572 0.000 0
#> DRR006480 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006481 3 0.2690 0.68586 0.000 0.000 0.844 0.156 0
#> DRR006482 3 0.4219 0.02338 0.000 0.000 0.584 0.416 0
#> DRR006483 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006484 3 0.4242 0.32080 0.428 0.000 0.572 0.000 0
#> DRR006485 2 0.0000 0.93764 0.000 1.000 0.000 0.000 0
#> DRR006486 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006487 3 0.0510 0.77019 0.000 0.000 0.984 0.016 0
#> DRR006488 5 0.0000 1.00000 0.000 0.000 0.000 0.000 1
#> DRR006489 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006490 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006491 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
#> DRR006492 3 0.3990 0.55840 0.308 0.000 0.688 0.004 0
#> DRR006493 3 0.0510 0.77019 0.000 0.000 0.984 0.016 0
#> DRR006494 1 0.0000 0.93449 1.000 0.000 0.000 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.0000 0.9045 0.000 1.000 0.000 0.000 0 0.000
#> DRR006375 1 0.3151 0.7784 0.748 0.000 0.252 0.000 0 0.000
#> DRR006376 4 0.1003 0.8048 0.000 0.000 0.016 0.964 0 0.020
#> DRR006377 4 0.0000 0.8010 0.000 0.000 0.000 1.000 0 0.000
#> DRR006378 2 0.0146 0.9026 0.000 0.996 0.000 0.000 0 0.004
#> DRR006379 4 0.1003 0.8048 0.000 0.000 0.016 0.964 0 0.020
#> DRR006380 2 0.3854 -0.1908 0.000 0.536 0.000 0.000 0 0.464
#> DRR006381 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006382 2 0.2793 0.6317 0.000 0.800 0.000 0.000 0 0.200
#> DRR006383 3 0.3672 0.7398 0.000 0.000 0.632 0.000 0 0.368
#> DRR006384 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006385 3 0.3398 0.7406 0.000 0.000 0.740 0.008 0 0.252
#> DRR006386 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006387 3 0.1387 0.6666 0.068 0.000 0.932 0.000 0 0.000
#> DRR006388 4 0.3588 0.7070 0.000 0.000 0.060 0.788 0 0.152
#> DRR006389 4 0.3588 0.7070 0.000 0.000 0.060 0.788 0 0.152
#> DRR006390 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006391 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006392 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006393 3 0.3330 0.4040 0.284 0.000 0.716 0.000 0 0.000
#> DRR006394 2 0.0146 0.9026 0.000 0.996 0.000 0.000 0 0.004
#> DRR006395 4 0.4080 0.3611 0.008 0.000 0.456 0.536 0 0.000
#> DRR006396 3 0.1387 0.6666 0.068 0.000 0.932 0.000 0 0.000
#> DRR006397 4 0.3588 0.7070 0.000 0.000 0.060 0.788 0 0.152
#> DRR006398 4 0.3588 0.7070 0.000 0.000 0.060 0.788 0 0.152
#> DRR006399 4 0.1003 0.8048 0.000 0.000 0.016 0.964 0 0.020
#> DRR006400 4 0.1003 0.8048 0.000 0.000 0.016 0.964 0 0.020
#> DRR006401 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006402 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006403 4 0.1003 0.8048 0.000 0.000 0.016 0.964 0 0.020
#> DRR006404 4 0.1003 0.8048 0.000 0.000 0.016 0.964 0 0.020
#> DRR006405 4 0.0000 0.8010 0.000 0.000 0.000 1.000 0 0.000
#> DRR006406 4 0.0000 0.8010 0.000 0.000 0.000 1.000 0 0.000
#> DRR006407 6 0.4829 0.7623 0.000 0.308 0.000 0.080 0 0.612
#> DRR006408 2 0.3854 -0.1908 0.000 0.536 0.000 0.000 0 0.464
#> DRR006409 1 0.3175 0.7753 0.744 0.000 0.256 0.000 0 0.000
#> DRR006410 1 0.3151 0.7784 0.748 0.000 0.252 0.000 0 0.000
#> DRR006411 6 0.4859 0.7672 0.000 0.304 0.000 0.084 0 0.612
#> DRR006412 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006413 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006414 3 0.2562 0.5956 0.172 0.000 0.828 0.000 0 0.000
#> DRR006415 3 0.3911 0.7375 0.000 0.000 0.624 0.008 0 0.368
#> DRR006416 4 0.5364 0.4757 0.000 0.000 0.172 0.584 0 0.244
#> DRR006417 3 0.5418 0.6624 0.000 0.000 0.508 0.124 0 0.368
#> DRR006418 3 0.3494 0.7398 0.000 0.000 0.736 0.012 0 0.252
#> DRR006419 3 0.3494 0.7398 0.000 0.000 0.736 0.012 0 0.252
#> DRR006420 3 0.4283 0.7429 0.048 0.000 0.696 0.004 0 0.252
#> DRR006421 3 0.5643 0.6248 0.000 0.000 0.476 0.156 0 0.368
#> DRR006422 4 0.0632 0.7944 0.000 0.000 0.000 0.976 0 0.024
#> DRR006423 2 0.0000 0.9045 0.000 1.000 0.000 0.000 0 0.000
#> DRR006424 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006425 6 0.5080 0.7941 0.000 0.288 0.000 0.112 0 0.600
#> DRR006426 3 0.5418 0.6624 0.000 0.000 0.508 0.124 0 0.368
#> DRR006427 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006428 1 0.3175 0.7753 0.744 0.000 0.256 0.000 0 0.000
#> DRR006429 6 0.5167 0.8706 0.000 0.148 0.000 0.240 0 0.612
#> DRR006430 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006431 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006432 3 0.5418 0.6624 0.000 0.000 0.508 0.124 0 0.368
#> DRR006433 4 0.0547 0.7958 0.000 0.000 0.000 0.980 0 0.020
#> DRR006434 2 0.0000 0.9045 0.000 1.000 0.000 0.000 0 0.000
#> DRR006435 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006436 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006437 4 0.6018 0.0982 0.000 0.000 0.332 0.416 0 0.252
#> DRR006438 3 0.2562 0.5956 0.172 0.000 0.828 0.000 0 0.000
#> DRR006439 3 0.4549 0.7329 0.008 0.000 0.596 0.028 0 0.368
#> DRR006440 2 0.0000 0.9045 0.000 1.000 0.000 0.000 0 0.000
#> DRR006441 2 0.0146 0.9026 0.000 0.996 0.000 0.000 0 0.004
#> DRR006442 1 0.3175 0.7753 0.744 0.000 0.256 0.000 0 0.000
#> DRR006443 2 0.0000 0.9045 0.000 1.000 0.000 0.000 0 0.000
#> DRR006444 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006445 3 0.3398 0.7406 0.000 0.000 0.740 0.008 0 0.252
#> DRR006446 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006447 3 0.3398 0.7406 0.000 0.000 0.740 0.008 0 0.252
#> DRR006448 4 0.1003 0.8048 0.000 0.000 0.016 0.964 0 0.020
#> DRR006449 3 0.1327 0.6683 0.064 0.000 0.936 0.000 0 0.000
#> DRR006450 3 0.3075 0.7142 0.040 0.000 0.844 0.008 0 0.108
#> DRR006451 4 0.1003 0.8048 0.000 0.000 0.016 0.964 0 0.020
#> DRR006452 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006453 3 0.3533 0.7442 0.012 0.000 0.748 0.004 0 0.236
#> DRR006454 6 0.5156 0.8665 0.000 0.144 0.000 0.244 0 0.612
#> DRR006455 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006456 3 0.3672 0.7398 0.000 0.000 0.632 0.000 0 0.368
#> DRR006457 3 0.5643 0.6248 0.000 0.000 0.476 0.156 0 0.368
#> DRR006458 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006459 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006460 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006461 2 0.0000 0.9045 0.000 1.000 0.000 0.000 0 0.000
#> DRR006462 3 0.1387 0.6666 0.068 0.000 0.932 0.000 0 0.000
#> DRR006463 2 0.0000 0.9045 0.000 1.000 0.000 0.000 0 0.000
#> DRR006464 6 0.5167 0.8706 0.000 0.148 0.000 0.240 0 0.612
#> DRR006465 3 0.3330 0.4040 0.284 0.000 0.716 0.000 0 0.000
#> DRR006466 4 0.2562 0.6035 0.000 0.000 0.000 0.828 0 0.172
#> DRR006467 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006468 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006469 2 0.0146 0.9026 0.000 0.996 0.000 0.000 0 0.004
#> DRR006470 3 0.4968 0.6997 0.000 0.000 0.556 0.076 0 0.368
#> DRR006471 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006472 4 0.0632 0.7944 0.000 0.000 0.000 0.976 0 0.024
#> DRR006473 2 0.0000 0.9045 0.000 1.000 0.000 0.000 0 0.000
#> DRR006474 2 0.0000 0.9045 0.000 1.000 0.000 0.000 0 0.000
#> DRR006475 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006476 6 0.5167 0.8706 0.000 0.148 0.000 0.240 0 0.612
#> DRR006477 4 0.0547 0.7958 0.000 0.000 0.000 0.980 0 0.020
#> DRR006478 3 0.3330 0.4040 0.284 0.000 0.716 0.000 0 0.000
#> DRR006479 3 0.2562 0.5956 0.172 0.000 0.828 0.000 0 0.000
#> DRR006480 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006481 3 0.5643 0.6248 0.000 0.000 0.476 0.156 0 0.368
#> DRR006482 4 0.6013 0.1076 0.000 0.000 0.328 0.420 0 0.252
#> DRR006483 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006484 3 0.2562 0.5956 0.172 0.000 0.828 0.000 0 0.000
#> DRR006485 2 0.0000 0.9045 0.000 1.000 0.000 0.000 0 0.000
#> DRR006486 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006487 3 0.3672 0.7398 0.000 0.000 0.632 0.000 0 0.368
#> DRR006488 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006489 1 0.0000 0.9132 1.000 0.000 0.000 0.000 0 0.000
#> DRR006490 1 0.3175 0.7753 0.744 0.000 0.256 0.000 0 0.000
#> DRR006491 1 0.3175 0.7753 0.744 0.000 0.256 0.000 0 0.000
#> DRR006492 3 0.1285 0.6702 0.052 0.000 0.944 0.004 0 0.000
#> DRR006493 3 0.3672 0.7398 0.000 0.000 0.632 0.000 0 0.368
#> DRR006494 1 0.0000 0.9132 1.000 0.000 0.000 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)
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 16187 rows and 121 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 1.000 1.000 0.4411 0.560 0.560
#> 3 3 0.672 0.896 0.920 0.4589 0.706 0.508
#> 4 4 0.749 0.663 0.777 0.1410 0.868 0.635
#> 5 5 0.688 0.768 0.813 0.0729 0.900 0.637
#> 6 6 0.758 0.667 0.782 0.0450 0.967 0.840
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
#> DRR006374 2 0.000 1.000 0.000 1.000
#> DRR006375 1 0.000 1.000 1.000 0.000
#> DRR006376 1 0.000 1.000 1.000 0.000
#> DRR006377 1 0.000 1.000 1.000 0.000
#> DRR006378 2 0.000 1.000 0.000 1.000
#> DRR006379 1 0.000 1.000 1.000 0.000
#> DRR006380 2 0.000 1.000 0.000 1.000
#> DRR006381 1 0.000 1.000 1.000 0.000
#> DRR006382 2 0.000 1.000 0.000 1.000
#> DRR006383 1 0.000 1.000 1.000 0.000
#> DRR006384 2 0.000 1.000 0.000 1.000
#> DRR006385 1 0.000 1.000 1.000 0.000
#> DRR006386 2 0.000 1.000 0.000 1.000
#> DRR006387 1 0.000 1.000 1.000 0.000
#> DRR006388 1 0.000 1.000 1.000 0.000
#> DRR006389 1 0.000 1.000 1.000 0.000
#> DRR006390 2 0.000 1.000 0.000 1.000
#> DRR006391 2 0.000 1.000 0.000 1.000
#> DRR006392 1 0.000 1.000 1.000 0.000
#> DRR006393 1 0.000 1.000 1.000 0.000
#> DRR006394 2 0.000 1.000 0.000 1.000
#> DRR006395 1 0.000 1.000 1.000 0.000
#> DRR006396 1 0.000 1.000 1.000 0.000
#> DRR006397 1 0.000 1.000 1.000 0.000
#> DRR006398 1 0.000 1.000 1.000 0.000
#> DRR006399 1 0.000 1.000 1.000 0.000
#> DRR006400 1 0.000 1.000 1.000 0.000
#> DRR006401 2 0.000 1.000 0.000 1.000
#> DRR006402 2 0.000 1.000 0.000 1.000
#> DRR006403 1 0.000 1.000 1.000 0.000
#> DRR006404 1 0.000 1.000 1.000 0.000
#> DRR006405 1 0.000 1.000 1.000 0.000
#> DRR006406 1 0.000 1.000 1.000 0.000
#> DRR006407 2 0.000 1.000 0.000 1.000
#> DRR006408 2 0.000 1.000 0.000 1.000
#> DRR006409 1 0.000 1.000 1.000 0.000
#> DRR006410 1 0.000 1.000 1.000 0.000
#> DRR006411 2 0.000 1.000 0.000 1.000
#> DRR006412 2 0.000 1.000 0.000 1.000
#> DRR006413 1 0.000 1.000 1.000 0.000
#> DRR006414 1 0.000 1.000 1.000 0.000
#> DRR006415 1 0.000 1.000 1.000 0.000
#> DRR006416 1 0.000 1.000 1.000 0.000
#> DRR006417 1 0.000 1.000 1.000 0.000
#> DRR006418 1 0.000 1.000 1.000 0.000
#> DRR006419 1 0.000 1.000 1.000 0.000
#> DRR006420 1 0.000 1.000 1.000 0.000
#> DRR006421 1 0.000 1.000 1.000 0.000
#> DRR006422 1 0.000 1.000 1.000 0.000
#> DRR006423 2 0.000 1.000 0.000 1.000
#> DRR006424 1 0.000 1.000 1.000 0.000
#> DRR006425 2 0.000 1.000 0.000 1.000
#> DRR006426 1 0.000 1.000 1.000 0.000
#> DRR006427 2 0.000 1.000 0.000 1.000
#> DRR006428 1 0.000 1.000 1.000 0.000
#> DRR006429 2 0.000 1.000 0.000 1.000
#> DRR006430 1 0.000 1.000 1.000 0.000
#> DRR006431 1 0.000 1.000 1.000 0.000
#> DRR006432 1 0.000 1.000 1.000 0.000
#> DRR006433 1 0.000 1.000 1.000 0.000
#> DRR006434 2 0.000 1.000 0.000 1.000
#> DRR006435 2 0.000 1.000 0.000 1.000
#> DRR006436 2 0.000 1.000 0.000 1.000
#> DRR006437 1 0.000 1.000 1.000 0.000
#> DRR006438 1 0.000 1.000 1.000 0.000
#> DRR006439 1 0.000 1.000 1.000 0.000
#> DRR006440 2 0.000 1.000 0.000 1.000
#> DRR006441 2 0.000 1.000 0.000 1.000
#> DRR006442 1 0.000 1.000 1.000 0.000
#> DRR006443 2 0.000 1.000 0.000 1.000
#> DRR006444 2 0.000 1.000 0.000 1.000
#> DRR006445 1 0.000 1.000 1.000 0.000
#> DRR006446 2 0.000 1.000 0.000 1.000
#> DRR006447 1 0.000 1.000 1.000 0.000
#> DRR006448 1 0.000 1.000 1.000 0.000
#> DRR006449 1 0.000 1.000 1.000 0.000
#> DRR006450 1 0.000 1.000 1.000 0.000
#> DRR006451 1 0.000 1.000 1.000 0.000
#> DRR006452 1 0.000 1.000 1.000 0.000
#> DRR006453 1 0.000 1.000 1.000 0.000
#> DRR006454 1 0.000 1.000 1.000 0.000
#> DRR006455 2 0.000 1.000 0.000 1.000
#> DRR006456 1 0.000 1.000 1.000 0.000
#> DRR006457 1 0.000 1.000 1.000 0.000
#> DRR006458 1 0.000 1.000 1.000 0.000
#> DRR006459 1 0.000 1.000 1.000 0.000
#> DRR006460 2 0.000 1.000 0.000 1.000
#> DRR006461 2 0.000 1.000 0.000 1.000
#> DRR006462 1 0.000 1.000 1.000 0.000
#> DRR006463 2 0.000 1.000 0.000 1.000
#> DRR006464 2 0.000 1.000 0.000 1.000
#> DRR006465 1 0.000 1.000 1.000 0.000
#> DRR006466 1 0.118 0.984 0.984 0.016
#> DRR006467 1 0.000 1.000 1.000 0.000
#> DRR006468 2 0.000 1.000 0.000 1.000
#> DRR006469 2 0.000 1.000 0.000 1.000
#> DRR006470 1 0.000 1.000 1.000 0.000
#> DRR006471 1 0.000 1.000 1.000 0.000
#> DRR006472 1 0.000 1.000 1.000 0.000
#> DRR006473 2 0.000 1.000 0.000 1.000
#> DRR006474 2 0.000 1.000 0.000 1.000
#> DRR006475 1 0.000 1.000 1.000 0.000
#> DRR006476 2 0.000 1.000 0.000 1.000
#> DRR006477 1 0.000 1.000 1.000 0.000
#> DRR006478 1 0.000 1.000 1.000 0.000
#> DRR006479 1 0.000 1.000 1.000 0.000
#> DRR006480 1 0.000 1.000 1.000 0.000
#> DRR006481 1 0.000 1.000 1.000 0.000
#> DRR006482 1 0.000 1.000 1.000 0.000
#> DRR006483 1 0.000 1.000 1.000 0.000
#> DRR006484 1 0.000 1.000 1.000 0.000
#> DRR006485 2 0.000 1.000 0.000 1.000
#> DRR006486 1 0.000 1.000 1.000 0.000
#> DRR006487 1 0.000 1.000 1.000 0.000
#> DRR006488 2 0.000 1.000 0.000 1.000
#> DRR006489 1 0.000 1.000 1.000 0.000
#> DRR006490 1 0.000 1.000 1.000 0.000
#> DRR006491 1 0.000 1.000 1.000 0.000
#> DRR006492 1 0.000 1.000 1.000 0.000
#> DRR006493 1 0.000 1.000 1.000 0.000
#> DRR006494 1 0.000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.1643 0.944 0.044 0.956 0.000
#> DRR006375 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006376 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006377 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006378 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006379 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006380 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006381 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006382 2 0.8628 0.420 0.116 0.544 0.340
#> DRR006383 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006384 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006385 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006386 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006387 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006388 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006389 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006390 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006391 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006392 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006393 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006394 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006395 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006396 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006397 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006398 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006399 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006400 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006401 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006403 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006404 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006405 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006406 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006407 3 0.3267 0.848 0.116 0.000 0.884
#> DRR006408 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006409 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006410 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006411 3 0.3267 0.848 0.116 0.000 0.884
#> DRR006412 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006413 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006414 1 0.4796 0.847 0.780 0.000 0.220
#> DRR006415 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006416 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006417 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006418 3 0.2625 0.855 0.084 0.000 0.916
#> DRR006419 3 0.0747 0.917 0.016 0.000 0.984
#> DRR006420 1 0.5465 0.745 0.712 0.000 0.288
#> DRR006421 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006422 3 0.2356 0.881 0.072 0.000 0.928
#> DRR006423 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006424 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006425 3 0.3845 0.840 0.116 0.012 0.872
#> DRR006426 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006427 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006428 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006429 3 0.3267 0.848 0.116 0.000 0.884
#> DRR006430 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006431 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006432 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006433 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006434 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006435 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006437 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006438 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006439 3 0.0237 0.926 0.004 0.000 0.996
#> DRR006440 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006441 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006442 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006443 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006444 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006445 3 0.2625 0.855 0.084 0.000 0.916
#> DRR006446 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006447 3 0.2625 0.855 0.084 0.000 0.916
#> DRR006448 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006449 1 0.6180 0.474 0.584 0.000 0.416
#> DRR006450 1 0.6309 0.227 0.504 0.000 0.496
#> DRR006451 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006452 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006453 1 0.3482 0.956 0.872 0.000 0.128
#> DRR006454 3 0.3267 0.848 0.116 0.000 0.884
#> DRR006455 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006456 3 0.2796 0.845 0.092 0.000 0.908
#> DRR006457 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006458 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006459 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006460 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006461 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006462 3 0.6309 -0.234 0.496 0.000 0.504
#> DRR006463 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006464 3 0.3267 0.848 0.116 0.000 0.884
#> DRR006465 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006466 3 0.3267 0.848 0.116 0.000 0.884
#> DRR006467 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006468 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006469 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006470 3 0.0592 0.920 0.012 0.000 0.988
#> DRR006471 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006472 3 0.3267 0.848 0.116 0.000 0.884
#> DRR006473 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006474 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006475 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006476 3 0.3267 0.848 0.116 0.000 0.884
#> DRR006477 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006478 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006479 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006480 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006481 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006482 3 0.0000 0.928 0.000 0.000 1.000
#> DRR006483 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006484 3 0.6168 0.115 0.412 0.000 0.588
#> DRR006485 2 0.3267 0.934 0.116 0.884 0.000
#> DRR006486 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006487 3 0.5291 0.552 0.268 0.000 0.732
#> DRR006488 2 0.0000 0.949 0.000 1.000 0.000
#> DRR006489 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006490 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006491 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006492 1 0.3267 0.967 0.884 0.000 0.116
#> DRR006493 3 0.4702 0.664 0.212 0.000 0.788
#> DRR006494 1 0.3267 0.967 0.884 0.000 0.116
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.1474 0.75107 0.000 0.948 0.052 0.000
#> DRR006375 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006376 4 0.0921 0.64468 0.000 0.000 0.028 0.972
#> DRR006377 4 0.0188 0.64960 0.000 0.000 0.004 0.996
#> DRR006378 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006379 4 0.0469 0.64917 0.000 0.000 0.012 0.988
#> DRR006380 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006381 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006382 2 0.4543 0.22819 0.000 0.676 0.000 0.324
#> DRR006383 3 0.4972 0.80751 0.000 0.000 0.544 0.456
#> DRR006384 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006385 3 0.4967 0.81048 0.000 0.000 0.548 0.452
#> DRR006386 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006387 3 0.6529 0.71922 0.080 0.000 0.532 0.388
#> DRR006388 4 0.1940 0.60056 0.000 0.000 0.076 0.924
#> DRR006389 4 0.1940 0.60056 0.000 0.000 0.076 0.924
#> DRR006390 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006391 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006392 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006393 1 0.4843 0.35323 0.604 0.000 0.396 0.000
#> DRR006394 2 0.0469 0.73267 0.000 0.988 0.000 0.012
#> DRR006395 4 0.4008 0.23465 0.000 0.000 0.244 0.756
#> DRR006396 1 0.4992 0.11711 0.524 0.000 0.476 0.000
#> DRR006397 4 0.1940 0.60056 0.000 0.000 0.076 0.924
#> DRR006398 4 0.1940 0.60056 0.000 0.000 0.076 0.924
#> DRR006399 4 0.0921 0.64468 0.000 0.000 0.028 0.972
#> DRR006400 4 0.0921 0.64468 0.000 0.000 0.028 0.972
#> DRR006401 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006402 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006403 4 0.0921 0.64468 0.000 0.000 0.028 0.972
#> DRR006404 4 0.0336 0.64948 0.000 0.000 0.008 0.992
#> DRR006405 4 0.0921 0.64468 0.000 0.000 0.028 0.972
#> DRR006406 4 0.0921 0.64468 0.000 0.000 0.028 0.972
#> DRR006407 4 0.5132 0.28651 0.000 0.448 0.004 0.548
#> DRR006408 2 0.3569 0.51301 0.000 0.804 0.000 0.196
#> DRR006409 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006410 1 0.0188 0.95725 0.996 0.000 0.004 0.000
#> DRR006411 4 0.4961 0.28870 0.000 0.448 0.000 0.552
#> DRR006412 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006413 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006414 3 0.4967 0.81048 0.000 0.000 0.548 0.452
#> DRR006415 4 0.4981 -0.64675 0.000 0.000 0.464 0.536
#> DRR006416 4 0.4843 -0.42245 0.000 0.000 0.396 0.604
#> DRR006417 4 0.4866 -0.46651 0.000 0.000 0.404 0.596
#> DRR006418 3 0.4967 0.81048 0.000 0.000 0.548 0.452
#> DRR006419 3 0.4972 0.80751 0.000 0.000 0.544 0.456
#> DRR006420 3 0.4967 0.81048 0.000 0.000 0.548 0.452
#> DRR006421 4 0.2216 0.58484 0.000 0.000 0.092 0.908
#> DRR006422 4 0.0000 0.64951 0.000 0.000 0.000 1.000
#> DRR006423 2 0.3266 0.76617 0.000 0.832 0.168 0.000
#> DRR006424 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006425 4 0.4967 0.28116 0.000 0.452 0.000 0.548
#> DRR006426 4 0.3569 0.35940 0.000 0.000 0.196 0.804
#> DRR006427 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006428 1 0.0707 0.94378 0.980 0.000 0.020 0.000
#> DRR006429 4 0.4961 0.28870 0.000 0.448 0.000 0.552
#> DRR006430 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006432 4 0.3074 0.47022 0.000 0.000 0.152 0.848
#> DRR006433 4 0.0188 0.64960 0.000 0.000 0.004 0.996
#> DRR006434 2 0.4888 0.78951 0.000 0.588 0.412 0.000
#> DRR006435 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006436 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006437 3 0.4967 0.81048 0.000 0.000 0.548 0.452
#> DRR006438 3 0.4972 0.05453 0.456 0.000 0.544 0.000
#> DRR006439 3 0.4967 0.81048 0.000 0.000 0.548 0.452
#> DRR006440 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006441 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006442 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006443 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006444 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006445 3 0.4967 0.81048 0.000 0.000 0.548 0.452
#> DRR006446 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006447 3 0.4967 0.81048 0.000 0.000 0.548 0.452
#> DRR006448 4 0.3219 0.43249 0.000 0.000 0.164 0.836
#> DRR006449 3 0.4961 0.80674 0.000 0.000 0.552 0.448
#> DRR006450 3 0.4967 0.81048 0.000 0.000 0.548 0.452
#> DRR006451 4 0.0336 0.64948 0.000 0.000 0.008 0.992
#> DRR006452 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006453 3 0.5132 0.80758 0.004 0.000 0.548 0.448
#> DRR006454 4 0.3801 0.52116 0.000 0.220 0.000 0.780
#> DRR006455 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006456 3 0.4972 0.80751 0.000 0.000 0.544 0.456
#> DRR006457 4 0.4804 -0.37447 0.000 0.000 0.384 0.616
#> DRR006458 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006460 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006461 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006462 3 0.4961 0.80674 0.000 0.000 0.552 0.448
#> DRR006463 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006464 4 0.4961 0.28870 0.000 0.448 0.000 0.552
#> DRR006465 3 0.7083 0.45199 0.328 0.000 0.528 0.144
#> DRR006466 4 0.4933 0.31366 0.000 0.432 0.000 0.568
#> DRR006467 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006468 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006469 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006470 3 0.4972 0.80751 0.000 0.000 0.544 0.456
#> DRR006471 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006472 4 0.0000 0.64951 0.000 0.000 0.000 1.000
#> DRR006473 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006474 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006475 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006476 4 0.4961 0.28870 0.000 0.448 0.000 0.552
#> DRR006477 4 0.0000 0.64951 0.000 0.000 0.000 1.000
#> DRR006478 3 0.6538 0.28377 0.392 0.000 0.528 0.080
#> DRR006479 3 0.4989 0.00118 0.472 0.000 0.528 0.000
#> DRR006480 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006481 4 0.4454 -0.05947 0.000 0.000 0.308 0.692
#> DRR006482 4 0.1940 0.60056 0.000 0.000 0.076 0.924
#> DRR006483 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006484 3 0.4967 0.81048 0.000 0.000 0.548 0.452
#> DRR006485 2 0.0000 0.74189 0.000 1.000 0.000 0.000
#> DRR006486 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006487 3 0.4972 0.80751 0.000 0.000 0.544 0.456
#> DRR006488 2 0.4961 0.79076 0.000 0.552 0.448 0.000
#> DRR006489 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006490 1 0.0000 0.96047 1.000 0.000 0.000 0.000
#> DRR006491 1 0.0817 0.94002 0.976 0.000 0.024 0.000
#> DRR006492 3 0.7281 0.58585 0.196 0.000 0.532 0.272
#> DRR006493 3 0.4972 0.80751 0.000 0.000 0.544 0.456
#> DRR006494 1 0.0000 0.96047 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
#> DRR006374 2 0.2516 0.5999 0.000 0.860 0.000 0.000 0.140
#> DRR006375 1 0.0404 0.9419 0.988 0.000 0.000 0.000 0.012
#> DRR006376 4 0.3551 0.8009 0.000 0.000 0.136 0.820 0.044
#> DRR006377 4 0.3365 0.8019 0.000 0.000 0.120 0.836 0.044
#> DRR006378 2 0.0000 0.7626 0.000 1.000 0.000 0.000 0.000
#> DRR006379 4 0.3506 0.8014 0.000 0.000 0.132 0.824 0.044
#> DRR006380 2 0.0000 0.7626 0.000 1.000 0.000 0.000 0.000
#> DRR006381 1 0.2408 0.8971 0.892 0.000 0.092 0.000 0.016
#> DRR006382 2 0.3628 0.6667 0.000 0.772 0.000 0.216 0.012
#> DRR006383 3 0.4453 0.7162 0.000 0.000 0.724 0.048 0.228
#> DRR006384 5 0.3837 0.9592 0.000 0.308 0.000 0.000 0.692
#> DRR006385 3 0.1740 0.7983 0.000 0.000 0.932 0.012 0.056
#> DRR006386 5 0.5218 0.9596 0.000 0.308 0.000 0.068 0.624
#> DRR006387 3 0.2615 0.7679 0.008 0.000 0.892 0.080 0.020
#> DRR006388 4 0.4854 0.7358 0.000 0.000 0.260 0.680 0.060
#> DRR006389 4 0.4854 0.7358 0.000 0.000 0.260 0.680 0.060
#> DRR006390 5 0.3837 0.9592 0.000 0.308 0.000 0.000 0.692
#> DRR006391 5 0.3837 0.9592 0.000 0.308 0.000 0.000 0.692
#> DRR006392 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006393 3 0.4318 0.4789 0.292 0.000 0.688 0.000 0.020
#> DRR006394 2 0.1670 0.7477 0.000 0.936 0.000 0.052 0.012
#> DRR006395 4 0.4744 0.7268 0.000 0.000 0.252 0.692 0.056
#> DRR006396 3 0.4407 0.5721 0.244 0.000 0.724 0.012 0.020
#> DRR006397 4 0.4854 0.7358 0.000 0.000 0.260 0.680 0.060
#> DRR006398 4 0.4854 0.7358 0.000 0.000 0.260 0.680 0.060
#> DRR006399 4 0.3551 0.8009 0.000 0.000 0.136 0.820 0.044
#> DRR006400 4 0.3551 0.8009 0.000 0.000 0.136 0.820 0.044
#> DRR006401 5 0.3837 0.9592 0.000 0.308 0.000 0.000 0.692
#> DRR006402 5 0.3837 0.9592 0.000 0.308 0.000 0.000 0.692
#> DRR006403 4 0.3551 0.8009 0.000 0.000 0.136 0.820 0.044
#> DRR006404 4 0.3413 0.8015 0.000 0.000 0.124 0.832 0.044
#> DRR006405 4 0.3692 0.8014 0.000 0.000 0.136 0.812 0.052
#> DRR006406 4 0.3692 0.8014 0.000 0.000 0.136 0.812 0.052
#> DRR006407 2 0.5359 0.3371 0.000 0.532 0.000 0.412 0.056
#> DRR006408 2 0.2719 0.7095 0.000 0.852 0.000 0.144 0.004
#> DRR006409 1 0.1549 0.9261 0.944 0.000 0.040 0.000 0.016
#> DRR006410 1 0.3852 0.7664 0.760 0.000 0.220 0.000 0.020
#> DRR006411 4 0.5057 -0.0877 0.000 0.440 0.008 0.532 0.020
#> DRR006412 5 0.3837 0.9592 0.000 0.308 0.000 0.000 0.692
#> DRR006413 1 0.2351 0.8999 0.896 0.000 0.088 0.000 0.016
#> DRR006414 3 0.2179 0.8008 0.000 0.000 0.888 0.000 0.112
#> DRR006415 3 0.5958 0.4212 0.000 0.000 0.592 0.204 0.204
#> DRR006416 4 0.5785 0.4926 0.000 0.000 0.404 0.504 0.092
#> DRR006417 3 0.6368 -0.0343 0.000 0.000 0.488 0.332 0.180
#> DRR006418 3 0.1831 0.7977 0.000 0.000 0.920 0.004 0.076
#> DRR006419 3 0.2806 0.7796 0.000 0.000 0.844 0.004 0.152
#> DRR006420 3 0.1197 0.8102 0.000 0.000 0.952 0.000 0.048
#> DRR006421 4 0.5642 0.6898 0.000 0.000 0.240 0.624 0.136
#> DRR006422 4 0.2873 0.7804 0.000 0.000 0.120 0.860 0.020
#> DRR006423 2 0.2516 0.5999 0.000 0.860 0.000 0.000 0.140
#> DRR006424 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006425 2 0.4387 0.5493 0.000 0.640 0.000 0.348 0.012
#> DRR006426 4 0.6016 0.6108 0.000 0.000 0.312 0.548 0.140
#> DRR006427 5 0.5218 0.9596 0.000 0.308 0.000 0.068 0.624
#> DRR006428 1 0.4307 0.7946 0.772 0.000 0.128 0.000 0.100
#> DRR006429 2 0.4467 0.5490 0.000 0.640 0.000 0.344 0.016
#> DRR006430 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006432 4 0.5834 0.6266 0.000 0.000 0.284 0.584 0.132
#> DRR006433 4 0.3365 0.8019 0.000 0.000 0.120 0.836 0.044
#> DRR006434 2 0.3999 -0.0850 0.000 0.656 0.000 0.000 0.344
#> DRR006435 5 0.5218 0.9596 0.000 0.308 0.000 0.068 0.624
#> DRR006436 5 0.5218 0.9596 0.000 0.308 0.000 0.068 0.624
#> DRR006437 3 0.2592 0.7782 0.000 0.000 0.892 0.052 0.056
#> DRR006438 3 0.4636 0.7156 0.132 0.000 0.744 0.000 0.124
#> DRR006439 3 0.2561 0.7902 0.000 0.000 0.856 0.000 0.144
#> DRR006440 2 0.0000 0.7626 0.000 1.000 0.000 0.000 0.000
#> DRR006441 2 0.0000 0.7626 0.000 1.000 0.000 0.000 0.000
#> DRR006442 1 0.3336 0.8710 0.844 0.000 0.060 0.000 0.096
#> DRR006443 2 0.0000 0.7626 0.000 1.000 0.000 0.000 0.000
#> DRR006444 5 0.5218 0.9596 0.000 0.308 0.000 0.068 0.624
#> DRR006445 3 0.0807 0.8054 0.000 0.000 0.976 0.012 0.012
#> DRR006446 5 0.3837 0.9592 0.000 0.308 0.000 0.000 0.692
#> DRR006447 3 0.1364 0.8022 0.000 0.000 0.952 0.012 0.036
#> DRR006448 4 0.4495 0.7284 0.000 0.000 0.244 0.712 0.044
#> DRR006449 3 0.2233 0.7692 0.000 0.000 0.904 0.080 0.016
#> DRR006450 3 0.0807 0.8054 0.000 0.000 0.976 0.012 0.012
#> DRR006451 4 0.3413 0.8015 0.000 0.000 0.124 0.832 0.044
#> DRR006452 1 0.2408 0.8971 0.892 0.000 0.092 0.000 0.016
#> DRR006453 3 0.0693 0.8037 0.000 0.000 0.980 0.012 0.008
#> DRR006454 4 0.3141 0.6243 0.000 0.152 0.000 0.832 0.016
#> DRR006455 5 0.5218 0.9596 0.000 0.308 0.000 0.068 0.624
#> DRR006456 3 0.3496 0.7665 0.000 0.000 0.788 0.012 0.200
#> DRR006457 4 0.6273 0.5417 0.000 0.000 0.316 0.512 0.172
#> DRR006458 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006460 5 0.3837 0.9592 0.000 0.308 0.000 0.000 0.692
#> DRR006461 2 0.0000 0.7626 0.000 1.000 0.000 0.000 0.000
#> DRR006462 3 0.2017 0.7711 0.000 0.000 0.912 0.080 0.008
#> DRR006463 2 0.0000 0.7626 0.000 1.000 0.000 0.000 0.000
#> DRR006464 2 0.4775 0.5485 0.000 0.640 0.008 0.332 0.020
#> DRR006465 3 0.3314 0.7443 0.124 0.000 0.844 0.012 0.020
#> DRR006466 4 0.3427 0.5675 0.000 0.192 0.000 0.796 0.012
#> DRR006467 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006468 5 0.5218 0.9596 0.000 0.308 0.000 0.068 0.624
#> DRR006469 2 0.0000 0.7626 0.000 1.000 0.000 0.000 0.000
#> DRR006470 3 0.2970 0.7725 0.000 0.000 0.828 0.004 0.168
#> DRR006471 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006472 4 0.3691 0.7639 0.000 0.004 0.164 0.804 0.028
#> DRR006473 2 0.1544 0.7093 0.000 0.932 0.000 0.000 0.068
#> DRR006474 2 0.1478 0.7132 0.000 0.936 0.000 0.000 0.064
#> DRR006475 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006476 2 0.4387 0.5493 0.000 0.640 0.000 0.348 0.012
#> DRR006477 4 0.2280 0.7993 0.000 0.000 0.120 0.880 0.000
#> DRR006478 3 0.3453 0.7346 0.136 0.000 0.832 0.012 0.020
#> DRR006479 3 0.4789 0.6856 0.156 0.000 0.728 0.000 0.116
#> DRR006480 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006481 4 0.6273 0.5417 0.000 0.000 0.316 0.512 0.172
#> DRR006482 4 0.4854 0.7351 0.000 0.000 0.260 0.680 0.060
#> DRR006483 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006484 3 0.3123 0.7770 0.000 0.000 0.812 0.004 0.184
#> DRR006485 2 0.0000 0.7626 0.000 1.000 0.000 0.000 0.000
#> DRR006486 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006487 3 0.3659 0.7571 0.000 0.000 0.768 0.012 0.220
#> DRR006488 5 0.5218 0.9596 0.000 0.308 0.000 0.068 0.624
#> DRR006489 1 0.0000 0.9454 1.000 0.000 0.000 0.000 0.000
#> DRR006490 1 0.2830 0.8940 0.876 0.000 0.044 0.000 0.080
#> DRR006491 1 0.4541 0.7704 0.752 0.000 0.136 0.000 0.112
#> DRR006492 3 0.2502 0.7841 0.060 0.000 0.904 0.012 0.024
#> DRR006493 3 0.3496 0.7665 0.000 0.000 0.788 0.012 0.200
#> DRR006494 1 0.0000 0.9454 1.000 0.000 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
#> DRR006374 2 0.3508 0.6352 0.000 0.704 0.000 0.000 0.292 0.004
#> DRR006375 1 0.1349 0.8785 0.940 0.004 0.000 0.000 0.000 0.056
#> DRR006376 4 0.1267 0.6593 0.000 0.000 0.060 0.940 0.000 0.000
#> DRR006377 4 0.1296 0.6593 0.000 0.004 0.044 0.948 0.000 0.004
#> DRR006378 2 0.1588 0.8215 0.000 0.924 0.000 0.000 0.072 0.004
#> DRR006379 4 0.1075 0.6602 0.000 0.000 0.048 0.952 0.000 0.000
#> DRR006380 2 0.1327 0.8236 0.000 0.936 0.000 0.000 0.064 0.000
#> DRR006381 1 0.4145 0.7425 0.736 0.012 0.208 0.000 0.000 0.044
#> DRR006382 2 0.2450 0.8069 0.000 0.896 0.000 0.040 0.016 0.048
#> DRR006383 6 0.4499 -0.0766 0.000 0.012 0.476 0.012 0.000 0.500
#> DRR006384 5 0.0146 0.9248 0.000 0.000 0.000 0.004 0.996 0.000
#> DRR006385 3 0.1765 0.6912 0.000 0.000 0.904 0.000 0.000 0.096
#> DRR006386 5 0.2340 0.9268 0.000 0.000 0.000 0.000 0.852 0.148
#> DRR006387 3 0.2294 0.6893 0.000 0.000 0.892 0.072 0.000 0.036
#> DRR006388 4 0.5809 0.1902 0.000 0.004 0.176 0.496 0.000 0.324
#> DRR006389 4 0.5809 0.1902 0.000 0.004 0.176 0.496 0.000 0.324
#> DRR006390 5 0.0146 0.9245 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006391 5 0.0146 0.9245 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006392 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006393 3 0.4051 0.5716 0.172 0.012 0.760 0.000 0.000 0.056
#> DRR006394 2 0.2670 0.8156 0.000 0.884 0.000 0.020 0.052 0.044
#> DRR006395 4 0.1806 0.6366 0.000 0.004 0.088 0.908 0.000 0.000
#> DRR006396 3 0.3447 0.6131 0.148 0.004 0.804 0.000 0.000 0.044
#> DRR006397 4 0.5809 0.1902 0.000 0.004 0.176 0.496 0.000 0.324
#> DRR006398 4 0.5809 0.1902 0.000 0.004 0.176 0.496 0.000 0.324
#> DRR006399 4 0.1267 0.6593 0.000 0.000 0.060 0.940 0.000 0.000
#> DRR006400 4 0.1267 0.6593 0.000 0.000 0.060 0.940 0.000 0.000
#> DRR006401 5 0.0146 0.9248 0.000 0.000 0.000 0.004 0.996 0.000
#> DRR006402 5 0.0146 0.9248 0.000 0.000 0.000 0.004 0.996 0.000
#> DRR006403 4 0.1267 0.6593 0.000 0.000 0.060 0.940 0.000 0.000
#> DRR006404 4 0.1007 0.6599 0.000 0.000 0.044 0.956 0.000 0.000
#> DRR006405 4 0.1471 0.6583 0.000 0.004 0.064 0.932 0.000 0.000
#> DRR006406 4 0.1471 0.6583 0.000 0.004 0.064 0.932 0.000 0.000
#> DRR006407 2 0.5373 0.5626 0.000 0.588 0.000 0.216 0.000 0.196
#> DRR006408 2 0.3145 0.8028 0.000 0.856 0.000 0.044 0.032 0.068
#> DRR006409 1 0.3278 0.8409 0.848 0.032 0.064 0.000 0.000 0.056
#> DRR006410 1 0.5123 0.5360 0.580 0.016 0.344 0.000 0.000 0.060
#> DRR006411 2 0.6034 0.1894 0.000 0.400 0.000 0.252 0.000 0.348
#> DRR006412 5 0.0291 0.9223 0.000 0.004 0.000 0.000 0.992 0.004
#> DRR006413 1 0.4145 0.7425 0.736 0.012 0.208 0.000 0.000 0.044
#> DRR006414 3 0.3388 0.6570 0.000 0.036 0.792 0.000 0.000 0.172
#> DRR006415 6 0.4976 0.4642 0.000 0.012 0.340 0.056 0.000 0.592
#> DRR006416 4 0.5991 -0.0607 0.000 0.000 0.256 0.436 0.000 0.308
#> DRR006417 6 0.5406 0.6213 0.000 0.004 0.276 0.140 0.000 0.580
#> DRR006418 3 0.2340 0.6680 0.000 0.000 0.852 0.000 0.000 0.148
#> DRR006419 3 0.3428 0.4969 0.000 0.000 0.696 0.000 0.000 0.304
#> DRR006420 3 0.1411 0.7220 0.000 0.004 0.936 0.000 0.000 0.060
#> DRR006421 4 0.5598 0.0577 0.000 0.008 0.140 0.552 0.000 0.300
#> DRR006422 4 0.4586 0.3163 0.000 0.004 0.032 0.564 0.000 0.400
#> DRR006423 2 0.3652 0.6074 0.000 0.672 0.000 0.000 0.324 0.004
#> DRR006424 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006425 2 0.4220 0.7194 0.000 0.732 0.000 0.096 0.000 0.172
#> DRR006426 6 0.5875 0.4345 0.000 0.008 0.172 0.320 0.000 0.500
#> DRR006427 5 0.2340 0.9268 0.000 0.000 0.000 0.000 0.852 0.148
#> DRR006428 1 0.5324 0.7039 0.672 0.040 0.148 0.000 0.000 0.140
#> DRR006429 2 0.4874 0.6446 0.000 0.640 0.000 0.108 0.000 0.252
#> DRR006430 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006432 6 0.5500 0.3837 0.000 0.004 0.128 0.332 0.000 0.536
#> DRR006433 4 0.1777 0.6547 0.000 0.004 0.044 0.928 0.000 0.024
#> DRR006434 2 0.3961 0.3647 0.000 0.556 0.000 0.000 0.440 0.004
#> DRR006435 5 0.2340 0.9268 0.000 0.000 0.000 0.000 0.852 0.148
#> DRR006436 5 0.2340 0.9268 0.000 0.000 0.000 0.000 0.852 0.148
#> DRR006437 3 0.2432 0.6768 0.000 0.000 0.876 0.024 0.000 0.100
#> DRR006438 3 0.4709 0.6255 0.052 0.040 0.712 0.000 0.000 0.196
#> DRR006439 3 0.3547 0.5106 0.000 0.004 0.696 0.000 0.000 0.300
#> DRR006440 2 0.1471 0.8239 0.000 0.932 0.000 0.000 0.064 0.004
#> DRR006441 2 0.1584 0.8238 0.000 0.928 0.000 0.008 0.064 0.000
#> DRR006442 1 0.4778 0.7675 0.728 0.040 0.096 0.000 0.000 0.136
#> DRR006443 2 0.1471 0.8239 0.000 0.932 0.000 0.000 0.064 0.004
#> DRR006444 5 0.2340 0.9268 0.000 0.000 0.000 0.000 0.852 0.148
#> DRR006445 3 0.1007 0.7223 0.000 0.000 0.956 0.000 0.000 0.044
#> DRR006446 5 0.0146 0.9245 0.000 0.000 0.000 0.000 0.996 0.004
#> DRR006447 3 0.1444 0.7088 0.000 0.000 0.928 0.000 0.000 0.072
#> DRR006448 4 0.1814 0.6242 0.000 0.000 0.100 0.900 0.000 0.000
#> DRR006449 3 0.1895 0.6964 0.000 0.000 0.912 0.072 0.000 0.016
#> DRR006450 3 0.1007 0.7223 0.000 0.000 0.956 0.000 0.000 0.044
#> DRR006451 4 0.1007 0.6599 0.000 0.000 0.044 0.956 0.000 0.000
#> DRR006452 1 0.4145 0.7425 0.736 0.012 0.208 0.000 0.000 0.044
#> DRR006453 3 0.0260 0.7259 0.000 0.000 0.992 0.000 0.000 0.008
#> DRR006454 4 0.5368 0.2466 0.000 0.112 0.000 0.488 0.000 0.400
#> DRR006455 5 0.2340 0.9268 0.000 0.000 0.000 0.000 0.852 0.148
#> DRR006456 3 0.4141 0.3757 0.000 0.016 0.596 0.000 0.000 0.388
#> DRR006457 6 0.6027 0.4933 0.000 0.008 0.184 0.372 0.000 0.436
#> DRR006458 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006460 5 0.0291 0.9231 0.000 0.004 0.000 0.004 0.992 0.000
#> DRR006461 2 0.1471 0.8239 0.000 0.932 0.000 0.000 0.064 0.004
#> DRR006462 3 0.1444 0.6994 0.000 0.000 0.928 0.072 0.000 0.000
#> DRR006463 2 0.1471 0.8239 0.000 0.932 0.000 0.000 0.064 0.004
#> DRR006464 2 0.5119 0.5759 0.000 0.584 0.000 0.108 0.000 0.308
#> DRR006465 3 0.2493 0.6875 0.076 0.004 0.884 0.000 0.000 0.036
#> DRR006466 4 0.5925 0.1965 0.000 0.224 0.000 0.444 0.000 0.332
#> DRR006467 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006468 5 0.2340 0.9268 0.000 0.000 0.000 0.000 0.852 0.148
#> DRR006469 2 0.1584 0.8238 0.000 0.928 0.000 0.008 0.064 0.000
#> DRR006470 3 0.3706 0.3686 0.000 0.000 0.620 0.000 0.000 0.380
#> DRR006471 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006472 4 0.4928 0.1811 0.000 0.016 0.032 0.480 0.000 0.472
#> DRR006473 2 0.2772 0.7623 0.000 0.816 0.000 0.000 0.180 0.004
#> DRR006474 2 0.2632 0.7731 0.000 0.832 0.000 0.000 0.164 0.004
#> DRR006475 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006476 2 0.4787 0.6597 0.000 0.656 0.000 0.108 0.000 0.236
#> DRR006477 4 0.3121 0.6037 0.000 0.004 0.044 0.836 0.000 0.116
#> DRR006478 3 0.2493 0.6875 0.076 0.004 0.884 0.000 0.000 0.036
#> DRR006479 3 0.4758 0.6260 0.064 0.040 0.716 0.000 0.000 0.180
#> DRR006480 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006481 6 0.6027 0.4933 0.000 0.008 0.184 0.372 0.000 0.436
#> DRR006482 4 0.5809 0.1902 0.000 0.004 0.176 0.496 0.000 0.324
#> DRR006483 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006484 3 0.4319 0.4305 0.000 0.032 0.620 0.000 0.000 0.348
#> DRR006485 2 0.1471 0.8239 0.000 0.932 0.000 0.000 0.064 0.004
#> DRR006486 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006487 3 0.4184 0.3354 0.000 0.016 0.576 0.000 0.000 0.408
#> DRR006488 5 0.2340 0.9268 0.000 0.000 0.000 0.000 0.852 0.148
#> DRR006489 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006490 1 0.4528 0.7850 0.752 0.040 0.088 0.000 0.000 0.120
#> DRR006491 1 0.5522 0.6754 0.648 0.040 0.152 0.000 0.000 0.160
#> DRR006492 3 0.1464 0.7180 0.016 0.004 0.944 0.000 0.000 0.036
#> DRR006493 3 0.4141 0.3757 0.000 0.016 0.596 0.000 0.000 0.388
#> DRR006494 1 0.0000 0.8956 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.994 0.997 0.4640 0.538 0.538
#> 3 3 1.000 0.984 0.994 0.2849 0.870 0.758
#> 4 4 0.850 0.784 0.889 0.1511 0.912 0.788
#> 5 5 0.928 0.939 0.970 0.0509 0.917 0.763
#> 6 6 0.933 0.847 0.937 0.0297 0.986 0.949
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 5
There is also optional best \(k\) = 2 3 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
#> DRR006374 2 0.000 1.000 0.000 1.000
#> DRR006375 1 0.000 0.996 1.000 0.000
#> DRR006376 1 0.000 0.996 1.000 0.000
#> DRR006377 1 0.000 0.996 1.000 0.000
#> DRR006378 2 0.000 1.000 0.000 1.000
#> DRR006379 1 0.000 0.996 1.000 0.000
#> DRR006380 2 0.000 1.000 0.000 1.000
#> DRR006381 1 0.000 0.996 1.000 0.000
#> DRR006382 2 0.000 1.000 0.000 1.000
#> DRR006383 1 0.000 0.996 1.000 0.000
#> DRR006384 2 0.000 1.000 0.000 1.000
#> DRR006385 1 0.000 0.996 1.000 0.000
#> DRR006386 2 0.000 1.000 0.000 1.000
#> DRR006387 1 0.000 0.996 1.000 0.000
#> DRR006388 1 0.000 0.996 1.000 0.000
#> DRR006389 1 0.000 0.996 1.000 0.000
#> DRR006390 2 0.000 1.000 0.000 1.000
#> DRR006391 2 0.000 1.000 0.000 1.000
#> DRR006392 1 0.000 0.996 1.000 0.000
#> DRR006393 1 0.000 0.996 1.000 0.000
#> DRR006394 2 0.000 1.000 0.000 1.000
#> DRR006395 1 0.000 0.996 1.000 0.000
#> DRR006396 1 0.000 0.996 1.000 0.000
#> DRR006397 1 0.000 0.996 1.000 0.000
#> DRR006398 1 0.000 0.996 1.000 0.000
#> DRR006399 1 0.000 0.996 1.000 0.000
#> DRR006400 1 0.000 0.996 1.000 0.000
#> DRR006401 2 0.000 1.000 0.000 1.000
#> DRR006402 2 0.000 1.000 0.000 1.000
#> DRR006403 1 0.000 0.996 1.000 0.000
#> DRR006404 1 0.000 0.996 1.000 0.000
#> DRR006405 1 0.000 0.996 1.000 0.000
#> DRR006406 1 0.000 0.996 1.000 0.000
#> DRR006407 2 0.000 1.000 0.000 1.000
#> DRR006408 2 0.000 1.000 0.000 1.000
#> DRR006409 1 0.000 0.996 1.000 0.000
#> DRR006410 1 0.000 0.996 1.000 0.000
#> DRR006411 2 0.000 1.000 0.000 1.000
#> DRR006412 2 0.000 1.000 0.000 1.000
#> DRR006413 1 0.000 0.996 1.000 0.000
#> DRR006414 1 0.000 0.996 1.000 0.000
#> DRR006415 1 0.000 0.996 1.000 0.000
#> DRR006416 1 0.000 0.996 1.000 0.000
#> DRR006417 1 0.000 0.996 1.000 0.000
#> DRR006418 1 0.000 0.996 1.000 0.000
#> DRR006419 1 0.000 0.996 1.000 0.000
#> DRR006420 1 0.000 0.996 1.000 0.000
#> DRR006421 1 0.000 0.996 1.000 0.000
#> DRR006422 2 0.000 1.000 0.000 1.000
#> DRR006423 2 0.000 1.000 0.000 1.000
#> DRR006424 1 0.000 0.996 1.000 0.000
#> DRR006425 2 0.000 1.000 0.000 1.000
#> DRR006426 1 0.000 0.996 1.000 0.000
#> DRR006427 2 0.000 1.000 0.000 1.000
#> DRR006428 1 0.000 0.996 1.000 0.000
#> DRR006429 2 0.000 1.000 0.000 1.000
#> DRR006430 1 0.000 0.996 1.000 0.000
#> DRR006431 1 0.000 0.996 1.000 0.000
#> DRR006432 1 0.745 0.732 0.788 0.212
#> DRR006433 1 0.000 0.996 1.000 0.000
#> DRR006434 2 0.000 1.000 0.000 1.000
#> DRR006435 2 0.000 1.000 0.000 1.000
#> DRR006436 2 0.000 1.000 0.000 1.000
#> DRR006437 1 0.000 0.996 1.000 0.000
#> DRR006438 1 0.000 0.996 1.000 0.000
#> DRR006439 1 0.000 0.996 1.000 0.000
#> DRR006440 2 0.000 1.000 0.000 1.000
#> DRR006441 2 0.000 1.000 0.000 1.000
#> DRR006442 1 0.000 0.996 1.000 0.000
#> DRR006443 2 0.000 1.000 0.000 1.000
#> DRR006444 2 0.000 1.000 0.000 1.000
#> DRR006445 1 0.000 0.996 1.000 0.000
#> DRR006446 2 0.000 1.000 0.000 1.000
#> DRR006447 1 0.000 0.996 1.000 0.000
#> DRR006448 1 0.000 0.996 1.000 0.000
#> DRR006449 1 0.000 0.996 1.000 0.000
#> DRR006450 1 0.000 0.996 1.000 0.000
#> DRR006451 1 0.295 0.944 0.948 0.052
#> DRR006452 1 0.000 0.996 1.000 0.000
#> DRR006453 1 0.000 0.996 1.000 0.000
#> DRR006454 2 0.000 1.000 0.000 1.000
#> DRR006455 2 0.000 1.000 0.000 1.000
#> DRR006456 1 0.000 0.996 1.000 0.000
#> DRR006457 1 0.000 0.996 1.000 0.000
#> DRR006458 1 0.000 0.996 1.000 0.000
#> DRR006459 1 0.000 0.996 1.000 0.000
#> DRR006460 2 0.000 1.000 0.000 1.000
#> DRR006461 2 0.000 1.000 0.000 1.000
#> DRR006462 1 0.000 0.996 1.000 0.000
#> DRR006463 2 0.000 1.000 0.000 1.000
#> DRR006464 2 0.000 1.000 0.000 1.000
#> DRR006465 1 0.000 0.996 1.000 0.000
#> DRR006466 2 0.000 1.000 0.000 1.000
#> DRR006467 1 0.000 0.996 1.000 0.000
#> DRR006468 2 0.000 1.000 0.000 1.000
#> DRR006469 2 0.000 1.000 0.000 1.000
#> DRR006470 1 0.000 0.996 1.000 0.000
#> DRR006471 1 0.000 0.996 1.000 0.000
#> DRR006472 2 0.000 1.000 0.000 1.000
#> DRR006473 2 0.000 1.000 0.000 1.000
#> DRR006474 2 0.000 1.000 0.000 1.000
#> DRR006475 1 0.000 0.996 1.000 0.000
#> DRR006476 2 0.000 1.000 0.000 1.000
#> DRR006477 1 0.260 0.953 0.956 0.044
#> DRR006478 1 0.000 0.996 1.000 0.000
#> DRR006479 1 0.000 0.996 1.000 0.000
#> DRR006480 1 0.000 0.996 1.000 0.000
#> DRR006481 1 0.000 0.996 1.000 0.000
#> DRR006482 1 0.000 0.996 1.000 0.000
#> DRR006483 1 0.000 0.996 1.000 0.000
#> DRR006484 1 0.000 0.996 1.000 0.000
#> DRR006485 2 0.000 1.000 0.000 1.000
#> DRR006486 1 0.000 0.996 1.000 0.000
#> DRR006487 1 0.000 0.996 1.000 0.000
#> DRR006488 2 0.000 1.000 0.000 1.000
#> DRR006489 1 0.000 0.996 1.000 0.000
#> DRR006490 1 0.000 0.996 1.000 0.000
#> DRR006491 1 0.000 0.996 1.000 0.000
#> DRR006492 1 0.000 0.996 1.000 0.000
#> DRR006493 1 0.000 0.996 1.000 0.000
#> DRR006494 1 0.000 0.996 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006375 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006376 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006377 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006378 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006379 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006380 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006381 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006382 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006383 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006384 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006385 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006386 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006387 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006388 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006389 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006390 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006391 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006392 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006393 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006394 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006395 3 0.0592 0.9868 0.012 0.000 0.988
#> DRR006396 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006397 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006398 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006399 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006400 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006401 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006402 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006403 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006404 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006405 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006406 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006407 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006408 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006409 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006410 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006411 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006412 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006413 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006414 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006415 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006416 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006417 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006418 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006419 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006420 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006421 3 0.0747 0.9824 0.016 0.000 0.984
#> DRR006422 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006423 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006424 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006425 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006426 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006427 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006428 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006429 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006430 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006431 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006432 1 0.4121 0.7728 0.832 0.168 0.000
#> DRR006433 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006434 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006435 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006436 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006437 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006438 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006439 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006440 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006441 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006442 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006443 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006444 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006445 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006446 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006447 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006448 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006449 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006450 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006451 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006452 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006453 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006454 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006455 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006456 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006457 1 0.1031 0.9652 0.976 0.000 0.024
#> DRR006458 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006459 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006460 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006461 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006462 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006463 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006464 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006465 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006466 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006467 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006468 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006469 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006470 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006471 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006472 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006473 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006474 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006475 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006476 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006477 3 0.0000 0.9977 0.000 0.000 1.000
#> DRR006478 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006479 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006480 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006481 1 0.6308 0.0361 0.508 0.000 0.492
#> DRR006482 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006483 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006484 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006485 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006486 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006487 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006488 2 0.0000 1.0000 0.000 1.000 0.000
#> DRR006489 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006490 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006491 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006492 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006493 1 0.0000 0.9884 1.000 0.000 0.000
#> DRR006494 1 0.0000 0.9884 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006375 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006376 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006377 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006378 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006379 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006380 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006381 1 0.4605 0.722 0.664 0.000 0.336 0.000
#> DRR006382 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006383 1 0.0592 0.562 0.984 0.000 0.016 0.000
#> DRR006384 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006385 3 0.3688 0.590 0.208 0.000 0.792 0.000
#> DRR006386 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006387 1 0.4605 0.722 0.664 0.000 0.336 0.000
#> DRR006388 3 0.0592 0.744 0.016 0.000 0.984 0.000
#> DRR006389 3 0.0592 0.744 0.016 0.000 0.984 0.000
#> DRR006390 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006391 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006392 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006393 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006394 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006395 4 0.2345 0.825 0.100 0.000 0.000 0.900
#> DRR006396 1 0.4605 0.722 0.664 0.000 0.336 0.000
#> DRR006397 3 0.0592 0.744 0.016 0.000 0.984 0.000
#> DRR006398 3 0.0592 0.744 0.016 0.000 0.984 0.000
#> DRR006399 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006400 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006401 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006402 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006403 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006404 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006405 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006406 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006407 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006408 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006409 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006410 1 0.4564 0.729 0.672 0.000 0.328 0.000
#> DRR006411 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006412 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006413 1 0.4605 0.722 0.664 0.000 0.336 0.000
#> DRR006414 1 0.0000 0.576 1.000 0.000 0.000 0.000
#> DRR006415 1 0.4948 -0.358 0.560 0.000 0.440 0.000
#> DRR006416 1 0.4605 0.722 0.664 0.000 0.336 0.000
#> DRR006417 3 0.4643 0.428 0.344 0.000 0.656 0.000
#> DRR006418 3 0.3975 0.540 0.240 0.000 0.760 0.000
#> DRR006419 1 0.4961 -0.387 0.552 0.000 0.448 0.000
#> DRR006420 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006421 1 0.3743 0.341 0.824 0.000 0.016 0.160
#> DRR006422 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006423 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006424 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006425 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006426 1 0.4916 -0.331 0.576 0.000 0.424 0.000
#> DRR006427 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006428 1 0.0000 0.576 1.000 0.000 0.000 0.000
#> DRR006429 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006430 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006431 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006432 3 0.4917 0.422 0.336 0.008 0.656 0.000
#> DRR006433 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006434 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006435 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006436 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006437 3 0.4040 0.514 0.248 0.000 0.752 0.000
#> DRR006438 1 0.0000 0.576 1.000 0.000 0.000 0.000
#> DRR006439 1 0.0000 0.576 1.000 0.000 0.000 0.000
#> DRR006440 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006441 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006442 1 0.0000 0.576 1.000 0.000 0.000 0.000
#> DRR006443 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006444 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006445 1 0.4624 0.718 0.660 0.000 0.340 0.000
#> DRR006446 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006447 3 0.3942 0.545 0.236 0.000 0.764 0.000
#> DRR006448 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006449 1 0.4605 0.722 0.664 0.000 0.336 0.000
#> DRR006450 1 0.4713 0.693 0.640 0.000 0.360 0.000
#> DRR006451 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006452 1 0.4605 0.722 0.664 0.000 0.336 0.000
#> DRR006453 1 0.4564 0.729 0.672 0.000 0.328 0.000
#> DRR006454 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006455 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006456 1 0.0592 0.562 0.984 0.000 0.016 0.000
#> DRR006457 1 0.0592 0.562 0.984 0.000 0.016 0.000
#> DRR006458 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006459 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006460 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006461 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006462 1 0.4605 0.722 0.664 0.000 0.336 0.000
#> DRR006463 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006464 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006465 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006466 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006467 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006468 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006469 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006470 1 0.4967 -0.373 0.548 0.000 0.452 0.000
#> DRR006471 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006472 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006473 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006474 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006475 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006476 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006477 4 0.0000 0.988 0.000 0.000 0.000 1.000
#> DRR006478 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006479 1 0.0188 0.577 0.996 0.000 0.004 0.000
#> DRR006480 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006481 1 0.2987 0.430 0.880 0.000 0.016 0.104
#> DRR006482 3 0.0817 0.741 0.024 0.000 0.976 0.000
#> DRR006483 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006484 1 0.0592 0.562 0.984 0.000 0.016 0.000
#> DRR006485 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006486 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006487 1 0.0592 0.562 0.984 0.000 0.016 0.000
#> DRR006488 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> DRR006489 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006490 1 0.0000 0.576 1.000 0.000 0.000 0.000
#> DRR006491 1 0.0000 0.576 1.000 0.000 0.000 0.000
#> DRR006492 1 0.4500 0.737 0.684 0.000 0.316 0.000
#> DRR006493 1 0.0592 0.562 0.984 0.000 0.016 0.000
#> DRR006494 1 0.4500 0.737 0.684 0.000 0.316 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006375 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006376 4 0.0000 0.971 0.000 0 0.000 1.000 0.000
#> DRR006377 4 0.1544 0.920 0.000 0 0.068 0.932 0.000
#> DRR006378 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006379 4 0.0000 0.971 0.000 0 0.000 1.000 0.000
#> DRR006380 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006381 1 0.0162 0.942 0.996 0 0.004 0.000 0.000
#> DRR006382 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006383 3 0.2583 0.838 0.132 0 0.864 0.000 0.004
#> DRR006384 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006385 5 0.2389 0.827 0.116 0 0.004 0.000 0.880
#> DRR006386 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006387 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006388 5 0.0162 0.928 0.004 0 0.000 0.000 0.996
#> DRR006389 5 0.0162 0.928 0.004 0 0.000 0.000 0.996
#> DRR006390 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006391 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006392 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006393 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006394 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006395 4 0.2605 0.729 0.148 0 0.000 0.852 0.000
#> DRR006396 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006397 5 0.0162 0.928 0.004 0 0.000 0.000 0.996
#> DRR006398 5 0.0162 0.928 0.004 0 0.000 0.000 0.996
#> DRR006399 4 0.0000 0.971 0.000 0 0.000 1.000 0.000
#> DRR006400 4 0.0000 0.971 0.000 0 0.000 1.000 0.000
#> DRR006401 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006402 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006403 4 0.0000 0.971 0.000 0 0.000 1.000 0.000
#> DRR006404 4 0.0000 0.971 0.000 0 0.000 1.000 0.000
#> DRR006405 4 0.0000 0.971 0.000 0 0.000 1.000 0.000
#> DRR006406 4 0.0000 0.971 0.000 0 0.000 1.000 0.000
#> DRR006407 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006408 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006409 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006410 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006411 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006412 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006413 1 0.0162 0.942 0.996 0 0.004 0.000 0.000
#> DRR006414 1 0.3398 0.755 0.780 0 0.216 0.000 0.004
#> DRR006415 3 0.1956 0.896 0.076 0 0.916 0.000 0.008
#> DRR006416 1 0.0162 0.942 0.996 0 0.004 0.000 0.000
#> DRR006417 3 0.1270 0.874 0.000 0 0.948 0.000 0.052
#> DRR006418 1 0.1282 0.912 0.952 0 0.004 0.000 0.044
#> DRR006419 1 0.4990 0.480 0.628 0 0.324 0.000 0.048
#> DRR006420 1 0.0162 0.942 0.996 0 0.004 0.000 0.000
#> DRR006421 3 0.1195 0.898 0.028 0 0.960 0.012 0.000
#> DRR006422 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006423 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006424 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006425 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006426 3 0.1557 0.879 0.008 0 0.940 0.000 0.052
#> DRR006427 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006428 1 0.3143 0.770 0.796 0 0.204 0.000 0.000
#> DRR006429 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006430 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006432 3 0.1544 0.866 0.000 0 0.932 0.000 0.068
#> DRR006433 4 0.0609 0.959 0.000 0 0.020 0.980 0.000
#> DRR006434 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006435 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006436 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006437 5 0.2674 0.798 0.140 0 0.004 0.000 0.856
#> DRR006438 1 0.3143 0.770 0.796 0 0.204 0.000 0.000
#> DRR006439 1 0.3210 0.762 0.788 0 0.212 0.000 0.000
#> DRR006440 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006441 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006442 1 0.3143 0.770 0.796 0 0.204 0.000 0.000
#> DRR006443 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006444 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006445 1 0.0162 0.942 0.996 0 0.004 0.000 0.000
#> DRR006446 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006447 1 0.1357 0.909 0.948 0 0.004 0.000 0.048
#> DRR006448 4 0.0000 0.971 0.000 0 0.000 1.000 0.000
#> DRR006449 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006450 1 0.0162 0.942 0.996 0 0.004 0.000 0.000
#> DRR006451 4 0.0000 0.971 0.000 0 0.000 1.000 0.000
#> DRR006452 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006453 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006454 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006455 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006456 3 0.2179 0.887 0.100 0 0.896 0.000 0.004
#> DRR006457 3 0.0404 0.893 0.012 0 0.988 0.000 0.000
#> DRR006458 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006460 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006461 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006462 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006463 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006464 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006465 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006466 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006467 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006468 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006469 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006470 3 0.1670 0.880 0.012 0 0.936 0.000 0.052
#> DRR006471 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006472 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006473 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006474 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006475 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006476 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006477 4 0.1041 0.944 0.000 0 0.032 0.964 0.004
#> DRR006478 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006479 1 0.3143 0.770 0.796 0 0.204 0.000 0.000
#> DRR006480 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006481 3 0.0162 0.887 0.004 0 0.996 0.000 0.000
#> DRR006482 5 0.0451 0.923 0.004 0 0.008 0.000 0.988
#> DRR006483 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006484 3 0.2020 0.887 0.100 0 0.900 0.000 0.000
#> DRR006485 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006486 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006487 3 0.2068 0.891 0.092 0 0.904 0.000 0.004
#> DRR006488 2 0.0000 1.000 0.000 1 0.000 0.000 0.000
#> DRR006489 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006490 1 0.3143 0.770 0.796 0 0.204 0.000 0.000
#> DRR006491 1 0.3210 0.762 0.788 0 0.212 0.000 0.000
#> DRR006492 1 0.0000 0.944 1.000 0 0.000 0.000 0.000
#> DRR006493 3 0.2179 0.887 0.100 0 0.896 0.000 0.004
#> DRR006494 1 0.0000 0.944 1.000 0 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
#> DRR006374 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006375 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006376 4 0.0000 0.9476 0.000 0.000 0.000 1.000 0.000 0.000
#> DRR006377 4 0.1219 0.9164 0.000 0.000 0.004 0.948 0.048 0.000
#> DRR006378 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006379 4 0.0000 0.9476 0.000 0.000 0.000 1.000 0.000 0.000
#> DRR006380 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006381 1 0.1370 0.8525 0.948 0.000 0.012 0.000 0.036 0.004
#> DRR006382 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006383 3 0.1657 0.8236 0.056 0.000 0.928 0.000 0.016 0.000
#> DRR006384 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006385 6 0.3150 0.7676 0.112 0.000 0.012 0.000 0.036 0.840
#> DRR006386 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006387 1 0.0291 0.8734 0.992 0.000 0.000 0.000 0.004 0.004
#> DRR006388 6 0.0146 0.8968 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006389 6 0.0146 0.8968 0.000 0.000 0.000 0.000 0.004 0.996
#> DRR006390 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006391 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006392 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006393 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006394 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006395 4 0.3136 0.5532 0.228 0.000 0.000 0.768 0.004 0.000
#> DRR006396 1 0.1003 0.8610 0.964 0.000 0.004 0.000 0.028 0.004
#> DRR006397 6 0.0260 0.8962 0.000 0.000 0.000 0.000 0.008 0.992
#> DRR006398 6 0.0260 0.8962 0.000 0.000 0.000 0.000 0.008 0.992
#> DRR006399 4 0.0000 0.9476 0.000 0.000 0.000 1.000 0.000 0.000
#> DRR006400 4 0.0000 0.9476 0.000 0.000 0.000 1.000 0.000 0.000
#> DRR006401 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006402 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006403 4 0.0000 0.9476 0.000 0.000 0.000 1.000 0.000 0.000
#> DRR006404 4 0.0000 0.9476 0.000 0.000 0.000 1.000 0.000 0.000
#> DRR006405 4 0.0146 0.9461 0.000 0.000 0.000 0.996 0.004 0.000
#> DRR006406 4 0.0146 0.9461 0.000 0.000 0.000 0.996 0.004 0.000
#> DRR006407 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006408 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006409 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006410 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006411 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006412 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006413 1 0.1370 0.8525 0.948 0.000 0.012 0.000 0.036 0.004
#> DRR006414 1 0.3986 0.2677 0.532 0.000 0.464 0.000 0.004 0.000
#> DRR006415 3 0.3342 0.6332 0.012 0.000 0.760 0.000 0.228 0.000
#> DRR006416 1 0.1370 0.8525 0.948 0.000 0.012 0.000 0.036 0.004
#> DRR006417 5 0.1625 0.7559 0.000 0.000 0.060 0.000 0.928 0.012
#> DRR006418 1 0.4313 0.0136 0.504 0.000 0.012 0.000 0.480 0.004
#> DRR006419 5 0.4767 -0.0340 0.444 0.000 0.040 0.000 0.512 0.004
#> DRR006420 1 0.0146 0.8743 0.996 0.000 0.004 0.000 0.000 0.000
#> DRR006421 3 0.1334 0.8446 0.020 0.000 0.948 0.000 0.032 0.000
#> DRR006422 2 0.0260 0.9853 0.000 0.992 0.008 0.000 0.000 0.000
#> DRR006423 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006424 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006425 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006426 5 0.1625 0.7559 0.000 0.000 0.060 0.000 0.928 0.012
#> DRR006427 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006428 1 0.3810 0.3502 0.572 0.000 0.428 0.000 0.000 0.000
#> DRR006429 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006430 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006432 5 0.1625 0.7559 0.000 0.000 0.060 0.000 0.928 0.012
#> DRR006433 4 0.3210 0.7743 0.000 0.000 0.152 0.812 0.036 0.000
#> DRR006434 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006435 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006436 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006437 6 0.3150 0.7676 0.112 0.000 0.012 0.000 0.036 0.840
#> DRR006438 1 0.3672 0.4710 0.632 0.000 0.368 0.000 0.000 0.000
#> DRR006439 1 0.3867 0.1907 0.512 0.000 0.488 0.000 0.000 0.000
#> DRR006440 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006441 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006442 1 0.3782 0.3849 0.588 0.000 0.412 0.000 0.000 0.000
#> DRR006443 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006444 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006445 1 0.1370 0.8525 0.948 0.000 0.012 0.000 0.036 0.004
#> DRR006446 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006447 1 0.1483 0.8501 0.944 0.000 0.012 0.000 0.036 0.008
#> DRR006448 4 0.0000 0.9476 0.000 0.000 0.000 1.000 0.000 0.000
#> DRR006449 1 0.1080 0.8589 0.960 0.000 0.004 0.000 0.032 0.004
#> DRR006450 1 0.1370 0.8525 0.948 0.000 0.012 0.000 0.036 0.004
#> DRR006451 4 0.0000 0.9476 0.000 0.000 0.000 1.000 0.000 0.000
#> DRR006452 1 0.1370 0.8525 0.948 0.000 0.012 0.000 0.036 0.004
#> DRR006453 1 0.0146 0.8748 0.996 0.000 0.000 0.000 0.004 0.000
#> DRR006454 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006455 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006456 3 0.0790 0.8498 0.032 0.000 0.968 0.000 0.000 0.000
#> DRR006457 3 0.1225 0.8411 0.012 0.000 0.952 0.000 0.036 0.000
#> DRR006458 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006460 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006461 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006462 1 0.1003 0.8610 0.964 0.000 0.004 0.000 0.028 0.004
#> DRR006463 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006464 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006465 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006466 2 0.0260 0.9868 0.000 0.992 0.000 0.000 0.008 0.000
#> DRR006467 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006468 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006469 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006470 5 0.1625 0.7559 0.000 0.000 0.060 0.000 0.928 0.012
#> DRR006471 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006472 2 0.3741 0.5297 0.000 0.672 0.008 0.000 0.320 0.000
#> DRR006473 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006474 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006475 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006476 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006477 3 0.4469 -0.0766 0.000 0.000 0.504 0.468 0.028 0.000
#> DRR006478 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006479 1 0.3620 0.4984 0.648 0.000 0.352 0.000 0.000 0.000
#> DRR006480 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006481 3 0.1204 0.8294 0.000 0.000 0.944 0.000 0.056 0.000
#> DRR006482 6 0.1408 0.8772 0.000 0.000 0.020 0.000 0.036 0.944
#> DRR006483 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006484 3 0.1219 0.8412 0.048 0.000 0.948 0.000 0.004 0.000
#> DRR006485 2 0.0146 0.9889 0.000 0.996 0.000 0.000 0.004 0.000
#> DRR006486 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006487 3 0.2420 0.7992 0.040 0.000 0.884 0.000 0.076 0.000
#> DRR006488 2 0.0000 0.9908 0.000 1.000 0.000 0.000 0.000 0.000
#> DRR006489 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006490 1 0.3747 0.4180 0.604 0.000 0.396 0.000 0.000 0.000
#> DRR006491 1 0.3847 0.2803 0.544 0.000 0.456 0.000 0.000 0.000
#> DRR006492 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
#> DRR006493 3 0.0865 0.8489 0.036 0.000 0.964 0.000 0.000 0.000
#> DRR006494 1 0.0000 0.8763 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16187 rows and 121 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 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.991 0.997 0.4445 0.554 0.554
#> 3 3 0.889 0.915 0.964 0.4163 0.803 0.649
#> 4 4 1.000 0.962 0.987 0.0876 0.929 0.813
#> 5 5 0.883 0.866 0.942 0.1392 0.833 0.527
#> 6 6 0.873 0.826 0.891 0.0415 0.944 0.757
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> DRR006374 2 0.0000 0.990 0.000 1.000
#> DRR006375 1 0.0000 1.000 1.000 0.000
#> DRR006376 1 0.0000 1.000 1.000 0.000
#> DRR006377 1 0.0000 1.000 1.000 0.000
#> DRR006378 2 0.0000 0.990 0.000 1.000
#> DRR006379 1 0.0000 1.000 1.000 0.000
#> DRR006380 2 0.0000 0.990 0.000 1.000
#> DRR006381 1 0.0000 1.000 1.000 0.000
#> DRR006382 2 0.0000 0.990 0.000 1.000
#> DRR006383 1 0.0000 1.000 1.000 0.000
#> DRR006384 2 0.0000 0.990 0.000 1.000
#> DRR006385 1 0.0000 1.000 1.000 0.000
#> DRR006386 2 0.0000 0.990 0.000 1.000
#> DRR006387 1 0.0000 1.000 1.000 0.000
#> DRR006388 1 0.0000 1.000 1.000 0.000
#> DRR006389 1 0.0000 1.000 1.000 0.000
#> DRR006390 2 0.0000 0.990 0.000 1.000
#> DRR006391 2 0.0000 0.990 0.000 1.000
#> DRR006392 1 0.0000 1.000 1.000 0.000
#> DRR006393 1 0.0000 1.000 1.000 0.000
#> DRR006394 2 0.0000 0.990 0.000 1.000
#> DRR006395 1 0.0000 1.000 1.000 0.000
#> DRR006396 1 0.0000 1.000 1.000 0.000
#> DRR006397 1 0.0000 1.000 1.000 0.000
#> DRR006398 1 0.0000 1.000 1.000 0.000
#> DRR006399 1 0.0000 1.000 1.000 0.000
#> DRR006400 1 0.0000 1.000 1.000 0.000
#> DRR006401 2 0.0000 0.990 0.000 1.000
#> DRR006402 2 0.0000 0.990 0.000 1.000
#> DRR006403 1 0.0000 1.000 1.000 0.000
#> DRR006404 1 0.0000 1.000 1.000 0.000
#> DRR006405 1 0.0000 1.000 1.000 0.000
#> DRR006406 1 0.0000 1.000 1.000 0.000
#> DRR006407 2 0.0000 0.990 0.000 1.000
#> DRR006408 2 0.0000 0.990 0.000 1.000
#> DRR006409 1 0.0000 1.000 1.000 0.000
#> DRR006410 1 0.0000 1.000 1.000 0.000
#> DRR006411 2 0.0000 0.990 0.000 1.000
#> DRR006412 2 0.0000 0.990 0.000 1.000
#> DRR006413 1 0.0000 1.000 1.000 0.000
#> DRR006414 1 0.0000 1.000 1.000 0.000
#> DRR006415 1 0.0000 1.000 1.000 0.000
#> DRR006416 1 0.0000 1.000 1.000 0.000
#> DRR006417 1 0.0000 1.000 1.000 0.000
#> DRR006418 1 0.0000 1.000 1.000 0.000
#> DRR006419 1 0.0000 1.000 1.000 0.000
#> DRR006420 1 0.0000 1.000 1.000 0.000
#> DRR006421 1 0.0000 1.000 1.000 0.000
#> DRR006422 1 0.0000 1.000 1.000 0.000
#> DRR006423 2 0.0000 0.990 0.000 1.000
#> DRR006424 1 0.0000 1.000 1.000 0.000
#> DRR006425 2 0.0000 0.990 0.000 1.000
#> DRR006426 1 0.0000 1.000 1.000 0.000
#> DRR006427 2 0.0000 0.990 0.000 1.000
#> DRR006428 1 0.0000 1.000 1.000 0.000
#> DRR006429 2 0.0000 0.990 0.000 1.000
#> DRR006430 1 0.0000 1.000 1.000 0.000
#> DRR006431 1 0.0000 1.000 1.000 0.000
#> DRR006432 1 0.0000 1.000 1.000 0.000
#> DRR006433 1 0.0000 1.000 1.000 0.000
#> DRR006434 2 0.0000 0.990 0.000 1.000
#> DRR006435 2 0.0000 0.990 0.000 1.000
#> DRR006436 2 0.0000 0.990 0.000 1.000
#> DRR006437 1 0.0000 1.000 1.000 0.000
#> DRR006438 1 0.0000 1.000 1.000 0.000
#> DRR006439 1 0.0000 1.000 1.000 0.000
#> DRR006440 2 0.0000 0.990 0.000 1.000
#> DRR006441 2 0.0000 0.990 0.000 1.000
#> DRR006442 1 0.0000 1.000 1.000 0.000
#> DRR006443 2 0.0000 0.990 0.000 1.000
#> DRR006444 2 0.0000 0.990 0.000 1.000
#> DRR006445 1 0.0000 1.000 1.000 0.000
#> DRR006446 2 0.0000 0.990 0.000 1.000
#> DRR006447 1 0.0000 1.000 1.000 0.000
#> DRR006448 1 0.0000 1.000 1.000 0.000
#> DRR006449 1 0.0000 1.000 1.000 0.000
#> DRR006450 1 0.0000 1.000 1.000 0.000
#> DRR006451 1 0.0000 1.000 1.000 0.000
#> DRR006452 1 0.0000 1.000 1.000 0.000
#> DRR006453 1 0.0000 1.000 1.000 0.000
#> DRR006454 1 0.0376 0.996 0.996 0.004
#> DRR006455 2 0.0000 0.990 0.000 1.000
#> DRR006456 1 0.0000 1.000 1.000 0.000
#> DRR006457 1 0.0000 1.000 1.000 0.000
#> DRR006458 1 0.0000 1.000 1.000 0.000
#> DRR006459 1 0.0000 1.000 1.000 0.000
#> DRR006460 2 0.0000 0.990 0.000 1.000
#> DRR006461 2 0.0000 0.990 0.000 1.000
#> DRR006462 1 0.0000 1.000 1.000 0.000
#> DRR006463 2 0.0000 0.990 0.000 1.000
#> DRR006464 2 0.0000 0.990 0.000 1.000
#> DRR006465 1 0.0000 1.000 1.000 0.000
#> DRR006466 2 0.9710 0.333 0.400 0.600
#> DRR006467 1 0.0000 1.000 1.000 0.000
#> DRR006468 2 0.0000 0.990 0.000 1.000
#> DRR006469 2 0.0000 0.990 0.000 1.000
#> DRR006470 1 0.0000 1.000 1.000 0.000
#> DRR006471 1 0.0000 1.000 1.000 0.000
#> DRR006472 1 0.0000 1.000 1.000 0.000
#> DRR006473 2 0.0000 0.990 0.000 1.000
#> DRR006474 2 0.0000 0.990 0.000 1.000
#> DRR006475 1 0.0000 1.000 1.000 0.000
#> DRR006476 2 0.0000 0.990 0.000 1.000
#> DRR006477 1 0.0000 1.000 1.000 0.000
#> DRR006478 1 0.0000 1.000 1.000 0.000
#> DRR006479 1 0.0000 1.000 1.000 0.000
#> DRR006480 1 0.0000 1.000 1.000 0.000
#> DRR006481 1 0.0000 1.000 1.000 0.000
#> DRR006482 1 0.0000 1.000 1.000 0.000
#> DRR006483 1 0.0000 1.000 1.000 0.000
#> DRR006484 1 0.0000 1.000 1.000 0.000
#> DRR006485 2 0.0000 0.990 0.000 1.000
#> DRR006486 1 0.0000 1.000 1.000 0.000
#> DRR006487 1 0.0000 1.000 1.000 0.000
#> DRR006488 2 0.0000 0.990 0.000 1.000
#> DRR006489 1 0.0000 1.000 1.000 0.000
#> DRR006490 1 0.0000 1.000 1.000 0.000
#> DRR006491 1 0.0000 1.000 1.000 0.000
#> DRR006492 1 0.0000 1.000 1.000 0.000
#> DRR006493 1 0.0000 1.000 1.000 0.000
#> DRR006494 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006375 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006376 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006377 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006378 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006379 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006380 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006381 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006382 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006383 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006384 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006385 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006386 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006387 3 0.4654 0.7381 0.208 0.000 0.792
#> DRR006388 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006389 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006390 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006391 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006392 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006393 3 0.5431 0.6202 0.284 0.000 0.716
#> DRR006394 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006395 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006396 1 0.1031 0.9464 0.976 0.000 0.024
#> DRR006397 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006398 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006399 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006400 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006401 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006403 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006404 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006405 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006406 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006407 2 0.5327 0.6744 0.000 0.728 0.272
#> DRR006408 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006409 1 0.6295 0.0133 0.528 0.000 0.472
#> DRR006410 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006411 2 0.5465 0.6550 0.000 0.712 0.288
#> DRR006412 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006413 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006414 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006415 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006416 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006417 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006418 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006419 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006420 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006421 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006422 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006423 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006424 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006425 2 0.3816 0.8075 0.000 0.852 0.148
#> DRR006426 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006427 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006428 3 0.5431 0.6202 0.284 0.000 0.716
#> DRR006429 2 0.5431 0.6604 0.000 0.716 0.284
#> DRR006430 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006431 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006432 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006433 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006434 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006435 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006436 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006437 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006438 3 0.5431 0.6202 0.284 0.000 0.716
#> DRR006439 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006440 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006441 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006442 1 0.2356 0.8952 0.928 0.000 0.072
#> DRR006443 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006444 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006445 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006446 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006447 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006448 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006449 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006450 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006451 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006452 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006453 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006454 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006455 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006456 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006457 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006458 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006459 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006460 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006461 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006462 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006463 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006464 2 0.5397 0.6651 0.000 0.720 0.280
#> DRR006465 3 0.4654 0.7380 0.208 0.000 0.792
#> DRR006466 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006467 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006468 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006469 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006470 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006471 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006472 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006473 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006474 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006475 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006476 2 0.5431 0.6604 0.000 0.716 0.284
#> DRR006477 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006478 3 0.4702 0.7325 0.212 0.000 0.788
#> DRR006479 3 0.5431 0.6202 0.284 0.000 0.716
#> DRR006480 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006481 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006482 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006483 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006484 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006485 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006486 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006487 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006488 2 0.0000 0.9497 0.000 1.000 0.000
#> DRR006489 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006490 1 0.0000 0.9693 1.000 0.000 0.000
#> DRR006491 3 0.6126 0.3695 0.400 0.000 0.600
#> DRR006492 3 0.0592 0.9492 0.012 0.000 0.988
#> DRR006493 3 0.0000 0.9592 0.000 0.000 1.000
#> DRR006494 1 0.0000 0.9693 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006375 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006376 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006377 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006378 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006379 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006380 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006381 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006382 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006383 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006384 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006385 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006386 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006387 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006388 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006389 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006390 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006391 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006392 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006393 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006394 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006395 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006396 1 0.3444 0.701 0.816 0.000 0.184 0.00
#> DRR006397 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006398 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006399 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006400 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006401 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006402 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006403 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006404 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006405 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006406 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006407 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006408 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006409 1 0.4989 0.105 0.528 0.000 0.472 0.00
#> DRR006410 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006411 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006412 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006413 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006414 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006415 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006416 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006417 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006418 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006419 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006420 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006421 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006422 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006423 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006424 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006425 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006426 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006427 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006428 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006429 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006430 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006431 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006432 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006433 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006434 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006435 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006436 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006437 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006438 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006439 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006440 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006441 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006442 1 0.1867 0.863 0.928 0.000 0.072 0.00
#> DRR006443 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006444 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006445 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006446 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006447 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006448 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006449 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006450 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006451 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006452 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006453 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006454 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006455 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006456 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006457 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006458 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006459 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006460 2 0.0707 0.980 0.000 0.980 0.000 0.02
#> DRR006461 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006462 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006463 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006464 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006465 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006466 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006467 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006468 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006469 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006470 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006471 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006472 3 0.4843 0.345 0.000 0.396 0.604 0.00
#> DRR006473 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006474 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006475 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006476 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006477 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006478 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006479 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006480 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006481 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006482 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006483 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006484 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006485 2 0.0000 0.999 0.000 1.000 0.000 0.00
#> DRR006486 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006487 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006488 4 0.0000 1.000 0.000 0.000 0.000 1.00
#> DRR006489 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006490 1 0.0000 0.951 1.000 0.000 0.000 0.00
#> DRR006491 3 0.4855 0.299 0.400 0.000 0.600 0.00
#> DRR006492 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006493 3 0.0000 0.985 0.000 0.000 1.000 0.00
#> DRR006494 1 0.0000 0.951 1.000 0.000 0.000 0.00
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006375 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006376 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006377 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006378 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006379 4 0.2732 0.7649 0.000 0.000 0.160 0.840 0.000
#> DRR006380 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006381 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006382 2 0.0510 0.8817 0.000 0.984 0.000 0.016 0.000
#> DRR006383 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006384 2 0.4138 0.4709 0.000 0.616 0.000 0.000 0.384
#> DRR006385 4 0.0510 0.8636 0.000 0.000 0.016 0.984 0.000
#> DRR006386 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000
#> DRR006387 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006388 4 0.0000 0.8639 0.000 0.000 0.000 1.000 0.000
#> DRR006389 4 0.0000 0.8639 0.000 0.000 0.000 1.000 0.000
#> DRR006390 2 0.4138 0.4709 0.000 0.616 0.000 0.000 0.384
#> DRR006391 2 0.4138 0.4709 0.000 0.616 0.000 0.000 0.384
#> DRR006392 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006393 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006394 2 0.0510 0.8817 0.000 0.984 0.000 0.016 0.000
#> DRR006395 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006396 1 0.2966 0.7092 0.816 0.000 0.184 0.000 0.000
#> DRR006397 4 0.0000 0.8639 0.000 0.000 0.000 1.000 0.000
#> DRR006398 4 0.0162 0.8642 0.000 0.000 0.004 0.996 0.000
#> DRR006399 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006400 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006401 2 0.4138 0.4709 0.000 0.616 0.000 0.000 0.384
#> DRR006402 2 0.4138 0.4709 0.000 0.616 0.000 0.000 0.384
#> DRR006403 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006404 3 0.2230 0.8522 0.000 0.000 0.884 0.116 0.000
#> DRR006405 4 0.2127 0.8109 0.000 0.000 0.108 0.892 0.000
#> DRR006406 4 0.0609 0.8628 0.000 0.000 0.020 0.980 0.000
#> DRR006407 4 0.4138 0.5004 0.000 0.384 0.000 0.616 0.000
#> DRR006408 2 0.0404 0.8837 0.000 0.988 0.000 0.012 0.000
#> DRR006409 1 0.4297 0.0947 0.528 0.000 0.472 0.000 0.000
#> DRR006410 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006411 4 0.3039 0.7368 0.000 0.192 0.000 0.808 0.000
#> DRR006412 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006413 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006414 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006415 4 0.2561 0.7517 0.000 0.000 0.144 0.856 0.000
#> DRR006416 4 0.0510 0.8636 0.000 0.000 0.016 0.984 0.000
#> DRR006417 4 0.0703 0.8613 0.000 0.000 0.024 0.976 0.000
#> DRR006418 4 0.4219 0.3781 0.000 0.000 0.416 0.584 0.000
#> DRR006419 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006420 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006421 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006422 4 0.0000 0.8639 0.000 0.000 0.000 1.000 0.000
#> DRR006423 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006424 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006425 2 0.0510 0.8817 0.000 0.984 0.000 0.016 0.000
#> DRR006426 4 0.0880 0.8572 0.000 0.000 0.032 0.968 0.000
#> DRR006427 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000
#> DRR006428 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006429 4 0.4138 0.5004 0.000 0.384 0.000 0.616 0.000
#> DRR006430 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006432 4 0.0000 0.8639 0.000 0.000 0.000 1.000 0.000
#> DRR006433 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006434 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006435 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000
#> DRR006436 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000
#> DRR006437 4 0.0510 0.8636 0.000 0.000 0.016 0.984 0.000
#> DRR006438 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006439 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006440 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006441 2 0.0510 0.8817 0.000 0.984 0.000 0.016 0.000
#> DRR006442 1 0.1608 0.8715 0.928 0.000 0.072 0.000 0.000
#> DRR006443 2 0.0510 0.8817 0.000 0.984 0.000 0.016 0.000
#> DRR006444 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000
#> DRR006445 4 0.3109 0.7227 0.000 0.000 0.200 0.800 0.000
#> DRR006446 2 0.4138 0.4709 0.000 0.616 0.000 0.000 0.384
#> DRR006447 4 0.1965 0.8202 0.000 0.000 0.096 0.904 0.000
#> DRR006448 3 0.3143 0.7061 0.000 0.000 0.796 0.204 0.000
#> DRR006449 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006450 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006451 4 0.0000 0.8639 0.000 0.000 0.000 1.000 0.000
#> DRR006452 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006453 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006454 4 0.0000 0.8639 0.000 0.000 0.000 1.000 0.000
#> DRR006455 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000
#> DRR006456 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006457 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006458 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006459 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006460 2 0.0609 0.8773 0.000 0.980 0.000 0.000 0.020
#> DRR006461 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006462 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006463 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006464 4 0.4138 0.5004 0.000 0.384 0.000 0.616 0.000
#> DRR006465 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006466 4 0.0000 0.8639 0.000 0.000 0.000 1.000 0.000
#> DRR006467 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006468 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000
#> DRR006469 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006470 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006471 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006472 4 0.0000 0.8639 0.000 0.000 0.000 1.000 0.000
#> DRR006473 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006474 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006475 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006476 4 0.4138 0.5004 0.000 0.384 0.000 0.616 0.000
#> DRR006477 3 0.2230 0.8524 0.000 0.000 0.884 0.116 0.000
#> DRR006478 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006479 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006480 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006481 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006482 4 0.0510 0.8636 0.000 0.000 0.016 0.984 0.000
#> DRR006483 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006484 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006485 2 0.0000 0.8886 0.000 1.000 0.000 0.000 0.000
#> DRR006486 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006487 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006488 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000
#> DRR006489 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006490 1 0.0000 0.9544 1.000 0.000 0.000 0.000 0.000
#> DRR006491 3 0.4182 0.2983 0.400 0.000 0.600 0.000 0.000
#> DRR006492 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006493 3 0.0000 0.9723 0.000 0.000 1.000 0.000 0.000
#> DRR006494 1 0.0000 0.9544 1.000 0.000 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
#> DRR006374 2 0.0458 0.833 0.000 0.984 0.000 0.016 0 0.000
#> DRR006375 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006376 4 0.3756 0.671 0.000 0.000 0.400 0.600 0 0.000
#> DRR006377 4 0.3756 0.671 0.000 0.000 0.400 0.600 0 0.000
#> DRR006378 2 0.0260 0.833 0.000 0.992 0.000 0.008 0 0.000
#> DRR006379 4 0.5259 0.621 0.000 0.000 0.160 0.600 0 0.240
#> DRR006380 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006381 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006382 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006383 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006384 2 0.3756 0.717 0.000 0.600 0.000 0.400 0 0.000
#> DRR006385 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006386 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006387 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006388 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006389 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006390 2 0.3756 0.717 0.000 0.600 0.000 0.400 0 0.000
#> DRR006391 2 0.3756 0.717 0.000 0.600 0.000 0.400 0 0.000
#> DRR006392 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006393 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006394 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006395 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006396 1 0.2664 0.700 0.816 0.000 0.184 0.000 0 0.000
#> DRR006397 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006398 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006399 4 0.3756 0.671 0.000 0.000 0.400 0.600 0 0.000
#> DRR006400 4 0.3756 0.671 0.000 0.000 0.400 0.600 0 0.000
#> DRR006401 2 0.3756 0.717 0.000 0.600 0.000 0.400 0 0.000
#> DRR006402 2 0.3756 0.717 0.000 0.600 0.000 0.400 0 0.000
#> DRR006403 4 0.3756 0.671 0.000 0.000 0.400 0.600 0 0.000
#> DRR006404 4 0.5138 0.693 0.000 0.000 0.276 0.600 0 0.124
#> DRR006405 4 0.4904 0.547 0.000 0.000 0.084 0.600 0 0.316
#> DRR006406 4 0.3881 0.428 0.000 0.000 0.004 0.600 0 0.396
#> DRR006407 4 0.3756 0.302 0.000 0.400 0.000 0.600 0 0.000
#> DRR006408 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006409 1 0.3860 0.126 0.528 0.000 0.472 0.000 0 0.000
#> DRR006410 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006411 6 0.2969 0.680 0.000 0.224 0.000 0.000 0 0.776
#> DRR006412 2 0.3756 0.717 0.000 0.600 0.000 0.400 0 0.000
#> DRR006413 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006414 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006415 6 0.2135 0.715 0.000 0.000 0.128 0.000 0 0.872
#> DRR006416 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006417 6 0.0260 0.844 0.000 0.000 0.008 0.000 0 0.992
#> DRR006418 6 0.3789 0.186 0.000 0.000 0.416 0.000 0 0.584
#> DRR006419 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006420 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006421 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006422 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006423 2 0.3756 0.717 0.000 0.600 0.000 0.400 0 0.000
#> DRR006424 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006425 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006426 6 0.0458 0.838 0.000 0.000 0.016 0.000 0 0.984
#> DRR006427 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006428 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006429 6 0.3756 0.485 0.000 0.400 0.000 0.000 0 0.600
#> DRR006430 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006431 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006432 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006433 4 0.3756 0.671 0.000 0.000 0.400 0.600 0 0.000
#> DRR006434 2 0.3756 0.717 0.000 0.600 0.000 0.400 0 0.000
#> DRR006435 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006436 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006437 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006438 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006439 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006440 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006441 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006442 1 0.1444 0.877 0.928 0.000 0.072 0.000 0 0.000
#> DRR006443 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006444 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006445 6 0.2793 0.612 0.000 0.000 0.200 0.000 0 0.800
#> DRR006446 2 0.3756 0.717 0.000 0.600 0.000 0.400 0 0.000
#> DRR006447 6 0.1714 0.766 0.000 0.000 0.092 0.000 0 0.908
#> DRR006448 4 0.3756 0.671 0.000 0.000 0.400 0.600 0 0.000
#> DRR006449 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006450 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006451 4 0.3756 0.420 0.000 0.000 0.000 0.600 0 0.400
#> DRR006452 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006453 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006454 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006455 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006456 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006457 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006458 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006459 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006460 2 0.3756 0.717 0.000 0.600 0.000 0.400 0 0.000
#> DRR006461 2 0.0458 0.833 0.000 0.984 0.000 0.016 0 0.000
#> DRR006462 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006463 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006464 6 0.3756 0.485 0.000 0.400 0.000 0.000 0 0.600
#> DRR006465 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006466 4 0.4131 0.431 0.000 0.016 0.000 0.600 0 0.384
#> DRR006467 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006468 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006469 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006470 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006471 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006472 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006473 2 0.0458 0.833 0.000 0.984 0.000 0.016 0 0.000
#> DRR006474 2 0.0458 0.833 0.000 0.984 0.000 0.016 0 0.000
#> DRR006475 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006476 6 0.3756 0.485 0.000 0.400 0.000 0.000 0 0.600
#> DRR006477 3 0.2178 0.753 0.000 0.000 0.868 0.000 0 0.132
#> DRR006478 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006479 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006480 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006481 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006482 6 0.0000 0.849 0.000 0.000 0.000 0.000 0 1.000
#> DRR006483 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006484 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006485 2 0.0000 0.833 0.000 1.000 0.000 0.000 0 0.000
#> DRR006486 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006487 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006488 5 0.0000 1.000 0.000 0.000 0.000 0.000 1 0.000
#> DRR006489 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006490 1 0.0000 0.956 1.000 0.000 0.000 0.000 0 0.000
#> DRR006491 3 0.3756 0.245 0.400 0.000 0.600 0.000 0 0.000
#> DRR006492 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006493 3 0.0000 0.968 0.000 0.000 1.000 0.000 0 0.000
#> DRR006494 1 0.0000 0.956 1.000 0.000 0.000 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)
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 16187 rows and 121 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 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 0.417 0.679 0.814 0.2800 0.890 0.890
#> 3 3 0.795 0.906 0.944 0.9520 0.592 0.542
#> 4 4 0.938 0.913 0.964 0.3085 0.689 0.421
#> 5 5 0.875 0.900 0.912 0.0447 0.952 0.838
#> 6 6 0.838 0.821 0.865 0.0430 0.985 0.944
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
#> DRR006374 1 0.0000 0.735 1.000 0.000
#> DRR006375 1 0.7674 0.721 0.776 0.224
#> DRR006376 1 0.9460 0.688 0.636 0.364
#> DRR006377 1 0.9460 0.688 0.636 0.364
#> DRR006378 1 0.2603 0.696 0.956 0.044
#> DRR006379 1 0.9460 0.688 0.636 0.364
#> DRR006380 1 0.2603 0.696 0.956 0.044
#> DRR006381 1 0.3584 0.746 0.932 0.068
#> DRR006382 1 0.2043 0.740 0.968 0.032
#> DRR006383 1 0.9460 0.688 0.636 0.364
#> DRR006384 1 0.5519 0.559 0.872 0.128
#> DRR006385 1 0.4161 0.745 0.916 0.084
#> DRR006386 2 0.9460 1.000 0.364 0.636
#> DRR006387 1 0.9129 0.694 0.672 0.328
#> DRR006388 1 0.0000 0.735 1.000 0.000
#> DRR006389 1 0.0000 0.735 1.000 0.000
#> DRR006390 1 0.9881 -0.554 0.564 0.436
#> DRR006391 1 0.9881 -0.554 0.564 0.436
#> DRR006392 1 0.0000 0.735 1.000 0.000
#> DRR006393 1 0.5408 0.741 0.876 0.124
#> DRR006394 1 0.1633 0.716 0.976 0.024
#> DRR006395 1 0.9460 0.688 0.636 0.364
#> DRR006396 1 0.2778 0.745 0.952 0.048
#> DRR006397 1 0.0000 0.735 1.000 0.000
#> DRR006398 1 0.0000 0.735 1.000 0.000
#> DRR006399 1 0.9460 0.688 0.636 0.364
#> DRR006400 1 0.9460 0.688 0.636 0.364
#> DRR006401 1 0.2603 0.696 0.956 0.044
#> DRR006402 1 0.2603 0.696 0.956 0.044
#> DRR006403 1 0.9460 0.688 0.636 0.364
#> DRR006404 1 0.9460 0.688 0.636 0.364
#> DRR006405 1 0.9460 0.688 0.636 0.364
#> DRR006406 1 0.9460 0.688 0.636 0.364
#> DRR006407 1 0.9460 0.688 0.636 0.364
#> DRR006408 1 0.9460 0.688 0.636 0.364
#> DRR006409 1 0.9460 0.688 0.636 0.364
#> DRR006410 1 0.9460 0.688 0.636 0.364
#> DRR006411 1 0.0672 0.729 0.992 0.008
#> DRR006412 1 0.9323 -0.247 0.652 0.348
#> DRR006413 1 0.7056 0.728 0.808 0.192
#> DRR006414 1 0.9427 0.689 0.640 0.360
#> DRR006415 1 0.9460 0.688 0.636 0.364
#> DRR006416 1 0.0000 0.735 1.000 0.000
#> DRR006417 1 0.9460 0.688 0.636 0.364
#> DRR006418 1 0.4690 0.744 0.900 0.100
#> DRR006419 1 0.7674 0.721 0.776 0.224
#> DRR006420 1 0.3584 0.746 0.932 0.068
#> DRR006421 1 0.9460 0.688 0.636 0.364
#> DRR006422 1 0.0000 0.735 1.000 0.000
#> DRR006423 1 0.2603 0.696 0.956 0.044
#> DRR006424 1 0.0000 0.735 1.000 0.000
#> DRR006425 1 0.2423 0.700 0.960 0.040
#> DRR006426 1 0.4022 0.739 0.920 0.080
#> DRR006427 1 0.9881 -0.554 0.564 0.436
#> DRR006428 1 0.9460 0.688 0.636 0.364
#> DRR006429 1 0.2423 0.700 0.960 0.040
#> DRR006430 1 0.0000 0.735 1.000 0.000
#> DRR006431 1 0.0672 0.738 0.992 0.008
#> DRR006432 1 0.2423 0.742 0.960 0.040
#> DRR006433 1 0.9460 0.688 0.636 0.364
#> DRR006434 1 0.0000 0.735 1.000 0.000
#> DRR006435 2 0.9460 1.000 0.364 0.636
#> DRR006436 2 0.9460 1.000 0.364 0.636
#> DRR006437 1 0.0376 0.737 0.996 0.004
#> DRR006438 1 0.9460 0.688 0.636 0.364
#> DRR006439 1 0.9460 0.688 0.636 0.364
#> DRR006440 1 0.2236 0.740 0.964 0.036
#> DRR006441 1 0.2603 0.696 0.956 0.044
#> DRR006442 1 0.9460 0.688 0.636 0.364
#> DRR006443 1 0.2236 0.740 0.964 0.036
#> DRR006444 2 0.9460 1.000 0.364 0.636
#> DRR006445 1 0.0672 0.738 0.992 0.008
#> DRR006446 1 0.9881 -0.554 0.564 0.436
#> DRR006447 1 0.1414 0.741 0.980 0.020
#> DRR006448 1 0.9460 0.688 0.636 0.364
#> DRR006449 1 0.9129 0.694 0.672 0.328
#> DRR006450 1 0.2423 0.744 0.960 0.040
#> DRR006451 1 0.9460 0.688 0.636 0.364
#> DRR006452 1 0.0672 0.738 0.992 0.008
#> DRR006453 1 0.0938 0.739 0.988 0.012
#> DRR006454 1 0.0000 0.735 1.000 0.000
#> DRR006455 2 0.9460 1.000 0.364 0.636
#> DRR006456 1 0.9460 0.688 0.636 0.364
#> DRR006457 1 0.9460 0.688 0.636 0.364
#> DRR006458 1 0.0000 0.735 1.000 0.000
#> DRR006459 1 0.3114 0.746 0.944 0.056
#> DRR006460 1 0.2603 0.696 0.956 0.044
#> DRR006461 1 0.0938 0.738 0.988 0.012
#> DRR006462 1 0.9129 0.694 0.672 0.328
#> DRR006463 1 0.2236 0.740 0.964 0.036
#> DRR006464 1 0.0672 0.729 0.992 0.008
#> DRR006465 1 0.5842 0.738 0.860 0.140
#> DRR006466 1 0.9460 0.688 0.636 0.364
#> DRR006467 1 0.0000 0.735 1.000 0.000
#> DRR006468 2 0.9460 1.000 0.364 0.636
#> DRR006469 1 0.2603 0.696 0.956 0.044
#> DRR006470 1 0.9460 0.688 0.636 0.364
#> DRR006471 1 0.0000 0.735 1.000 0.000
#> DRR006472 1 0.0000 0.735 1.000 0.000
#> DRR006473 1 0.2603 0.696 0.956 0.044
#> DRR006474 1 0.2603 0.696 0.956 0.044
#> DRR006475 1 0.0000 0.735 1.000 0.000
#> DRR006476 1 0.3733 0.717 0.928 0.072
#> DRR006477 1 0.9460 0.688 0.636 0.364
#> DRR006478 1 0.6531 0.733 0.832 0.168
#> DRR006479 1 0.6048 0.737 0.852 0.148
#> DRR006480 1 0.0000 0.735 1.000 0.000
#> DRR006481 1 0.9460 0.688 0.636 0.364
#> DRR006482 1 0.0000 0.735 1.000 0.000
#> DRR006483 1 0.0000 0.735 1.000 0.000
#> DRR006484 1 0.9460 0.688 0.636 0.364
#> DRR006485 1 0.2236 0.740 0.964 0.036
#> DRR006486 1 0.7453 0.724 0.788 0.212
#> DRR006487 1 0.9460 0.688 0.636 0.364
#> DRR006488 2 0.9460 1.000 0.364 0.636
#> DRR006489 1 0.0000 0.735 1.000 0.000
#> DRR006490 1 0.9427 0.689 0.640 0.360
#> DRR006491 1 0.9460 0.688 0.636 0.364
#> DRR006492 1 0.4022 0.747 0.920 0.080
#> DRR006493 1 0.9460 0.688 0.636 0.364
#> DRR006494 1 0.0000 0.735 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006375 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006376 3 0.1411 0.911 0.036 0.000 0.964
#> DRR006377 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006378 3 0.4842 0.783 0.000 0.224 0.776
#> DRR006379 3 0.1964 0.906 0.056 0.000 0.944
#> DRR006380 3 0.3310 0.899 0.028 0.064 0.908
#> DRR006381 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006382 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006383 3 0.1964 0.901 0.056 0.000 0.944
#> DRR006384 3 0.5138 0.750 0.000 0.252 0.748
#> DRR006385 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006386 2 0.0000 1.000 0.000 1.000 0.000
#> DRR006387 1 0.0237 0.979 0.996 0.000 0.004
#> DRR006388 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006389 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006390 3 0.5948 0.584 0.000 0.360 0.640
#> DRR006391 3 0.5948 0.584 0.000 0.360 0.640
#> DRR006392 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006393 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006394 3 0.1643 0.909 0.044 0.000 0.956
#> DRR006395 3 0.1860 0.907 0.052 0.000 0.948
#> DRR006396 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006397 1 0.0237 0.979 0.996 0.000 0.004
#> DRR006398 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006399 3 0.1964 0.906 0.056 0.000 0.944
#> DRR006400 3 0.1964 0.906 0.056 0.000 0.944
#> DRR006401 3 0.3941 0.834 0.000 0.156 0.844
#> DRR006402 3 0.3941 0.834 0.000 0.156 0.844
#> DRR006403 3 0.1860 0.907 0.052 0.000 0.948
#> DRR006404 3 0.1163 0.912 0.028 0.000 0.972
#> DRR006405 3 0.1964 0.906 0.056 0.000 0.944
#> DRR006406 3 0.1964 0.906 0.056 0.000 0.944
#> DRR006407 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006408 3 0.1964 0.906 0.056 0.000 0.944
#> DRR006409 3 0.2796 0.884 0.092 0.000 0.908
#> DRR006410 1 0.1753 0.924 0.952 0.000 0.048
#> DRR006411 3 0.4189 0.885 0.056 0.068 0.876
#> DRR006412 3 0.5948 0.584 0.000 0.360 0.640
#> DRR006413 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006414 3 0.3879 0.835 0.152 0.000 0.848
#> DRR006415 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006416 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006417 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006418 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006419 1 0.3816 0.766 0.852 0.000 0.148
#> DRR006420 3 0.5968 0.505 0.364 0.000 0.636
#> DRR006421 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006422 3 0.4288 0.883 0.060 0.068 0.872
#> DRR006423 3 0.4842 0.783 0.000 0.224 0.776
#> DRR006424 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006425 3 0.1964 0.906 0.056 0.000 0.944
#> DRR006426 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006427 3 0.5948 0.584 0.000 0.360 0.640
#> DRR006428 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006429 3 0.4189 0.885 0.056 0.068 0.876
#> DRR006430 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006431 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006432 3 0.0237 0.913 0.004 0.000 0.996
#> DRR006433 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006434 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006435 2 0.0000 1.000 0.000 1.000 0.000
#> DRR006436 2 0.0000 1.000 0.000 1.000 0.000
#> DRR006437 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006438 3 0.2261 0.891 0.068 0.000 0.932
#> DRR006439 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006440 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006441 3 0.3412 0.871 0.000 0.124 0.876
#> DRR006442 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006443 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006444 2 0.0000 1.000 0.000 1.000 0.000
#> DRR006445 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006446 3 0.5948 0.584 0.000 0.360 0.640
#> DRR006447 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006448 3 0.2066 0.905 0.060 0.000 0.940
#> DRR006449 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006450 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006451 3 0.1964 0.906 0.056 0.000 0.944
#> DRR006452 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006453 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006454 3 0.4189 0.885 0.056 0.068 0.876
#> DRR006455 2 0.0000 1.000 0.000 1.000 0.000
#> DRR006456 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006457 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006458 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006459 3 0.4931 0.730 0.232 0.000 0.768
#> DRR006460 3 0.3941 0.834 0.000 0.156 0.844
#> DRR006461 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006462 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006463 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006464 3 0.4189 0.885 0.056 0.068 0.876
#> DRR006465 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006466 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006467 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006468 2 0.0000 1.000 0.000 1.000 0.000
#> DRR006469 3 0.3412 0.871 0.000 0.124 0.876
#> DRR006470 3 0.0424 0.913 0.008 0.000 0.992
#> DRR006471 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006472 3 0.1753 0.908 0.048 0.000 0.952
#> DRR006473 3 0.4842 0.783 0.000 0.224 0.776
#> DRR006474 3 0.3619 0.864 0.000 0.136 0.864
#> DRR006475 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006476 3 0.1860 0.907 0.052 0.000 0.948
#> DRR006477 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006478 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006479 3 0.3412 0.862 0.124 0.000 0.876
#> DRR006480 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006481 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006482 1 0.1411 0.939 0.964 0.000 0.036
#> DRR006483 1 0.0237 0.979 0.996 0.000 0.004
#> DRR006484 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006485 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006486 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006487 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006488 2 0.0000 1.000 0.000 1.000 0.000
#> DRR006489 1 0.0000 0.983 1.000 0.000 0.000
#> DRR006490 3 0.2261 0.891 0.068 0.000 0.932
#> DRR006491 3 0.1289 0.908 0.032 0.000 0.968
#> DRR006492 1 0.1643 0.930 0.956 0.000 0.044
#> DRR006493 3 0.0000 0.913 0.000 0.000 1.000
#> DRR006494 1 0.4002 0.750 0.840 0.000 0.160
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 2 0.4804 0.472 0.000 0.616 0.384 0.000
#> DRR006375 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006376 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006377 3 0.4925 0.250 0.000 0.000 0.572 0.428
#> DRR006378 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006379 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006380 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006381 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006382 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006383 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006384 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006385 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006386 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006387 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006388 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006389 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006390 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006391 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006392 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006393 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006394 2 0.4713 0.517 0.000 0.640 0.360 0.000
#> DRR006395 4 0.3873 0.671 0.000 0.000 0.228 0.772
#> DRR006396 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006397 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006398 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006399 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006400 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006401 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006402 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006403 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006404 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006405 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006406 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006407 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006408 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006409 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006410 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006411 1 0.0336 0.979 0.992 0.000 0.008 0.000
#> DRR006412 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006413 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006414 1 0.4855 0.310 0.600 0.000 0.400 0.000
#> DRR006415 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006416 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006417 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006418 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006419 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006420 1 0.0188 0.983 0.996 0.000 0.004 0.000
#> DRR006421 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006422 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006423 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006424 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006425 2 0.5874 0.514 0.008 0.616 0.344 0.032
#> DRR006426 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006427 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006428 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006429 2 0.6222 0.539 0.080 0.616 0.304 0.000
#> DRR006430 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006431 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006432 3 0.0336 0.957 0.008 0.000 0.992 0.000
#> DRR006433 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006434 2 0.4804 0.472 0.000 0.616 0.384 0.000
#> DRR006435 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006436 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006437 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006438 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006439 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006440 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006441 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006442 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006443 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006444 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006445 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006446 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006447 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006448 4 0.0188 0.973 0.004 0.000 0.000 0.996
#> DRR006449 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006450 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006451 4 0.0000 0.977 0.000 0.000 0.000 1.000
#> DRR006452 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006453 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006454 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006455 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006456 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006457 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006458 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006459 1 0.2011 0.892 0.920 0.000 0.080 0.000
#> DRR006460 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006461 3 0.1302 0.919 0.000 0.044 0.956 0.000
#> DRR006462 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006463 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006464 2 0.6695 0.541 0.220 0.616 0.164 0.000
#> DRR006465 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006466 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006467 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006468 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006469 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006470 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006471 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006472 3 0.4193 0.595 0.268 0.000 0.732 0.000
#> DRR006473 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006474 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006475 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006476 2 0.4804 0.472 0.000 0.616 0.384 0.000
#> DRR006477 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006478 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006479 3 0.3219 0.750 0.164 0.000 0.836 0.000
#> DRR006480 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006481 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006482 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006483 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006484 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006485 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006486 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006487 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006488 2 0.0000 0.889 0.000 1.000 0.000 0.000
#> DRR006489 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006490 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006491 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006492 1 0.0000 0.987 1.000 0.000 0.000 0.000
#> DRR006493 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> DRR006494 1 0.0000 0.987 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
#> DRR006374 2 0.3304 0.687 0.000 0.816 0.168 0.000 0.016
#> DRR006375 1 0.1774 0.947 0.932 0.000 0.016 0.000 0.052
#> DRR006376 4 0.0000 0.976 0.000 0.000 0.000 1.000 0.000
#> DRR006377 3 0.4199 0.851 0.000 0.000 0.764 0.056 0.180
#> DRR006378 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000
#> DRR006379 4 0.0324 0.976 0.000 0.000 0.004 0.992 0.004
#> DRR006380 2 0.0854 0.915 0.000 0.976 0.004 0.008 0.012
#> DRR006381 1 0.1557 0.941 0.940 0.000 0.008 0.000 0.052
#> DRR006382 3 0.1990 0.839 0.004 0.068 0.920 0.000 0.008
#> DRR006383 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006384 2 0.0671 0.920 0.000 0.980 0.004 0.000 0.016
#> DRR006385 1 0.1557 0.941 0.940 0.000 0.008 0.000 0.052
#> DRR006386 5 0.3857 1.000 0.000 0.312 0.000 0.000 0.688
#> DRR006387 1 0.0898 0.953 0.972 0.000 0.020 0.000 0.008
#> DRR006388 1 0.0865 0.950 0.972 0.000 0.004 0.000 0.024
#> DRR006389 1 0.0865 0.950 0.972 0.000 0.004 0.000 0.024
#> DRR006390 2 0.0451 0.918 0.008 0.988 0.000 0.000 0.004
#> DRR006391 2 0.0451 0.918 0.008 0.988 0.000 0.000 0.004
#> DRR006392 1 0.1544 0.943 0.932 0.000 0.000 0.000 0.068
#> DRR006393 1 0.1251 0.953 0.956 0.000 0.008 0.000 0.036
#> DRR006394 2 0.2805 0.786 0.000 0.872 0.108 0.008 0.012
#> DRR006395 4 0.2339 0.847 0.000 0.004 0.100 0.892 0.004
#> DRR006396 1 0.1701 0.942 0.936 0.000 0.016 0.000 0.048
#> DRR006397 1 0.0451 0.952 0.988 0.000 0.004 0.008 0.000
#> DRR006398 1 0.0451 0.952 0.988 0.000 0.004 0.008 0.000
#> DRR006399 4 0.0000 0.976 0.000 0.000 0.000 1.000 0.000
#> DRR006400 4 0.0000 0.976 0.000 0.000 0.000 1.000 0.000
#> DRR006401 2 0.0671 0.920 0.000 0.980 0.004 0.000 0.016
#> DRR006402 2 0.0671 0.920 0.000 0.980 0.004 0.000 0.016
#> DRR006403 4 0.0000 0.976 0.000 0.000 0.000 1.000 0.000
#> DRR006404 4 0.0000 0.976 0.000 0.000 0.000 1.000 0.000
#> DRR006405 4 0.0613 0.974 0.000 0.004 0.008 0.984 0.004
#> DRR006406 4 0.0613 0.974 0.000 0.004 0.008 0.984 0.004
#> DRR006407 4 0.0981 0.961 0.000 0.012 0.008 0.972 0.008
#> DRR006408 4 0.0693 0.964 0.000 0.012 0.000 0.980 0.008
#> DRR006409 3 0.2729 0.870 0.008 0.004 0.876 0.004 0.108
#> DRR006410 1 0.0798 0.954 0.976 0.000 0.016 0.008 0.000
#> DRR006411 1 0.4118 0.716 0.772 0.188 0.032 0.008 0.000
#> DRR006412 2 0.0451 0.918 0.008 0.988 0.000 0.000 0.004
#> DRR006413 1 0.2054 0.937 0.920 0.000 0.028 0.000 0.052
#> DRR006414 3 0.3366 0.573 0.232 0.000 0.768 0.000 0.000
#> DRR006415 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006416 1 0.0451 0.952 0.988 0.000 0.004 0.008 0.000
#> DRR006417 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006418 1 0.0510 0.954 0.984 0.000 0.016 0.000 0.000
#> DRR006419 1 0.1608 0.931 0.928 0.000 0.072 0.000 0.000
#> DRR006420 1 0.2771 0.878 0.860 0.000 0.128 0.000 0.012
#> DRR006421 3 0.3492 0.863 0.000 0.000 0.796 0.016 0.188
#> DRR006422 1 0.1243 0.947 0.960 0.004 0.028 0.008 0.000
#> DRR006423 2 0.0162 0.920 0.000 0.996 0.000 0.000 0.004
#> DRR006424 1 0.1478 0.944 0.936 0.000 0.000 0.000 0.064
#> DRR006425 2 0.3279 0.807 0.004 0.868 0.064 0.052 0.012
#> DRR006426 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006427 2 0.0162 0.920 0.000 0.996 0.000 0.000 0.004
#> DRR006428 3 0.2966 0.867 0.000 0.000 0.816 0.000 0.184
#> DRR006429 2 0.2938 0.808 0.048 0.880 0.064 0.008 0.000
#> DRR006430 1 0.1544 0.943 0.932 0.000 0.000 0.000 0.068
#> DRR006431 1 0.2046 0.944 0.916 0.000 0.016 0.000 0.068
#> DRR006432 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006433 3 0.4133 0.853 0.000 0.000 0.768 0.052 0.180
#> DRR006434 3 0.4767 0.311 0.000 0.420 0.560 0.000 0.020
#> DRR006435 5 0.3857 1.000 0.000 0.312 0.000 0.000 0.688
#> DRR006436 5 0.3857 1.000 0.000 0.312 0.000 0.000 0.688
#> DRR006437 1 0.1701 0.942 0.936 0.000 0.016 0.000 0.048
#> DRR006438 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006439 3 0.2179 0.870 0.004 0.000 0.896 0.000 0.100
#> DRR006440 3 0.3988 0.853 0.000 0.036 0.768 0.000 0.196
#> DRR006441 2 0.0451 0.918 0.008 0.988 0.000 0.004 0.000
#> DRR006442 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006443 3 0.3988 0.853 0.000 0.036 0.768 0.000 0.196
#> DRR006444 5 0.3857 1.000 0.000 0.312 0.000 0.000 0.688
#> DRR006445 1 0.1270 0.939 0.948 0.000 0.000 0.000 0.052
#> DRR006446 2 0.0451 0.918 0.008 0.988 0.000 0.000 0.004
#> DRR006447 1 0.0798 0.953 0.976 0.000 0.008 0.000 0.016
#> DRR006448 4 0.0486 0.975 0.004 0.000 0.004 0.988 0.004
#> DRR006449 1 0.0932 0.953 0.972 0.000 0.020 0.004 0.004
#> DRR006450 1 0.1557 0.941 0.940 0.000 0.008 0.000 0.052
#> DRR006451 4 0.0324 0.976 0.000 0.000 0.004 0.992 0.004
#> DRR006452 1 0.1557 0.941 0.940 0.000 0.008 0.000 0.052
#> DRR006453 1 0.0290 0.952 0.992 0.000 0.000 0.000 0.008
#> DRR006454 1 0.1455 0.941 0.952 0.032 0.008 0.008 0.000
#> DRR006455 5 0.3857 1.000 0.000 0.312 0.000 0.000 0.688
#> DRR006456 3 0.2966 0.867 0.000 0.000 0.816 0.000 0.184
#> DRR006457 3 0.3282 0.865 0.000 0.000 0.804 0.008 0.188
#> DRR006458 1 0.1478 0.943 0.936 0.000 0.000 0.000 0.064
#> DRR006459 1 0.2770 0.914 0.880 0.000 0.076 0.000 0.044
#> DRR006460 2 0.0671 0.920 0.000 0.980 0.004 0.000 0.016
#> DRR006461 3 0.4101 0.850 0.000 0.048 0.768 0.000 0.184
#> DRR006462 1 0.0898 0.953 0.972 0.000 0.020 0.000 0.008
#> DRR006463 3 0.3988 0.853 0.000 0.036 0.768 0.000 0.196
#> DRR006464 2 0.3620 0.737 0.048 0.832 0.112 0.008 0.000
#> DRR006465 1 0.0898 0.954 0.972 0.000 0.008 0.000 0.020
#> DRR006466 3 0.4106 0.854 0.000 0.028 0.768 0.008 0.196
#> DRR006467 1 0.1830 0.944 0.924 0.000 0.008 0.000 0.068
#> DRR006468 5 0.3857 1.000 0.000 0.312 0.000 0.000 0.688
#> DRR006469 2 0.0162 0.921 0.000 0.996 0.000 0.004 0.000
#> DRR006470 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006471 1 0.1981 0.943 0.920 0.000 0.016 0.000 0.064
#> DRR006472 3 0.1186 0.849 0.020 0.008 0.964 0.008 0.000
#> DRR006473 2 0.0486 0.920 0.004 0.988 0.004 0.000 0.004
#> DRR006474 2 0.0162 0.922 0.000 0.996 0.000 0.000 0.004
#> DRR006475 1 0.1981 0.943 0.920 0.000 0.016 0.000 0.064
#> DRR006476 3 0.5182 0.344 0.012 0.404 0.564 0.008 0.012
#> DRR006477 3 0.3841 0.857 0.000 0.000 0.780 0.032 0.188
#> DRR006478 1 0.1557 0.948 0.940 0.000 0.008 0.000 0.052
#> DRR006479 3 0.2233 0.765 0.104 0.000 0.892 0.000 0.004
#> DRR006480 1 0.1981 0.943 0.920 0.000 0.016 0.000 0.064
#> DRR006481 3 0.3282 0.865 0.000 0.000 0.804 0.008 0.188
#> DRR006482 1 0.0566 0.952 0.984 0.000 0.004 0.000 0.012
#> DRR006483 1 0.2171 0.940 0.912 0.000 0.024 0.000 0.064
#> DRR006484 3 0.2966 0.867 0.000 0.000 0.816 0.000 0.184
#> DRR006485 3 0.3988 0.853 0.000 0.036 0.768 0.000 0.196
#> DRR006486 1 0.1557 0.943 0.940 0.000 0.052 0.000 0.008
#> DRR006487 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006488 5 0.3857 1.000 0.000 0.312 0.000 0.000 0.688
#> DRR006489 1 0.0693 0.953 0.980 0.000 0.008 0.000 0.012
#> DRR006490 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006491 3 0.0162 0.858 0.004 0.000 0.996 0.000 0.000
#> DRR006492 1 0.0992 0.954 0.968 0.000 0.008 0.000 0.024
#> DRR006493 3 0.3123 0.867 0.004 0.000 0.812 0.000 0.184
#> DRR006494 1 0.2795 0.920 0.880 0.000 0.056 0.000 0.064
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 2 0.2302 0.809 0.000 0.872 0.120 0.000 0.000 NA
#> DRR006375 1 0.3337 0.774 0.736 0.000 0.004 0.000 0.000 NA
#> DRR006376 4 0.0260 0.973 0.000 0.008 0.000 0.992 0.000 NA
#> DRR006377 3 0.0520 0.810 0.000 0.000 0.984 0.008 0.000 NA
#> DRR006378 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006379 4 0.0260 0.973 0.000 0.008 0.000 0.992 0.000 NA
#> DRR006380 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006381 1 0.2190 0.822 0.900 0.000 0.000 0.000 0.040 NA
#> DRR006382 3 0.2451 0.785 0.000 0.068 0.888 0.004 0.000 NA
#> DRR006383 3 0.4189 0.676 0.004 0.000 0.552 0.008 0.000 NA
#> DRR006384 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006385 1 0.1865 0.832 0.920 0.000 0.000 0.000 0.040 NA
#> DRR006386 5 0.1007 1.000 0.000 0.044 0.000 0.000 0.956 NA
#> DRR006387 1 0.1049 0.851 0.960 0.008 0.000 0.032 0.000 NA
#> DRR006388 1 0.1245 0.845 0.952 0.000 0.000 0.000 0.016 NA
#> DRR006389 1 0.1245 0.845 0.952 0.000 0.000 0.000 0.016 NA
#> DRR006390 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006391 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006392 1 0.3547 0.731 0.668 0.000 0.000 0.000 0.000 NA
#> DRR006393 1 0.1765 0.844 0.904 0.000 0.000 0.000 0.000 NA
#> DRR006394 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006395 4 0.2400 0.809 0.000 0.008 0.116 0.872 0.000 NA
#> DRR006396 1 0.1865 0.832 0.920 0.000 0.000 0.000 0.040 NA
#> DRR006397 1 0.0520 0.852 0.984 0.000 0.000 0.000 0.008 NA
#> DRR006398 1 0.0520 0.852 0.984 0.000 0.000 0.000 0.008 NA
#> DRR006399 4 0.0260 0.973 0.000 0.008 0.000 0.992 0.000 NA
#> DRR006400 4 0.0260 0.973 0.000 0.008 0.000 0.992 0.000 NA
#> DRR006401 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006402 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006403 4 0.0260 0.973 0.000 0.008 0.000 0.992 0.000 NA
#> DRR006404 4 0.0260 0.973 0.000 0.008 0.000 0.992 0.000 NA
#> DRR006405 4 0.0665 0.966 0.008 0.008 0.004 0.980 0.000 NA
#> DRR006406 4 0.0260 0.973 0.000 0.008 0.000 0.992 0.000 NA
#> DRR006407 4 0.0405 0.971 0.000 0.008 0.004 0.988 0.000 NA
#> DRR006408 4 0.0363 0.971 0.000 0.012 0.000 0.988 0.000 NA
#> DRR006409 3 0.1498 0.808 0.028 0.000 0.940 0.000 0.000 NA
#> DRR006410 1 0.1049 0.851 0.960 0.008 0.000 0.032 0.000 NA
#> DRR006411 1 0.4741 0.430 0.600 0.344 0.052 0.000 0.004 NA
#> DRR006412 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006413 1 0.2190 0.822 0.900 0.000 0.000 0.000 0.040 NA
#> DRR006414 3 0.5659 0.575 0.168 0.000 0.496 0.000 0.000 NA
#> DRR006415 3 0.4147 0.676 0.000 0.000 0.552 0.012 0.000 NA
#> DRR006416 1 0.0405 0.852 0.988 0.000 0.000 0.000 0.004 NA
#> DRR006417 3 0.4147 0.676 0.000 0.000 0.552 0.012 0.000 NA
#> DRR006418 1 0.0000 0.852 1.000 0.000 0.000 0.000 0.000 NA
#> DRR006419 1 0.1367 0.846 0.944 0.000 0.044 0.000 0.000 NA
#> DRR006420 1 0.4022 0.622 0.708 0.000 0.252 0.000 0.000 NA
#> DRR006421 3 0.0146 0.812 0.000 0.000 0.996 0.004 0.000 NA
#> DRR006422 1 0.1370 0.849 0.948 0.012 0.036 0.004 0.000 NA
#> DRR006423 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006424 1 0.2964 0.804 0.792 0.000 0.000 0.000 0.004 NA
#> DRR006425 2 0.1779 0.864 0.000 0.920 0.064 0.016 0.000 NA
#> DRR006426 3 0.2234 0.798 0.000 0.000 0.872 0.004 0.000 NA
#> DRR006427 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006428 3 0.0146 0.813 0.004 0.000 0.996 0.000 0.000 NA
#> DRR006429 2 0.1411 0.873 0.000 0.936 0.060 0.004 0.000 NA
#> DRR006430 1 0.3563 0.728 0.664 0.000 0.000 0.000 0.000 NA
#> DRR006431 1 0.3804 0.652 0.576 0.000 0.000 0.000 0.000 NA
#> DRR006432 3 0.4144 0.695 0.000 0.004 0.580 0.008 0.000 NA
#> DRR006433 3 0.0291 0.812 0.000 0.000 0.992 0.004 0.000 NA
#> DRR006434 2 0.3774 0.493 0.000 0.664 0.328 0.000 0.000 NA
#> DRR006435 5 0.1007 1.000 0.000 0.044 0.000 0.000 0.956 NA
#> DRR006436 5 0.1007 1.000 0.000 0.044 0.000 0.000 0.956 NA
#> DRR006437 1 0.1865 0.834 0.920 0.000 0.000 0.000 0.040 NA
#> DRR006438 3 0.3862 0.710 0.004 0.000 0.608 0.000 0.000 NA
#> DRR006439 3 0.1297 0.812 0.012 0.000 0.948 0.000 0.000 NA
#> DRR006440 3 0.1471 0.786 0.000 0.004 0.932 0.000 0.000 NA
#> DRR006441 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006442 3 0.3728 0.729 0.004 0.000 0.652 0.000 0.000 NA
#> DRR006443 3 0.1471 0.786 0.000 0.004 0.932 0.000 0.000 NA
#> DRR006444 5 0.1007 1.000 0.000 0.044 0.000 0.000 0.956 NA
#> DRR006445 1 0.1794 0.835 0.924 0.000 0.000 0.000 0.040 NA
#> DRR006446 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006447 1 0.0622 0.850 0.980 0.000 0.000 0.000 0.008 NA
#> DRR006448 4 0.1333 0.919 0.048 0.008 0.000 0.944 0.000 NA
#> DRR006449 1 0.1049 0.851 0.960 0.008 0.000 0.032 0.000 NA
#> DRR006450 1 0.1865 0.832 0.920 0.000 0.000 0.000 0.040 NA
#> DRR006451 4 0.0891 0.951 0.024 0.008 0.000 0.968 0.000 NA
#> DRR006452 1 0.2066 0.826 0.908 0.000 0.000 0.000 0.040 NA
#> DRR006453 1 0.0000 0.852 1.000 0.000 0.000 0.000 0.000 NA
#> DRR006454 1 0.1777 0.839 0.928 0.044 0.024 0.004 0.000 NA
#> DRR006455 5 0.1007 1.000 0.000 0.044 0.000 0.000 0.956 NA
#> DRR006456 3 0.0146 0.813 0.004 0.000 0.996 0.000 0.000 NA
#> DRR006457 3 0.0146 0.813 0.004 0.000 0.996 0.000 0.000 NA
#> DRR006458 1 0.3810 0.649 0.572 0.000 0.000 0.000 0.000 NA
#> DRR006459 1 0.5000 0.643 0.580 0.000 0.088 0.000 0.000 NA
#> DRR006460 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006461 3 0.1643 0.770 0.000 0.068 0.924 0.000 0.000 NA
#> DRR006462 1 0.0935 0.851 0.964 0.004 0.000 0.032 0.000 NA
#> DRR006463 3 0.1471 0.786 0.000 0.004 0.932 0.000 0.000 NA
#> DRR006464 2 0.2135 0.797 0.000 0.872 0.128 0.000 0.000 NA
#> DRR006465 1 0.1075 0.851 0.952 0.000 0.000 0.000 0.000 NA
#> DRR006466 3 0.0291 0.812 0.000 0.000 0.992 0.004 0.000 NA
#> DRR006467 1 0.3351 0.759 0.712 0.000 0.000 0.000 0.000 NA
#> DRR006468 5 0.1007 1.000 0.000 0.044 0.000 0.000 0.956 NA
#> DRR006469 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006470 3 0.4246 0.693 0.008 0.000 0.576 0.008 0.000 NA
#> DRR006471 1 0.3446 0.747 0.692 0.000 0.000 0.000 0.000 NA
#> DRR006472 3 0.4841 0.466 0.320 0.008 0.620 0.004 0.000 NA
#> DRR006473 2 0.0000 0.927 0.000 1.000 0.000 0.000 0.000 NA
#> DRR006474 2 0.0146 0.924 0.000 0.996 0.004 0.000 0.000 NA
#> DRR006475 1 0.3515 0.736 0.676 0.000 0.000 0.000 0.000 NA
#> DRR006476 2 0.3930 0.181 0.000 0.576 0.420 0.004 0.000 NA
#> DRR006477 3 0.0291 0.812 0.000 0.000 0.992 0.004 0.000 NA
#> DRR006478 1 0.2003 0.838 0.884 0.000 0.000 0.000 0.000 NA
#> DRR006479 3 0.5475 0.617 0.148 0.000 0.536 0.000 0.000 NA
#> DRR006480 1 0.3810 0.649 0.572 0.000 0.000 0.000 0.000 NA
#> DRR006481 3 0.0146 0.813 0.004 0.000 0.996 0.000 0.000 NA
#> DRR006482 1 0.0622 0.851 0.980 0.000 0.000 0.000 0.008 NA
#> DRR006483 1 0.3804 0.653 0.576 0.000 0.000 0.000 0.000 NA
#> DRR006484 3 0.0146 0.813 0.004 0.000 0.996 0.000 0.000 NA
#> DRR006485 3 0.1471 0.786 0.000 0.004 0.932 0.000 0.000 NA
#> DRR006486 1 0.1528 0.846 0.936 0.000 0.048 0.000 0.000 NA
#> DRR006487 3 0.3852 0.713 0.004 0.000 0.612 0.000 0.000 NA
#> DRR006488 5 0.1007 1.000 0.000 0.044 0.000 0.000 0.956 NA
#> DRR006489 1 0.1682 0.855 0.928 0.000 0.000 0.000 0.020 NA
#> DRR006490 3 0.3862 0.710 0.004 0.000 0.608 0.000 0.000 NA
#> DRR006491 3 0.3852 0.713 0.004 0.000 0.612 0.000 0.000 NA
#> DRR006492 1 0.1814 0.843 0.900 0.000 0.000 0.000 0.000 NA
#> DRR006493 3 0.0603 0.814 0.004 0.000 0.980 0.000 0.000 NA
#> DRR006494 1 0.3804 0.652 0.576 0.000 0.000 0.000 0.000 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 16187 rows and 121 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.669 0.826 0.928 0.4695 0.533 0.533
#> 3 3 0.710 0.803 0.911 0.3984 0.717 0.510
#> 4 4 0.604 0.685 0.834 0.1239 0.811 0.518
#> 5 5 0.605 0.628 0.780 0.0773 0.853 0.511
#> 6 6 0.598 0.476 0.676 0.0386 0.899 0.569
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
#> DRR006374 1 0.9850 0.2355 0.572 0.428
#> DRR006375 1 0.4690 0.8546 0.900 0.100
#> DRR006376 2 0.0000 0.9094 0.000 1.000
#> DRR006377 2 0.0000 0.9094 0.000 1.000
#> DRR006378 2 0.0000 0.9094 0.000 1.000
#> DRR006379 2 0.0000 0.9094 0.000 1.000
#> DRR006380 2 0.0000 0.9094 0.000 1.000
#> DRR006381 1 0.0000 0.9229 1.000 0.000
#> DRR006382 1 0.0000 0.9229 1.000 0.000
#> DRR006383 1 0.0000 0.9229 1.000 0.000
#> DRR006384 2 0.0000 0.9094 0.000 1.000
#> DRR006385 1 0.0000 0.9229 1.000 0.000
#> DRR006386 1 0.8955 0.5255 0.688 0.312
#> DRR006387 2 0.8016 0.6617 0.244 0.756
#> DRR006388 1 0.2236 0.9049 0.964 0.036
#> DRR006389 1 0.1843 0.9102 0.972 0.028
#> DRR006390 2 0.0000 0.9094 0.000 1.000
#> DRR006391 2 0.0000 0.9094 0.000 1.000
#> DRR006392 1 0.0000 0.9229 1.000 0.000
#> DRR006393 1 0.1414 0.9159 0.980 0.020
#> DRR006394 2 0.0000 0.9094 0.000 1.000
#> DRR006395 2 0.0000 0.9094 0.000 1.000
#> DRR006396 1 0.2236 0.9050 0.964 0.036
#> DRR006397 1 0.6148 0.8016 0.848 0.152
#> DRR006398 1 0.4939 0.8474 0.892 0.108
#> DRR006399 2 0.0000 0.9094 0.000 1.000
#> DRR006400 2 0.0000 0.9094 0.000 1.000
#> DRR006401 2 0.0000 0.9094 0.000 1.000
#> DRR006402 2 0.0000 0.9094 0.000 1.000
#> DRR006403 2 0.0000 0.9094 0.000 1.000
#> DRR006404 2 0.0000 0.9094 0.000 1.000
#> DRR006405 2 0.0000 0.9094 0.000 1.000
#> DRR006406 2 0.0000 0.9094 0.000 1.000
#> DRR006407 2 0.0000 0.9094 0.000 1.000
#> DRR006408 2 0.0000 0.9094 0.000 1.000
#> DRR006409 1 0.9323 0.4767 0.652 0.348
#> DRR006410 2 0.6712 0.7558 0.176 0.824
#> DRR006411 2 0.4431 0.8459 0.092 0.908
#> DRR006412 2 0.0000 0.9094 0.000 1.000
#> DRR006413 1 0.0000 0.9229 1.000 0.000
#> DRR006414 1 0.0000 0.9229 1.000 0.000
#> DRR006415 1 0.0000 0.9229 1.000 0.000
#> DRR006416 1 0.0000 0.9229 1.000 0.000
#> DRR006417 1 0.0000 0.9229 1.000 0.000
#> DRR006418 1 0.0000 0.9229 1.000 0.000
#> DRR006419 1 0.0000 0.9229 1.000 0.000
#> DRR006420 1 0.0376 0.9220 0.996 0.004
#> DRR006421 2 0.5842 0.7987 0.140 0.860
#> DRR006422 1 0.0376 0.9220 0.996 0.004
#> DRR006423 1 0.9954 0.1629 0.540 0.460
#> DRR006424 1 0.0000 0.9229 1.000 0.000
#> DRR006425 2 0.0000 0.9094 0.000 1.000
#> DRR006426 1 0.4815 0.8453 0.896 0.104
#> DRR006427 1 0.0938 0.9191 0.988 0.012
#> DRR006428 1 0.3114 0.8906 0.944 0.056
#> DRR006429 1 0.9754 0.3297 0.592 0.408
#> DRR006430 1 0.0000 0.9229 1.000 0.000
#> DRR006431 1 0.1184 0.9167 0.984 0.016
#> DRR006432 1 0.2043 0.9076 0.968 0.032
#> DRR006433 2 0.0000 0.9094 0.000 1.000
#> DRR006434 1 0.9954 0.1221 0.540 0.460
#> DRR006435 1 0.9580 0.3646 0.620 0.380
#> DRR006436 1 0.0000 0.9229 1.000 0.000
#> DRR006437 1 0.2236 0.9050 0.964 0.036
#> DRR006438 1 0.0376 0.9220 0.996 0.004
#> DRR006439 1 0.6048 0.8026 0.852 0.148
#> DRR006440 2 0.9686 0.3534 0.396 0.604
#> DRR006441 2 0.0938 0.9025 0.012 0.988
#> DRR006442 1 0.0000 0.9229 1.000 0.000
#> DRR006443 2 0.7883 0.6708 0.236 0.764
#> DRR006444 1 0.0000 0.9229 1.000 0.000
#> DRR006445 1 0.0000 0.9229 1.000 0.000
#> DRR006446 2 0.0000 0.9094 0.000 1.000
#> DRR006447 1 0.0000 0.9229 1.000 0.000
#> DRR006448 2 0.0000 0.9094 0.000 1.000
#> DRR006449 2 0.6531 0.7664 0.168 0.832
#> DRR006450 1 0.0000 0.9229 1.000 0.000
#> DRR006451 2 0.0000 0.9094 0.000 1.000
#> DRR006452 1 0.0000 0.9229 1.000 0.000
#> DRR006453 1 0.0000 0.9229 1.000 0.000
#> DRR006454 2 0.2778 0.8789 0.048 0.952
#> DRR006455 1 0.0000 0.9229 1.000 0.000
#> DRR006456 1 0.0000 0.9229 1.000 0.000
#> DRR006457 1 0.9427 0.4289 0.640 0.360
#> DRR006458 1 0.0000 0.9229 1.000 0.000
#> DRR006459 1 0.0376 0.9220 0.996 0.004
#> DRR006460 2 0.0000 0.9094 0.000 1.000
#> DRR006461 1 0.8763 0.5645 0.704 0.296
#> DRR006462 2 0.9129 0.5084 0.328 0.672
#> DRR006463 2 0.9775 0.3128 0.412 0.588
#> DRR006464 1 0.2236 0.9049 0.964 0.036
#> DRR006465 1 0.1414 0.9159 0.980 0.020
#> DRR006466 2 0.0000 0.9094 0.000 1.000
#> DRR006467 1 0.0000 0.9229 1.000 0.000
#> DRR006468 1 0.9710 0.3157 0.600 0.400
#> DRR006469 2 0.8144 0.6559 0.252 0.748
#> DRR006470 1 0.0672 0.9207 0.992 0.008
#> DRR006471 1 0.0000 0.9229 1.000 0.000
#> DRR006472 1 0.0672 0.9207 0.992 0.008
#> DRR006473 1 0.0000 0.9229 1.000 0.000
#> DRR006474 1 0.8144 0.6572 0.748 0.252
#> DRR006475 1 0.0000 0.9229 1.000 0.000
#> DRR006476 2 0.0000 0.9094 0.000 1.000
#> DRR006477 2 0.0000 0.9094 0.000 1.000
#> DRR006478 1 0.1843 0.9124 0.972 0.028
#> DRR006479 1 0.0376 0.9220 0.996 0.004
#> DRR006480 1 0.0000 0.9229 1.000 0.000
#> DRR006481 2 0.9998 0.0386 0.492 0.508
#> DRR006482 1 0.1843 0.9114 0.972 0.028
#> DRR006483 1 0.0000 0.9229 1.000 0.000
#> DRR006484 1 0.4298 0.8610 0.912 0.088
#> DRR006485 2 0.9970 0.1338 0.468 0.532
#> DRR006486 1 0.0000 0.9229 1.000 0.000
#> DRR006487 1 0.0000 0.9229 1.000 0.000
#> DRR006488 1 0.0000 0.9229 1.000 0.000
#> DRR006489 1 0.0000 0.9229 1.000 0.000
#> DRR006490 1 0.0000 0.9229 1.000 0.000
#> DRR006491 1 0.0000 0.9229 1.000 0.000
#> DRR006492 1 0.1184 0.9182 0.984 0.016
#> DRR006493 1 0.0376 0.9220 0.996 0.004
#> DRR006494 1 0.0000 0.9229 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> DRR006374 3 0.0592 0.857 0.000 0.012 0.988
#> DRR006375 1 0.2711 0.867 0.912 0.088 0.000
#> DRR006376 2 0.0237 0.902 0.000 0.996 0.004
#> DRR006377 2 0.1643 0.884 0.000 0.956 0.044
#> DRR006378 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006379 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006380 2 0.0747 0.899 0.000 0.984 0.016
#> DRR006381 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006382 3 0.0892 0.859 0.020 0.000 0.980
#> DRR006383 1 0.2066 0.889 0.940 0.000 0.060
#> DRR006384 2 0.1289 0.892 0.000 0.968 0.032
#> DRR006385 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006386 1 0.3879 0.795 0.848 0.152 0.000
#> DRR006387 2 0.5497 0.595 0.292 0.708 0.000
#> DRR006388 1 0.1031 0.917 0.976 0.024 0.000
#> DRR006389 1 0.1031 0.917 0.976 0.024 0.000
#> DRR006390 3 0.6126 0.305 0.000 0.400 0.600
#> DRR006391 3 0.6225 0.213 0.000 0.432 0.568
#> DRR006392 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006393 1 0.0592 0.922 0.988 0.012 0.000
#> DRR006394 2 0.3412 0.816 0.000 0.876 0.124
#> DRR006395 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006396 1 0.0892 0.919 0.980 0.020 0.000
#> DRR006397 1 0.3619 0.815 0.864 0.136 0.000
#> DRR006398 1 0.3038 0.851 0.896 0.104 0.000
#> DRR006399 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006400 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006401 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006402 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006403 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006404 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006405 2 0.0237 0.902 0.004 0.996 0.000
#> DRR006406 2 0.0237 0.902 0.004 0.996 0.000
#> DRR006407 2 0.0892 0.897 0.000 0.980 0.020
#> DRR006408 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006409 1 0.6359 0.283 0.592 0.404 0.004
#> DRR006410 2 0.4750 0.716 0.216 0.784 0.000
#> DRR006411 2 0.7250 0.338 0.032 0.572 0.396
#> DRR006412 2 0.6180 0.286 0.000 0.584 0.416
#> DRR006413 1 0.0237 0.924 0.996 0.000 0.004
#> DRR006414 1 0.2261 0.883 0.932 0.000 0.068
#> DRR006415 3 0.1860 0.849 0.052 0.000 0.948
#> DRR006416 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006417 3 0.0237 0.859 0.004 0.000 0.996
#> DRR006418 1 0.0892 0.917 0.980 0.000 0.020
#> DRR006419 1 0.5882 0.456 0.652 0.000 0.348
#> DRR006420 1 0.2537 0.873 0.920 0.000 0.080
#> DRR006421 3 0.0892 0.854 0.000 0.020 0.980
#> DRR006422 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006423 3 0.1525 0.853 0.004 0.032 0.964
#> DRR006424 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006425 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006426 3 0.0424 0.860 0.008 0.000 0.992
#> DRR006427 3 0.0000 0.859 0.000 0.000 1.000
#> DRR006428 3 0.3192 0.811 0.112 0.000 0.888
#> DRR006429 3 0.9653 0.239 0.232 0.312 0.456
#> DRR006430 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006431 1 0.0592 0.922 0.988 0.012 0.000
#> DRR006432 3 0.1031 0.859 0.024 0.000 0.976
#> DRR006433 3 0.5859 0.461 0.000 0.344 0.656
#> DRR006434 3 0.0424 0.858 0.000 0.008 0.992
#> DRR006435 1 0.7158 0.336 0.596 0.032 0.372
#> DRR006436 3 0.6286 0.117 0.464 0.000 0.536
#> DRR006437 1 0.0892 0.919 0.980 0.020 0.000
#> DRR006438 3 0.2356 0.839 0.072 0.000 0.928
#> DRR006439 3 0.5595 0.675 0.228 0.016 0.756
#> DRR006440 3 0.0592 0.857 0.000 0.012 0.988
#> DRR006441 2 0.4654 0.719 0.000 0.792 0.208
#> DRR006442 3 0.5859 0.482 0.344 0.000 0.656
#> DRR006443 3 0.0747 0.855 0.000 0.016 0.984
#> DRR006444 1 0.4931 0.700 0.768 0.000 0.232
#> DRR006445 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006446 2 0.1289 0.891 0.000 0.968 0.032
#> DRR006447 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006448 2 0.0747 0.895 0.016 0.984 0.000
#> DRR006449 2 0.5016 0.682 0.240 0.760 0.000
#> DRR006450 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006451 2 0.0000 0.903 0.000 1.000 0.000
#> DRR006452 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006453 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006454 2 0.3987 0.816 0.108 0.872 0.020
#> DRR006455 1 0.0237 0.924 0.996 0.000 0.004
#> DRR006456 3 0.1163 0.858 0.028 0.000 0.972
#> DRR006457 3 0.0237 0.859 0.000 0.004 0.996
#> DRR006458 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006459 1 0.0237 0.924 0.996 0.004 0.000
#> DRR006460 2 0.0237 0.903 0.000 0.996 0.004
#> DRR006461 3 0.0237 0.859 0.000 0.004 0.996
#> DRR006462 2 0.6192 0.296 0.420 0.580 0.000
#> DRR006463 3 0.0424 0.858 0.000 0.008 0.992
#> DRR006464 3 0.3482 0.804 0.128 0.000 0.872
#> DRR006465 1 0.0592 0.922 0.988 0.012 0.000
#> DRR006466 3 0.3752 0.752 0.000 0.144 0.856
#> DRR006467 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006468 3 0.2050 0.854 0.020 0.028 0.952
#> DRR006469 3 0.1643 0.843 0.000 0.044 0.956
#> DRR006470 3 0.1529 0.855 0.040 0.000 0.960
#> DRR006471 1 0.0237 0.924 0.996 0.000 0.004
#> DRR006472 3 0.5431 0.608 0.284 0.000 0.716
#> DRR006473 1 0.5706 0.524 0.680 0.000 0.320
#> DRR006474 3 0.3550 0.825 0.080 0.024 0.896
#> DRR006475 1 0.0237 0.924 0.996 0.000 0.004
#> DRR006476 2 0.1031 0.896 0.000 0.976 0.024
#> DRR006477 3 0.5810 0.491 0.000 0.336 0.664
#> DRR006478 1 0.0747 0.920 0.984 0.016 0.000
#> DRR006479 1 0.5254 0.639 0.736 0.000 0.264
#> DRR006480 1 0.0237 0.924 0.996 0.000 0.004
#> DRR006481 3 0.0424 0.858 0.000 0.008 0.992
#> DRR006482 1 0.0747 0.921 0.984 0.016 0.000
#> DRR006483 1 0.0237 0.924 0.996 0.000 0.004
#> DRR006484 3 0.0237 0.859 0.004 0.000 0.996
#> DRR006485 3 0.0237 0.859 0.000 0.004 0.996
#> DRR006486 1 0.0747 0.919 0.984 0.000 0.016
#> DRR006487 3 0.1163 0.858 0.028 0.000 0.972
#> DRR006488 1 0.3752 0.809 0.856 0.000 0.144
#> DRR006489 1 0.0000 0.925 1.000 0.000 0.000
#> DRR006490 1 0.5621 0.557 0.692 0.000 0.308
#> DRR006491 3 0.5810 0.498 0.336 0.000 0.664
#> DRR006492 1 0.0592 0.922 0.988 0.012 0.000
#> DRR006493 3 0.0747 0.860 0.016 0.000 0.984
#> DRR006494 1 0.0424 0.923 0.992 0.000 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> DRR006374 3 0.1770 0.7834 0.016 0.016 0.952 0.016
#> DRR006375 1 0.1821 0.8206 0.948 0.012 0.008 0.032
#> DRR006376 4 0.1118 0.8598 0.000 0.036 0.000 0.964
#> DRR006377 4 0.1520 0.8541 0.000 0.024 0.020 0.956
#> DRR006378 2 0.2882 0.7302 0.024 0.892 0.000 0.084
#> DRR006379 4 0.0707 0.8654 0.000 0.020 0.000 0.980
#> DRR006380 4 0.1256 0.8579 0.000 0.008 0.028 0.964
#> DRR006381 1 0.1867 0.7969 0.928 0.072 0.000 0.000
#> DRR006382 3 0.2412 0.7853 0.084 0.008 0.908 0.000
#> DRR006383 1 0.5150 0.3076 0.596 0.000 0.396 0.008
#> DRR006384 4 0.1042 0.8613 0.000 0.008 0.020 0.972
#> DRR006385 1 0.3873 0.6567 0.772 0.228 0.000 0.000
#> DRR006386 1 0.4877 0.7262 0.800 0.092 0.012 0.096
#> DRR006387 4 0.7273 0.0535 0.400 0.148 0.000 0.452
#> DRR006388 2 0.4948 0.2627 0.440 0.560 0.000 0.000
#> DRR006389 2 0.4972 0.2126 0.456 0.544 0.000 0.000
#> DRR006390 2 0.2988 0.7162 0.000 0.876 0.112 0.012
#> DRR006391 2 0.2741 0.7271 0.000 0.892 0.096 0.012
#> DRR006392 1 0.1296 0.8243 0.964 0.004 0.028 0.004
#> DRR006393 1 0.1471 0.8239 0.960 0.004 0.024 0.012
#> DRR006394 2 0.4234 0.6938 0.000 0.816 0.132 0.052
#> DRR006395 4 0.0188 0.8656 0.004 0.000 0.000 0.996
#> DRR006396 1 0.2921 0.7526 0.860 0.140 0.000 0.000
#> DRR006397 2 0.4391 0.6225 0.252 0.740 0.000 0.008
#> DRR006398 2 0.4511 0.6029 0.268 0.724 0.000 0.008
#> DRR006399 4 0.0336 0.8667 0.000 0.008 0.000 0.992
#> DRR006400 4 0.0336 0.8667 0.000 0.008 0.000 0.992
#> DRR006401 4 0.0469 0.8664 0.000 0.012 0.000 0.988
#> DRR006402 4 0.0469 0.8664 0.000 0.012 0.000 0.988
#> DRR006403 4 0.0336 0.8667 0.000 0.008 0.000 0.992
#> DRR006404 4 0.0000 0.8658 0.000 0.000 0.000 1.000
#> DRR006405 4 0.0927 0.8617 0.008 0.016 0.000 0.976
#> DRR006406 4 0.0779 0.8653 0.004 0.016 0.000 0.980
#> DRR006407 4 0.2149 0.8195 0.000 0.088 0.000 0.912
#> DRR006408 4 0.0469 0.8664 0.000 0.012 0.000 0.988
#> DRR006409 4 0.6845 0.3712 0.308 0.000 0.128 0.564
#> DRR006410 4 0.4464 0.6538 0.208 0.024 0.000 0.768
#> DRR006411 2 0.0992 0.7527 0.012 0.976 0.008 0.004
#> DRR006412 2 0.1970 0.7433 0.000 0.932 0.060 0.008
#> DRR006413 1 0.2011 0.7939 0.920 0.080 0.000 0.000
#> DRR006414 1 0.4855 0.4344 0.644 0.004 0.352 0.000
#> DRR006415 3 0.3217 0.7745 0.128 0.012 0.860 0.000
#> DRR006416 1 0.2814 0.7603 0.868 0.132 0.000 0.000
#> DRR006417 2 0.4855 0.2588 0.000 0.600 0.400 0.000
#> DRR006418 2 0.4543 0.5261 0.324 0.676 0.000 0.000
#> DRR006419 2 0.4464 0.6806 0.208 0.768 0.024 0.000
#> DRR006420 1 0.2124 0.8224 0.932 0.040 0.028 0.000
#> DRR006421 3 0.2467 0.7653 0.004 0.024 0.920 0.052
#> DRR006422 1 0.2587 0.8090 0.908 0.004 0.076 0.012
#> DRR006423 2 0.2408 0.7280 0.000 0.896 0.104 0.000
#> DRR006424 1 0.0592 0.8200 0.984 0.016 0.000 0.000
#> DRR006425 4 0.0336 0.8668 0.000 0.008 0.000 0.992
#> DRR006426 2 0.4188 0.5866 0.004 0.752 0.244 0.000
#> DRR006427 3 0.2319 0.7862 0.036 0.040 0.924 0.000
#> DRR006428 3 0.3764 0.7371 0.172 0.000 0.816 0.012
#> DRR006429 2 0.2421 0.7539 0.048 0.924 0.020 0.008
#> DRR006430 1 0.1109 0.8248 0.968 0.004 0.028 0.000
#> DRR006431 1 0.2413 0.8119 0.916 0.000 0.064 0.020
#> DRR006432 2 0.3498 0.6882 0.008 0.832 0.160 0.000
#> DRR006433 4 0.5427 0.2044 0.000 0.016 0.416 0.568
#> DRR006434 3 0.3402 0.6931 0.000 0.164 0.832 0.004
#> DRR006435 1 0.7337 0.2920 0.540 0.132 0.316 0.012
#> DRR006436 3 0.7469 0.2794 0.340 0.188 0.472 0.000
#> DRR006437 1 0.4164 0.5964 0.736 0.264 0.000 0.000
#> DRR006438 3 0.5395 0.7152 0.184 0.084 0.732 0.000
#> DRR006439 3 0.4482 0.6322 0.264 0.000 0.728 0.008
#> DRR006440 3 0.4483 0.5542 0.000 0.284 0.712 0.004
#> DRR006441 2 0.2368 0.7489 0.008 0.928 0.032 0.032
#> DRR006442 3 0.4193 0.6354 0.268 0.000 0.732 0.000
#> DRR006443 3 0.3450 0.6988 0.000 0.156 0.836 0.008
#> DRR006444 1 0.6039 0.3885 0.596 0.056 0.348 0.000
#> DRR006445 1 0.3907 0.6513 0.768 0.232 0.000 0.000
#> DRR006446 2 0.1488 0.7498 0.032 0.956 0.000 0.012
#> DRR006447 2 0.4948 0.2662 0.440 0.560 0.000 0.000
#> DRR006448 4 0.0657 0.8657 0.004 0.012 0.000 0.984
#> DRR006449 4 0.7063 0.2244 0.360 0.132 0.000 0.508
#> DRR006450 1 0.3942 0.6457 0.764 0.236 0.000 0.000
#> DRR006451 4 0.3257 0.7567 0.004 0.152 0.000 0.844
#> DRR006452 1 0.2868 0.7566 0.864 0.136 0.000 0.000
#> DRR006453 1 0.2469 0.7774 0.892 0.108 0.000 0.000
#> DRR006454 2 0.5613 0.6315 0.120 0.724 0.000 0.156
#> DRR006455 1 0.1637 0.8194 0.940 0.000 0.060 0.000
#> DRR006456 3 0.2814 0.7680 0.132 0.000 0.868 0.000
#> DRR006457 3 0.1305 0.7698 0.000 0.036 0.960 0.004
#> DRR006458 1 0.2441 0.8127 0.916 0.004 0.068 0.012
#> DRR006459 1 0.3808 0.7397 0.824 0.004 0.160 0.012
#> DRR006460 4 0.0188 0.8655 0.000 0.000 0.004 0.996
#> DRR006461 3 0.2099 0.7870 0.040 0.004 0.936 0.020
#> DRR006462 1 0.6828 0.4109 0.588 0.264 0.000 0.148
#> DRR006463 3 0.4343 0.5822 0.000 0.264 0.732 0.004
#> DRR006464 2 0.2282 0.7514 0.024 0.924 0.052 0.000
#> DRR006465 1 0.1114 0.8213 0.972 0.016 0.004 0.008
#> DRR006466 3 0.4988 0.5836 0.000 0.036 0.728 0.236
#> DRR006467 1 0.1004 0.8246 0.972 0.004 0.024 0.000
#> DRR006468 3 0.4606 0.7405 0.060 0.124 0.808 0.008
#> DRR006469 2 0.2714 0.7250 0.000 0.884 0.112 0.004
#> DRR006470 2 0.3852 0.6549 0.008 0.800 0.192 0.000
#> DRR006471 1 0.1406 0.8240 0.960 0.016 0.024 0.000
#> DRR006472 1 0.7344 0.2649 0.512 0.188 0.300 0.000
#> DRR006473 2 0.5169 0.6277 0.272 0.696 0.032 0.000
#> DRR006474 3 0.5209 0.7046 0.104 0.000 0.756 0.140
#> DRR006475 1 0.1610 0.8247 0.952 0.016 0.032 0.000
#> DRR006476 4 0.1118 0.8588 0.000 0.036 0.000 0.964
#> DRR006477 4 0.5497 0.0794 0.016 0.000 0.460 0.524
#> DRR006478 1 0.1762 0.8119 0.944 0.048 0.004 0.004
#> DRR006479 1 0.4483 0.5756 0.712 0.004 0.284 0.000
#> DRR006480 1 0.2266 0.8047 0.912 0.000 0.084 0.004
#> DRR006481 3 0.3105 0.7108 0.000 0.140 0.856 0.004
#> DRR006482 1 0.3852 0.7032 0.800 0.192 0.008 0.000
#> DRR006483 1 0.1953 0.8237 0.940 0.012 0.044 0.004
#> DRR006484 3 0.1706 0.7789 0.016 0.036 0.948 0.000
#> DRR006485 3 0.4454 0.5187 0.000 0.308 0.692 0.000
#> DRR006486 1 0.1970 0.8198 0.932 0.008 0.060 0.000
#> DRR006487 3 0.2542 0.7866 0.084 0.012 0.904 0.000
#> DRR006488 1 0.4840 0.6412 0.732 0.028 0.240 0.000
#> DRR006489 1 0.1022 0.8157 0.968 0.032 0.000 0.000
#> DRR006490 3 0.4989 0.1219 0.472 0.000 0.528 0.000
#> DRR006491 3 0.4193 0.6365 0.268 0.000 0.732 0.000
#> DRR006492 1 0.3289 0.7789 0.864 0.004 0.120 0.012
#> DRR006493 3 0.1767 0.7876 0.044 0.012 0.944 0.000
#> DRR006494 1 0.3672 0.7331 0.824 0.000 0.164 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> DRR006374 3 0.3269 0.758138 0.096 0.056 0.848 0.000 0.000
#> DRR006375 1 0.2868 0.697998 0.884 0.012 0.000 0.032 0.072
#> DRR006376 4 0.2103 0.873923 0.056 0.020 0.000 0.920 0.004
#> DRR006377 1 0.6106 0.088439 0.544 0.056 0.028 0.368 0.004
#> DRR006378 2 0.4087 0.779776 0.032 0.820 0.000 0.068 0.080
#> DRR006379 4 0.1560 0.882249 0.020 0.028 0.000 0.948 0.004
#> DRR006380 4 0.7114 0.396167 0.008 0.020 0.236 0.500 0.236
#> DRR006381 5 0.2520 0.667550 0.096 0.004 0.012 0.000 0.888
#> DRR006382 3 0.2124 0.745844 0.004 0.000 0.900 0.000 0.096
#> DRR006383 3 0.6058 0.241179 0.136 0.000 0.528 0.000 0.336
#> DRR006384 4 0.5672 0.687684 0.008 0.024 0.112 0.700 0.156
#> DRR006385 5 0.2370 0.677487 0.056 0.040 0.000 0.000 0.904
#> DRR006386 5 0.2856 0.662371 0.040 0.004 0.012 0.052 0.892
#> DRR006387 5 0.5645 0.222813 0.040 0.020 0.000 0.408 0.532
#> DRR006388 5 0.5294 0.343380 0.056 0.332 0.000 0.004 0.608
#> DRR006389 5 0.5279 0.352261 0.056 0.328 0.000 0.004 0.612
#> DRR006390 2 0.1997 0.819019 0.000 0.932 0.028 0.016 0.024
#> DRR006391 2 0.1913 0.819632 0.000 0.936 0.024 0.020 0.020
#> DRR006392 1 0.3957 0.571332 0.712 0.000 0.008 0.000 0.280
#> DRR006393 1 0.3439 0.664450 0.800 0.008 0.000 0.004 0.188
#> DRR006394 2 0.1743 0.813032 0.000 0.940 0.028 0.028 0.004
#> DRR006395 4 0.2237 0.865331 0.084 0.008 0.000 0.904 0.004
#> DRR006396 5 0.2166 0.675065 0.072 0.012 0.000 0.004 0.912
#> DRR006397 2 0.5124 0.530759 0.036 0.636 0.000 0.012 0.316
#> DRR006398 2 0.5176 0.532415 0.040 0.636 0.000 0.012 0.312
#> DRR006399 4 0.0798 0.878878 0.000 0.008 0.000 0.976 0.016
#> DRR006400 4 0.0798 0.878878 0.000 0.008 0.000 0.976 0.016
#> DRR006401 4 0.0290 0.882941 0.000 0.008 0.000 0.992 0.000
#> DRR006402 4 0.0290 0.882941 0.000 0.008 0.000 0.992 0.000
#> DRR006403 4 0.0324 0.883277 0.004 0.004 0.000 0.992 0.000
#> DRR006404 4 0.2102 0.871235 0.068 0.012 0.000 0.916 0.004
#> DRR006405 4 0.3243 0.835768 0.116 0.032 0.000 0.848 0.004
#> DRR006406 4 0.3007 0.847885 0.104 0.028 0.000 0.864 0.004
#> DRR006407 4 0.2228 0.863441 0.012 0.076 0.000 0.908 0.004
#> DRR006408 4 0.1978 0.865041 0.004 0.024 0.000 0.928 0.044
#> DRR006409 1 0.5174 -0.000219 0.516 0.000 0.016 0.452 0.016
#> DRR006410 4 0.2885 0.828507 0.052 0.004 0.000 0.880 0.064
#> DRR006411 2 0.3359 0.792754 0.000 0.848 0.016 0.024 0.112
#> DRR006412 2 0.1974 0.819621 0.000 0.932 0.016 0.016 0.036
#> DRR006413 5 0.3080 0.651702 0.140 0.008 0.008 0.000 0.844
#> DRR006414 3 0.5049 0.066185 0.032 0.000 0.484 0.000 0.484
#> DRR006415 3 0.2881 0.729496 0.012 0.004 0.860 0.000 0.124
#> DRR006416 5 0.5343 0.386094 0.340 0.068 0.000 0.000 0.592
#> DRR006417 2 0.3895 0.369774 0.000 0.680 0.320 0.000 0.000
#> DRR006418 2 0.5446 0.503935 0.100 0.628 0.000 0.000 0.272
#> DRR006419 2 0.5727 0.299534 0.028 0.532 0.036 0.000 0.404
#> DRR006420 5 0.5067 0.561069 0.232 0.024 0.044 0.000 0.700
#> DRR006421 3 0.2077 0.768462 0.000 0.040 0.920 0.040 0.000
#> DRR006422 1 0.1836 0.682882 0.936 0.000 0.008 0.040 0.016
#> DRR006423 2 0.1530 0.819420 0.028 0.952 0.008 0.004 0.008
#> DRR006424 5 0.4201 0.259057 0.408 0.000 0.000 0.000 0.592
#> DRR006425 4 0.2522 0.866327 0.076 0.024 0.000 0.896 0.004
#> DRR006426 2 0.4049 0.722167 0.140 0.800 0.052 0.004 0.004
#> DRR006427 1 0.4995 0.373515 0.668 0.068 0.264 0.000 0.000
#> DRR006428 3 0.4659 0.532990 0.332 0.000 0.644 0.020 0.004
#> DRR006429 2 0.3990 0.773042 0.096 0.828 0.004 0.032 0.040
#> DRR006430 1 0.3333 0.651506 0.788 0.000 0.004 0.000 0.208
#> DRR006431 1 0.3563 0.695993 0.824 0.000 0.008 0.028 0.140
#> DRR006432 2 0.2569 0.793671 0.068 0.892 0.040 0.000 0.000
#> DRR006433 4 0.5421 0.301996 0.044 0.012 0.360 0.584 0.000
#> DRR006434 3 0.3300 0.713816 0.004 0.204 0.792 0.000 0.000
#> DRR006435 1 0.8634 0.238902 0.380 0.184 0.220 0.012 0.204
#> DRR006436 1 0.7915 0.206878 0.416 0.144 0.308 0.000 0.132
#> DRR006437 5 0.2578 0.655592 0.000 0.040 0.016 0.040 0.904
#> DRR006438 3 0.4907 0.737297 0.052 0.120 0.764 0.000 0.064
#> DRR006439 3 0.4433 0.699825 0.116 0.000 0.792 0.032 0.060
#> DRR006440 3 0.3707 0.644442 0.000 0.284 0.716 0.000 0.000
#> DRR006441 2 0.2722 0.810457 0.000 0.892 0.008 0.040 0.060
#> DRR006442 3 0.4718 0.461062 0.344 0.000 0.628 0.000 0.028
#> DRR006443 3 0.3246 0.724853 0.008 0.184 0.808 0.000 0.000
#> DRR006444 5 0.6264 0.351630 0.096 0.024 0.328 0.000 0.552
#> DRR006445 5 0.3507 0.658904 0.120 0.052 0.000 0.000 0.828
#> DRR006446 2 0.3192 0.782464 0.000 0.848 0.000 0.040 0.112
#> DRR006447 5 0.5375 0.254844 0.064 0.368 0.000 0.000 0.568
#> DRR006448 4 0.2672 0.825046 0.008 0.004 0.000 0.872 0.116
#> DRR006449 5 0.4712 0.403282 0.004 0.016 0.008 0.304 0.668
#> DRR006450 5 0.2074 0.675949 0.044 0.036 0.000 0.000 0.920
#> DRR006451 4 0.4779 0.725257 0.008 0.120 0.000 0.748 0.124
#> DRR006452 5 0.2777 0.663616 0.120 0.016 0.000 0.000 0.864
#> DRR006453 5 0.4318 0.501457 0.292 0.020 0.000 0.000 0.688
#> DRR006454 5 0.6672 0.351634 0.008 0.212 0.024 0.172 0.584
#> DRR006455 5 0.4797 0.573058 0.172 0.000 0.104 0.000 0.724
#> DRR006456 3 0.1798 0.759808 0.064 0.004 0.928 0.000 0.004
#> DRR006457 3 0.3736 0.756692 0.072 0.100 0.824 0.004 0.000
#> DRR006458 1 0.3031 0.699606 0.852 0.000 0.016 0.004 0.128
#> DRR006459 1 0.2521 0.660534 0.900 0.000 0.024 0.068 0.008
#> DRR006460 4 0.0854 0.883092 0.012 0.004 0.008 0.976 0.000
#> DRR006461 3 0.1029 0.766215 0.008 0.008 0.972 0.008 0.004
#> DRR006462 5 0.5532 0.612479 0.064 0.068 0.000 0.156 0.712
#> DRR006463 3 0.3612 0.663521 0.000 0.268 0.732 0.000 0.000
#> DRR006464 2 0.1822 0.820054 0.024 0.936 0.004 0.000 0.036
#> DRR006465 1 0.2967 0.699159 0.868 0.016 0.000 0.012 0.104
#> DRR006466 3 0.4830 0.648979 0.004 0.072 0.716 0.208 0.000
#> DRR006467 1 0.4304 0.068860 0.516 0.000 0.000 0.000 0.484
#> DRR006468 3 0.6016 0.678461 0.136 0.180 0.656 0.004 0.024
#> DRR006469 2 0.0960 0.819071 0.008 0.972 0.016 0.000 0.004
#> DRR006470 2 0.2364 0.794460 0.020 0.908 0.064 0.000 0.008
#> DRR006471 1 0.3548 0.667051 0.796 0.004 0.012 0.000 0.188
#> DRR006472 1 0.5705 0.581457 0.684 0.184 0.104 0.004 0.024
#> DRR006473 1 0.4830 0.545767 0.700 0.248 0.012 0.000 0.040
#> DRR006474 3 0.6453 0.468243 0.256 0.000 0.552 0.180 0.012
#> DRR006475 1 0.2733 0.702155 0.872 0.004 0.012 0.000 0.112
#> DRR006476 4 0.1652 0.881306 0.004 0.040 0.004 0.944 0.008
#> DRR006477 3 0.5402 0.534617 0.008 0.004 0.664 0.252 0.072
#> DRR006478 1 0.3086 0.695975 0.876 0.036 0.000 0.020 0.068
#> DRR006479 5 0.5568 0.236582 0.076 0.000 0.384 0.000 0.540
#> DRR006480 1 0.4696 0.431820 0.616 0.000 0.024 0.000 0.360
#> DRR006481 3 0.3993 0.704986 0.028 0.216 0.756 0.000 0.000
#> DRR006482 5 0.4081 0.616308 0.008 0.028 0.084 0.052 0.828
#> DRR006483 1 0.2037 0.704572 0.920 0.004 0.012 0.000 0.064
#> DRR006484 3 0.2450 0.767683 0.028 0.076 0.896 0.000 0.000
#> DRR006485 3 0.3932 0.589302 0.000 0.328 0.672 0.000 0.000
#> DRR006486 1 0.4553 0.491286 0.652 0.004 0.016 0.000 0.328
#> DRR006487 3 0.1904 0.766259 0.020 0.016 0.936 0.000 0.028
#> DRR006488 1 0.5745 0.584174 0.656 0.012 0.152 0.000 0.180
#> DRR006489 5 0.4211 0.381740 0.360 0.004 0.000 0.000 0.636
#> DRR006490 3 0.5224 0.555138 0.140 0.000 0.684 0.000 0.176
#> DRR006491 3 0.3401 0.713327 0.064 0.000 0.840 0.000 0.096
#> DRR006492 5 0.5490 0.545750 0.176 0.000 0.120 0.016 0.688
#> DRR006493 3 0.0451 0.766542 0.000 0.008 0.988 0.000 0.004
#> DRR006494 1 0.2938 0.697625 0.880 0.000 0.048 0.008 0.064
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> DRR006374 3 0.4884 0.72393 0.004 0.016 0.736 0.116 0.112 0.016
#> DRR006375 4 0.4967 0.48148 0.080 0.064 0.000 0.728 0.124 0.004
#> DRR006376 4 0.6091 0.08154 0.032 0.400 0.004 0.460 0.000 0.104
#> DRR006377 4 0.6645 0.46812 0.000 0.200 0.044 0.560 0.164 0.032
#> DRR006378 6 0.1621 0.77396 0.028 0.008 0.004 0.004 0.012 0.944
#> DRR006379 2 0.6759 -0.01389 0.120 0.424 0.000 0.360 0.000 0.096
#> DRR006380 2 0.7220 0.19944 0.316 0.408 0.176 0.092 0.000 0.008
#> DRR006381 1 0.4518 0.35859 0.612 0.000 0.004 0.016 0.356 0.012
#> DRR006382 3 0.4702 0.69474 0.176 0.000 0.716 0.084 0.024 0.000
#> DRR006383 3 0.6646 0.26508 0.392 0.000 0.408 0.116 0.084 0.000
#> DRR006384 2 0.5492 0.55540 0.148 0.688 0.068 0.084 0.000 0.012
#> DRR006385 1 0.4773 0.50230 0.684 0.000 0.000 0.008 0.208 0.100
#> DRR006386 1 0.6047 0.47634 0.620 0.088 0.000 0.076 0.204 0.012
#> DRR006387 1 0.6564 0.50209 0.576 0.236 0.000 0.048 0.076 0.064
#> DRR006388 6 0.5799 0.33093 0.292 0.004 0.000 0.004 0.168 0.532
#> DRR006389 6 0.5871 0.26811 0.316 0.004 0.000 0.004 0.168 0.508
#> DRR006390 6 0.1456 0.77374 0.008 0.004 0.020 0.012 0.004 0.952
#> DRR006391 6 0.1456 0.77374 0.008 0.004 0.020 0.012 0.004 0.952
#> DRR006392 4 0.5537 -0.00473 0.116 0.000 0.004 0.472 0.408 0.000
#> DRR006393 5 0.3908 0.52652 0.092 0.004 0.000 0.104 0.792 0.008
#> DRR006394 6 0.2030 0.76173 0.000 0.016 0.048 0.012 0.004 0.920
#> DRR006395 2 0.2288 0.68370 0.000 0.904 0.004 0.048 0.040 0.004
#> DRR006396 1 0.4652 0.54214 0.728 0.000 0.000 0.076 0.164 0.032
#> DRR006397 6 0.3400 0.71616 0.132 0.000 0.000 0.008 0.044 0.816
#> DRR006398 6 0.3567 0.70867 0.136 0.000 0.000 0.008 0.052 0.804
#> DRR006399 2 0.2189 0.68483 0.032 0.904 0.004 0.060 0.000 0.000
#> DRR006400 2 0.2128 0.68754 0.032 0.908 0.004 0.056 0.000 0.000
#> DRR006401 2 0.0520 0.71071 0.000 0.984 0.000 0.008 0.000 0.008
#> DRR006402 2 0.0520 0.71071 0.000 0.984 0.000 0.008 0.000 0.008
#> DRR006403 2 0.0748 0.70901 0.000 0.976 0.004 0.016 0.000 0.004
#> DRR006404 2 0.3607 0.28452 0.000 0.652 0.000 0.348 0.000 0.000
#> DRR006405 4 0.5183 0.34308 0.020 0.316 0.000 0.612 0.016 0.036
#> DRR006406 4 0.5088 0.24123 0.016 0.384 0.000 0.560 0.012 0.028
#> DRR006407 2 0.2463 0.67589 0.004 0.888 0.004 0.024 0.000 0.080
#> DRR006408 2 0.1426 0.70715 0.028 0.948 0.000 0.008 0.000 0.016
#> DRR006409 2 0.6741 -0.33377 0.004 0.388 0.036 0.356 0.216 0.000
#> DRR006410 1 0.7229 -0.00504 0.356 0.304 0.000 0.280 0.036 0.024
#> DRR006411 6 0.2432 0.76053 0.072 0.000 0.016 0.020 0.000 0.892
#> DRR006412 6 0.1396 0.77352 0.024 0.004 0.008 0.012 0.000 0.952
#> DRR006413 1 0.4659 0.35139 0.592 0.000 0.000 0.020 0.368 0.020
#> DRR006414 1 0.5954 -0.17051 0.492 0.000 0.372 0.100 0.036 0.000
#> DRR006415 3 0.5838 0.61014 0.196 0.000 0.624 0.100 0.080 0.000
#> DRR006416 5 0.5817 0.25730 0.256 0.000 0.000 0.032 0.580 0.132
#> DRR006417 6 0.4106 0.48393 0.004 0.000 0.312 0.020 0.000 0.664
#> DRR006418 6 0.3896 0.70804 0.112 0.000 0.000 0.016 0.080 0.792
#> DRR006419 6 0.6069 0.43249 0.176 0.000 0.016 0.008 0.252 0.548
#> DRR006420 5 0.6145 0.27265 0.244 0.000 0.028 0.040 0.596 0.092
#> DRR006421 3 0.3059 0.76205 0.048 0.032 0.872 0.036 0.000 0.012
#> DRR006422 4 0.4495 0.31727 0.000 0.020 0.004 0.580 0.392 0.004
#> DRR006423 6 0.4083 0.73967 0.012 0.024 0.028 0.024 0.100 0.812
#> DRR006424 1 0.5916 0.24124 0.464 0.000 0.000 0.376 0.148 0.012
#> DRR006425 4 0.5808 0.12959 0.036 0.412 0.004 0.488 0.004 0.056
#> DRR006426 6 0.4525 0.67007 0.000 0.000 0.092 0.060 0.088 0.760
#> DRR006427 5 0.5753 0.07150 0.000 0.000 0.120 0.352 0.512 0.016
#> DRR006428 3 0.4971 0.69738 0.008 0.044 0.736 0.124 0.084 0.004
#> DRR006429 6 0.5169 0.60913 0.008 0.036 0.020 0.020 0.244 0.672
#> DRR006430 4 0.5121 0.33974 0.096 0.004 0.000 0.624 0.272 0.004
#> DRR006431 4 0.5642 0.20259 0.064 0.028 0.000 0.500 0.404 0.004
#> DRR006432 6 0.2737 0.75718 0.000 0.000 0.060 0.036 0.024 0.880
#> DRR006433 2 0.4284 0.04609 0.000 0.544 0.440 0.000 0.012 0.004
#> DRR006434 3 0.2238 0.75706 0.004 0.000 0.900 0.016 0.004 0.076
#> DRR006435 5 0.8403 0.29146 0.060 0.056 0.168 0.108 0.464 0.144
#> DRR006436 5 0.6584 0.36004 0.048 0.000 0.140 0.164 0.596 0.052
#> DRR006437 1 0.2820 0.56256 0.880 0.012 0.004 0.036 0.004 0.064
#> DRR006438 3 0.4566 0.71141 0.040 0.000 0.756 0.012 0.144 0.048
#> DRR006439 3 0.5527 0.71438 0.076 0.068 0.704 0.032 0.120 0.000
#> DRR006440 3 0.2933 0.73863 0.008 0.000 0.848 0.016 0.004 0.124
#> DRR006441 6 0.5546 0.64370 0.048 0.188 0.008 0.024 0.048 0.684
#> DRR006442 3 0.4971 0.68233 0.032 0.000 0.704 0.120 0.144 0.000
#> DRR006443 3 0.2209 0.75732 0.000 0.000 0.900 0.024 0.004 0.072
#> DRR006444 1 0.7564 0.10355 0.380 0.000 0.212 0.084 0.300 0.024
#> DRR006445 1 0.4756 0.55270 0.736 0.000 0.000 0.048 0.108 0.108
#> DRR006446 6 0.2179 0.76372 0.064 0.008 0.004 0.016 0.000 0.908
#> DRR006447 6 0.5521 0.14843 0.412 0.000 0.000 0.028 0.064 0.496
#> DRR006448 1 0.5961 0.10752 0.500 0.288 0.000 0.204 0.000 0.008
#> DRR006449 1 0.4263 0.53912 0.776 0.084 0.000 0.100 0.000 0.040
#> DRR006450 1 0.4266 0.54835 0.760 0.004 0.000 0.008 0.116 0.112
#> DRR006451 1 0.7310 0.11533 0.392 0.248 0.000 0.120 0.000 0.240
#> DRR006452 1 0.4544 0.54932 0.748 0.000 0.000 0.064 0.140 0.048
#> DRR006453 1 0.5965 0.17263 0.452 0.000 0.000 0.044 0.420 0.084
#> DRR006454 1 0.6659 0.18192 0.496 0.120 0.016 0.056 0.000 0.312
#> DRR006455 1 0.5771 0.21139 0.476 0.000 0.024 0.096 0.404 0.000
#> DRR006456 3 0.2434 0.76859 0.008 0.000 0.892 0.036 0.064 0.000
#> DRR006457 3 0.2693 0.76710 0.000 0.004 0.888 0.024 0.048 0.036
#> DRR006458 5 0.3883 0.37959 0.028 0.000 0.000 0.240 0.728 0.004
#> DRR006459 4 0.5257 0.20369 0.004 0.032 0.020 0.484 0.456 0.004
#> DRR006460 2 0.1439 0.70492 0.008 0.952 0.004 0.024 0.004 0.008
#> DRR006461 3 0.2452 0.76903 0.020 0.008 0.900 0.056 0.016 0.000
#> DRR006462 1 0.5987 0.53026 0.652 0.032 0.000 0.168 0.064 0.084
#> DRR006463 3 0.3061 0.73544 0.008 0.000 0.840 0.020 0.004 0.128
#> DRR006464 6 0.2302 0.77754 0.024 0.000 0.016 0.012 0.036 0.912
#> DRR006465 4 0.5161 0.38687 0.032 0.020 0.000 0.604 0.328 0.016
#> DRR006466 3 0.3553 0.72583 0.016 0.120 0.824 0.020 0.000 0.020
#> DRR006467 5 0.5333 0.33428 0.240 0.000 0.004 0.152 0.604 0.000
#> DRR006468 3 0.7463 0.51433 0.024 0.056 0.560 0.108 0.136 0.116
#> DRR006469 6 0.1325 0.77581 0.004 0.004 0.024 0.008 0.004 0.956
#> DRR006470 6 0.3333 0.72993 0.004 0.000 0.128 0.024 0.016 0.828
#> DRR006471 5 0.4470 0.39288 0.052 0.000 0.000 0.256 0.684 0.008
#> DRR006472 5 0.4250 0.49311 0.004 0.000 0.044 0.052 0.780 0.120
#> DRR006473 5 0.5906 0.30092 0.004 0.000 0.020 0.136 0.552 0.288
#> DRR006474 3 0.7648 0.08920 0.008 0.288 0.296 0.120 0.288 0.000
#> DRR006475 5 0.2136 0.52611 0.012 0.000 0.000 0.064 0.908 0.016
#> DRR006476 2 0.3613 0.64247 0.012 0.840 0.012 0.048 0.012 0.076
#> DRR006477 3 0.6328 0.32530 0.080 0.328 0.500 0.092 0.000 0.000
#> DRR006478 5 0.4799 0.20079 0.020 0.004 0.000 0.324 0.624 0.028
#> DRR006479 3 0.6621 0.04711 0.372 0.000 0.396 0.044 0.188 0.000
#> DRR006480 5 0.3821 0.50556 0.156 0.000 0.004 0.064 0.776 0.000
#> DRR006481 3 0.2896 0.75154 0.000 0.000 0.864 0.044 0.012 0.080
#> DRR006482 1 0.3693 0.52418 0.840 0.008 0.044 0.064 0.012 0.032
#> DRR006483 5 0.4227 -0.20736 0.008 0.000 0.000 0.488 0.500 0.004
#> DRR006484 3 0.1777 0.76894 0.000 0.000 0.932 0.032 0.012 0.024
#> DRR006485 3 0.3104 0.72166 0.004 0.000 0.824 0.016 0.004 0.152
#> DRR006486 5 0.2257 0.55321 0.076 0.000 0.004 0.012 0.900 0.008
#> DRR006487 3 0.2880 0.76277 0.060 0.000 0.876 0.036 0.024 0.004
#> DRR006488 5 0.3577 0.50332 0.028 0.000 0.020 0.136 0.812 0.004
#> DRR006489 5 0.5832 -0.03695 0.380 0.000 0.000 0.124 0.480 0.016
#> DRR006490 3 0.5141 0.65839 0.168 0.000 0.688 0.040 0.104 0.000
#> DRR006491 3 0.4787 0.70436 0.140 0.000 0.728 0.044 0.088 0.000
#> DRR006492 5 0.6919 0.24172 0.228 0.120 0.028 0.088 0.536 0.000
#> DRR006493 3 0.1755 0.76907 0.032 0.000 0.932 0.028 0.008 0.000
#> DRR006494 5 0.3319 0.44327 0.004 0.004 0.016 0.160 0.812 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)
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