Date: 2019-12-26 01:34:11 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 17471 rows and 87 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] 17471 87
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 | 0.976 | 0.990 | ** | |
| MAD:kmeans | 2 | 1.000 | 0.974 | 0.989 | ** | |
| MAD:NMF | 2 | 1.000 | 0.963 | 0.985 | ** | |
| SD:NMF | 2 | 0.976 | 0.963 | 0.984 | ** | |
| ATC:NMF | 3 | 0.958 | 0.915 | 0.970 | ** | 2 |
| ATC:hclust | 2 | 0.953 | 0.960 | 0.967 | ** | |
| CV:NMF | 4 | 0.939 | 0.911 | 0.943 | * | 2 |
| MAD:pam | 6 | 0.932 | 0.905 | 0.951 | * | |
| ATC:skmeans | 3 | 0.925 | 0.894 | 0.954 | * | 2 |
| MAD:skmeans | 4 | 0.923 | 0.902 | 0.955 | * | 2,3 |
| SD:skmeans | 4 | 0.914 | 0.899 | 0.955 | * | 2,3 |
| CV:skmeans | 2 | 0.906 | 0.946 | 0.975 | * | |
| SD:pam | 6 | 0.841 | 0.840 | 0.915 | ||
| ATC:kmeans | 3 | 0.837 | 0.918 | 0.948 | ||
| CV:mclust | 4 | 0.743 | 0.816 | 0.902 | ||
| ATC:pam | 3 | 0.679 | 0.899 | 0.945 | ||
| CV:kmeans | 2 | 0.641 | 0.900 | 0.937 | ||
| CV:pam | 2 | 0.506 | 0.874 | 0.921 | ||
| MAD:hclust | 2 | 0.444 | 0.778 | 0.878 | ||
| CV:hclust | 3 | 0.411 | 0.652 | 0.853 | ||
| SD:hclust | 3 | 0.390 | 0.705 | 0.799 | ||
| MAD:mclust | 2 | 0.345 | 0.818 | 0.865 | ||
| ATC:mclust | 3 | 0.335 | 0.817 | 0.847 | ||
| SD:mclust | 2 | 0.212 | 0.811 | 0.863 |
**: 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 0.976 0.963 0.984 0.497 0.502 0.502
#> CV:NMF 2 0.973 0.944 0.976 0.496 0.505 0.505
#> MAD:NMF 2 1.000 0.963 0.985 0.500 0.502 0.502
#> ATC:NMF 2 0.976 0.954 0.979 0.310 0.668 0.668
#> SD:skmeans 2 1.000 0.953 0.982 0.499 0.500 0.500
#> CV:skmeans 2 0.906 0.946 0.975 0.496 0.509 0.509
#> MAD:skmeans 2 1.000 0.977 0.989 0.501 0.500 0.500
#> ATC:skmeans 2 0.907 0.966 0.984 0.505 0.495 0.495
#> SD:mclust 2 0.212 0.811 0.863 0.459 0.505 0.505
#> CV:mclust 2 0.351 0.604 0.840 0.436 0.518 0.518
#> MAD:mclust 2 0.345 0.818 0.865 0.463 0.505 0.505
#> ATC:mclust 2 0.134 0.504 0.737 0.440 0.513 0.513
#> SD:kmeans 2 1.000 0.976 0.990 0.493 0.509 0.509
#> CV:kmeans 2 0.641 0.900 0.937 0.457 0.518 0.518
#> MAD:kmeans 2 1.000 0.974 0.989 0.495 0.505 0.505
#> ATC:kmeans 2 0.656 0.819 0.919 0.437 0.543 0.543
#> SD:pam 2 0.637 0.796 0.904 0.467 0.536 0.536
#> CV:pam 2 0.506 0.874 0.921 0.481 0.495 0.495
#> MAD:pam 2 0.567 0.771 0.900 0.476 0.530 0.530
#> ATC:pam 2 0.659 0.892 0.930 0.424 0.543 0.543
#> SD:hclust 2 0.456 0.129 0.580 0.419 0.607 0.607
#> CV:hclust 2 0.859 0.896 0.951 0.145 0.933 0.933
#> MAD:hclust 2 0.444 0.778 0.878 0.440 0.513 0.513
#> ATC:hclust 2 0.953 0.960 0.967 0.308 0.696 0.696
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.747 0.866 0.937 0.320 0.720 0.502
#> CV:NMF 3 0.824 0.878 0.936 0.328 0.733 0.521
#> MAD:NMF 3 0.656 0.764 0.894 0.290 0.746 0.541
#> ATC:NMF 3 0.958 0.915 0.970 0.973 0.621 0.472
#> SD:skmeans 3 0.965 0.926 0.968 0.328 0.809 0.628
#> CV:skmeans 3 0.732 0.848 0.913 0.352 0.775 0.577
#> MAD:skmeans 3 0.954 0.949 0.967 0.321 0.809 0.628
#> ATC:skmeans 3 0.925 0.894 0.954 0.329 0.728 0.503
#> SD:mclust 3 0.468 0.607 0.784 0.317 0.760 0.574
#> CV:mclust 3 0.496 0.729 0.834 0.418 0.628 0.404
#> MAD:mclust 3 0.502 0.599 0.816 0.322 0.649 0.431
#> ATC:mclust 3 0.335 0.817 0.847 0.316 0.565 0.359
#> SD:kmeans 3 0.528 0.695 0.813 0.293 0.638 0.410
#> CV:kmeans 3 0.474 0.776 0.814 0.312 0.923 0.851
#> MAD:kmeans 3 0.525 0.665 0.764 0.285 0.636 0.406
#> ATC:kmeans 3 0.837 0.918 0.948 0.450 0.603 0.393
#> SD:pam 3 0.513 0.583 0.810 0.412 0.590 0.362
#> CV:pam 3 0.419 0.533 0.777 0.365 0.752 0.537
#> MAD:pam 3 0.655 0.711 0.867 0.376 0.596 0.365
#> ATC:pam 3 0.679 0.899 0.945 0.424 0.655 0.463
#> SD:hclust 3 0.390 0.705 0.799 0.323 0.461 0.329
#> CV:hclust 3 0.411 0.652 0.853 1.299 0.769 0.752
#> MAD:hclust 3 0.304 0.665 0.790 0.209 0.926 0.856
#> ATC:hclust 3 0.396 0.526 0.745 0.819 0.765 0.662
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.731 0.821 0.884 0.136 0.765 0.432
#> CV:NMF 4 0.939 0.911 0.943 0.137 0.806 0.506
#> MAD:NMF 4 0.596 0.685 0.794 0.147 0.767 0.443
#> ATC:NMF 4 0.646 0.726 0.862 0.180 0.779 0.496
#> SD:skmeans 4 0.914 0.899 0.955 0.137 0.869 0.635
#> CV:skmeans 4 0.893 0.876 0.949 0.122 0.878 0.652
#> MAD:skmeans 4 0.923 0.902 0.955 0.137 0.870 0.638
#> ATC:skmeans 4 0.719 0.653 0.859 0.118 0.804 0.496
#> SD:mclust 4 0.694 0.803 0.841 0.155 0.752 0.450
#> CV:mclust 4 0.743 0.816 0.902 0.150 0.818 0.552
#> MAD:mclust 4 0.645 0.748 0.822 0.121 0.737 0.426
#> ATC:mclust 4 0.464 0.628 0.817 0.128 0.590 0.294
#> SD:kmeans 4 0.878 0.886 0.910 0.147 0.823 0.555
#> CV:kmeans 4 0.716 0.836 0.870 0.194 0.793 0.541
#> MAD:kmeans 4 0.829 0.838 0.893 0.151 0.805 0.523
#> ATC:kmeans 4 0.753 0.755 0.884 0.145 0.836 0.586
#> SD:pam 4 0.783 0.831 0.918 0.139 0.814 0.515
#> CV:pam 4 0.524 0.504 0.753 0.112 0.782 0.453
#> MAD:pam 4 0.892 0.884 0.942 0.154 0.837 0.563
#> ATC:pam 4 0.686 0.726 0.871 0.217 0.676 0.338
#> SD:hclust 4 0.499 0.615 0.781 0.109 0.927 0.845
#> CV:hclust 4 0.368 0.649 0.793 0.436 0.834 0.764
#> MAD:hclust 4 0.446 0.525 0.733 0.166 0.924 0.835
#> ATC:hclust 4 0.578 0.561 0.809 0.264 0.803 0.573
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.749 0.683 0.820 0.0612 0.918 0.701
#> CV:NMF 5 0.854 0.828 0.915 0.0561 0.925 0.720
#> MAD:NMF 5 0.721 0.611 0.791 0.0664 0.909 0.680
#> ATC:NMF 5 0.836 0.839 0.919 0.0821 0.889 0.623
#> SD:skmeans 5 0.875 0.852 0.923 0.0615 0.928 0.723
#> CV:skmeans 5 0.817 0.781 0.883 0.0651 0.936 0.753
#> MAD:skmeans 5 0.891 0.820 0.919 0.0629 0.920 0.698
#> ATC:skmeans 5 0.724 0.725 0.849 0.0642 0.871 0.558
#> SD:mclust 5 0.672 0.650 0.809 0.0956 0.962 0.863
#> CV:mclust 5 0.716 0.592 0.788 0.0838 0.885 0.608
#> MAD:mclust 5 0.667 0.631 0.827 0.0929 0.921 0.741
#> ATC:mclust 5 0.505 0.550 0.764 0.1202 0.866 0.639
#> SD:kmeans 5 0.761 0.618 0.829 0.0736 0.957 0.841
#> CV:kmeans 5 0.767 0.699 0.828 0.0807 0.994 0.976
#> MAD:kmeans 5 0.761 0.649 0.799 0.0761 0.921 0.721
#> ATC:kmeans 5 0.645 0.623 0.785 0.0787 0.854 0.544
#> SD:pam 5 0.747 0.697 0.868 0.0672 0.920 0.691
#> CV:pam 5 0.644 0.635 0.781 0.0582 0.872 0.558
#> MAD:pam 5 0.833 0.778 0.885 0.0647 0.893 0.605
#> ATC:pam 5 0.762 0.787 0.866 0.0798 0.899 0.640
#> SD:hclust 5 0.487 0.465 0.693 0.0729 0.945 0.869
#> CV:hclust 5 0.439 0.643 0.785 0.1821 0.886 0.795
#> MAD:hclust 5 0.482 0.234 0.591 0.1159 0.822 0.598
#> ATC:hclust 5 0.628 0.638 0.767 0.0909 0.886 0.606
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.793 0.676 0.834 0.0353 0.941 0.741
#> CV:NMF 6 0.781 0.641 0.783 0.0413 0.892 0.560
#> MAD:NMF 6 0.805 0.696 0.836 0.0392 0.936 0.731
#> ATC:NMF 6 0.721 0.707 0.807 0.0526 0.899 0.579
#> SD:skmeans 6 0.818 0.661 0.821 0.0344 0.983 0.918
#> CV:skmeans 6 0.748 0.643 0.798 0.0365 0.968 0.849
#> MAD:skmeans 6 0.812 0.684 0.838 0.0349 0.976 0.885
#> ATC:skmeans 6 0.741 0.584 0.769 0.0370 0.916 0.627
#> SD:mclust 6 0.691 0.578 0.785 0.0459 0.907 0.651
#> CV:mclust 6 0.766 0.698 0.846 0.0560 0.926 0.670
#> MAD:mclust 6 0.675 0.611 0.799 0.0507 0.915 0.693
#> ATC:mclust 6 0.584 0.486 0.685 0.0621 0.890 0.642
#> SD:kmeans 6 0.744 0.671 0.791 0.0448 0.908 0.643
#> CV:kmeans 6 0.730 0.598 0.769 0.0420 0.886 0.577
#> MAD:kmeans 6 0.745 0.670 0.810 0.0461 0.909 0.631
#> ATC:kmeans 6 0.661 0.467 0.659 0.0508 0.844 0.466
#> SD:pam 6 0.841 0.840 0.915 0.0359 0.922 0.645
#> CV:pam 6 0.741 0.761 0.866 0.0380 0.957 0.804
#> MAD:pam 6 0.932 0.905 0.951 0.0368 0.967 0.832
#> ATC:pam 6 0.790 0.713 0.868 0.0341 0.856 0.455
#> SD:hclust 6 0.474 0.613 0.763 0.1183 0.790 0.496
#> CV:hclust 6 0.425 0.649 0.792 0.0504 0.967 0.930
#> MAD:hclust 6 0.461 0.591 0.749 0.0723 0.778 0.424
#> ATC:hclust 6 0.690 0.690 0.792 0.0505 0.933 0.694
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 17471 rows and 87 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 0.456 0.129 0.580 0.4194 0.607 0.607
#> 3 3 0.390 0.705 0.799 0.3232 0.461 0.329
#> 4 4 0.499 0.615 0.781 0.1091 0.927 0.845
#> 5 5 0.487 0.465 0.693 0.0729 0.945 0.869
#> 6 6 0.474 0.613 0.763 0.1183 0.790 0.496
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 1 1.0000 -0.927 0.504 0.496
#> F569915C-8F77-4D67-9730-30824DB57EE5 1 0.9988 -0.896 0.520 0.480
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.9977 0.414 0.528 0.472
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.9970 -0.838 0.532 0.468
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.9710 0.421 0.600 0.400
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.9833 -0.707 0.576 0.424
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.9977 0.414 0.528 0.472
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0672 0.307 0.992 0.008
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 1 0.9427 -0.594 0.640 0.360
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.9977 0.414 0.528 0.472
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.6712 0.388 0.824 0.176
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.9944 0.418 0.544 0.456
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 1 0.9970 -0.839 0.532 0.468
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.9977 0.414 0.528 0.472
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 1 0.9954 -0.844 0.540 0.460
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.3733 0.281 0.928 0.072
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.9970 0.415 0.532 0.468
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.9977 0.414 0.528 0.472
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 1.0000 0.931 0.500 0.500
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.9775 -0.729 0.588 0.412
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.9977 0.975 0.472 0.528
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.9896 0.919 0.440 0.560
#> F9C23182-91C4-4145-AE52-526FE8EB199D 1 0.9983 -0.840 0.524 0.476
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.9977 0.975 0.472 0.528
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.9944 0.418 0.544 0.456
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.9963 -0.854 0.536 0.464
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 1 1.0000 -0.935 0.500 0.500
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.9977 0.975 0.472 0.528
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.9970 0.416 0.532 0.468
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.8016 0.077 0.756 0.244
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.9977 0.975 0.472 0.528
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.9970 0.072 0.532 0.468
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.9710 0.420 0.600 0.400
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.9970 -0.837 0.532 0.468
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.9896 0.919 0.440 0.560
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.9963 0.965 0.464 0.536
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.9977 0.975 0.472 0.528
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.8144 -0.284 0.748 0.252
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.9129 0.024 0.672 0.328
#> FF7021BB-414B-4466-BC43-372D856E6A1D 1 0.9209 -0.265 0.664 0.336
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 1 0.9998 -0.916 0.508 0.492
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.1633 0.282 0.976 0.024
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 1 0.9922 -0.817 0.552 0.448
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0672 0.307 0.992 0.008
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.9977 0.975 0.472 0.528
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4815 0.363 0.896 0.104
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 1.0000 0.931 0.500 0.500
#> 117673A3-2918-4702-8583-B66ADE6E4338 1 0.9963 -0.854 0.536 0.464
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.9922 -0.817 0.552 0.448
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.9944 0.418 0.544 0.456
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.9998 0.945 0.492 0.508
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.9977 0.975 0.472 0.528
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.9754 0.420 0.592 0.408
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.9977 0.975 0.472 0.528
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.9732 0.421 0.596 0.404
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.9323 -0.539 0.652 0.348
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 1 0.9988 -0.896 0.520 0.480
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 1 0.9993 -0.896 0.516 0.484
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.6801 0.389 0.820 0.180
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.9710 0.420 0.600 0.400
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.9988 0.964 0.480 0.520
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.9909 0.927 0.444 0.556
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0938 0.303 0.988 0.012
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 1 0.7453 0.318 0.788 0.212
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.9881 -0.790 0.564 0.436
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.9977 0.975 0.472 0.528
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 1 1.0000 -0.929 0.504 0.496
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.9977 0.975 0.472 0.528
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.3733 0.281 0.928 0.072
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.9954 0.417 0.540 0.460
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.1414 0.316 0.980 0.020
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.9977 0.975 0.472 0.528
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 1 1.0000 -0.929 0.504 0.496
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 1 0.9775 -0.729 0.588 0.412
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.9358 0.401 0.648 0.352
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.3114 0.339 0.944 0.056
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.9977 0.975 0.472 0.528
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.9963 0.965 0.464 0.536
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.9977 0.414 0.528 0.472
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.1184 0.281 0.984 0.016
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 1 1.0000 -0.935 0.500 0.500
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 1 1.0000 -0.929 0.504 0.496
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 1 0.9833 -0.707 0.576 0.424
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.9977 0.975 0.472 0.528
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 1 1.0000 -0.927 0.504 0.496
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 1.0000 0.931 0.500 0.500
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0376 0.303 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.1529 0.7898 0.000 0.960 0.040
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.2590 0.7748 0.004 0.924 0.072
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0237 0.8302 0.996 0.004 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.2939 0.7730 0.012 0.916 0.072
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.7816 0.5148 0.628 0.084 0.288
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.4475 0.7053 0.016 0.840 0.144
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0237 0.8302 0.996 0.004 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.7562 0.8300 0.064 0.308 0.628
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.5366 0.6039 0.016 0.776 0.208
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0237 0.8302 0.996 0.004 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.8098 0.6181 0.216 0.140 0.644
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3983 0.7874 0.884 0.048 0.068
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.5305 0.7625 0.020 0.788 0.192
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0237 0.8302 0.996 0.004 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.2448 0.7796 0.000 0.924 0.076
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.6835 0.7911 0.040 0.284 0.676
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0661 0.8297 0.988 0.004 0.008
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0237 0.8302 0.996 0.004 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.1964 0.7835 0.000 0.944 0.056
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.3918 0.7227 0.004 0.856 0.140
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3816 0.7713 0.000 0.852 0.148
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.5722 0.6467 0.004 0.704 0.292
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.3618 0.7797 0.012 0.884 0.104
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.3879 0.7725 0.000 0.848 0.152
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1636 0.8212 0.964 0.020 0.016
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.2356 0.7753 0.000 0.928 0.072
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.1860 0.7850 0.000 0.948 0.052
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.3816 0.7713 0.000 0.852 0.148
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2496 0.7987 0.928 0.004 0.068
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.9113 0.0206 0.300 0.528 0.172
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.3879 0.7744 0.000 0.848 0.152
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.9296 0.1422 0.404 0.436 0.160
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.7995 0.4834 0.608 0.088 0.304
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.3031 0.7751 0.012 0.912 0.076
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.5722 0.6467 0.004 0.704 0.292
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.4178 0.7583 0.000 0.828 0.172
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3941 0.7669 0.000 0.844 0.156
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.6962 0.2677 0.036 0.648 0.316
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.8918 0.1386 0.288 0.552 0.160
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.6962 0.0610 0.020 0.568 0.412
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.4121 0.7832 0.000 0.832 0.168
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.7448 0.8114 0.052 0.332 0.616
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.3116 0.7552 0.000 0.892 0.108
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.7562 0.8300 0.064 0.308 0.628
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.3816 0.7713 0.000 0.852 0.148
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 3 0.8803 0.7454 0.180 0.240 0.580
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.1964 0.7835 0.000 0.944 0.056
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.2537 0.7783 0.000 0.920 0.080
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.3116 0.7552 0.000 0.892 0.108
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1636 0.8212 0.964 0.020 0.016
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.2356 0.7935 0.000 0.928 0.072
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.3816 0.7713 0.000 0.852 0.148
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.7742 0.5297 0.632 0.080 0.288
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.3879 0.7725 0.000 0.848 0.152
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.7816 0.5220 0.628 0.084 0.288
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.5643 0.5759 0.020 0.760 0.220
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.2590 0.7748 0.004 0.924 0.072
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.2772 0.7756 0.004 0.916 0.080
#> C41F3064-4483-4796-B860-82155BAA5157 3 0.8213 0.6017 0.228 0.140 0.632
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.7995 0.4834 0.608 0.088 0.304
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.3686 0.7763 0.000 0.860 0.140
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.4796 0.7183 0.000 0.780 0.220
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.7694 0.8280 0.068 0.316 0.616
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.7960 0.6351 0.136 0.208 0.656
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.3715 0.7394 0.004 0.868 0.128
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.3816 0.7713 0.000 0.852 0.148
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.2066 0.7826 0.000 0.940 0.060
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3816 0.7713 0.000 0.852 0.148
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.6835 0.7911 0.040 0.284 0.676
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.3590 0.7976 0.896 0.028 0.076
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.7916 0.8264 0.088 0.292 0.620
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.3816 0.7713 0.000 0.852 0.148
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.2066 0.7826 0.000 0.940 0.060
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.3918 0.7227 0.004 0.856 0.140
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.7337 -0.2156 0.428 0.032 0.540
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.8399 0.7952 0.136 0.256 0.608
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.3816 0.7713 0.000 0.852 0.148
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.4178 0.7583 0.000 0.828 0.172
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1129 0.8222 0.976 0.004 0.020
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.7357 0.8095 0.048 0.332 0.620
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.1529 0.7915 0.000 0.960 0.040
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.2066 0.7826 0.000 0.940 0.060
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.4475 0.7053 0.016 0.840 0.144
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.3816 0.7713 0.000 0.852 0.148
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.1529 0.7898 0.000 0.960 0.040
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.1964 0.7835 0.000 0.944 0.056
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.7588 0.8291 0.064 0.312 0.624
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.4164 0.7554 0.000 0.736 0.000 0.264
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.4584 0.7362 0.000 0.696 0.004 0.300
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.7927 1.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.4795 0.7339 0.012 0.696 0.000 0.292
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.5560 0.5521 0.584 0.000 0.024 0.392
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.5616 0.6515 0.012 0.604 0.012 0.372
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0188 0.7931 0.996 0.000 0.004 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3538 0.6167 0.024 0.036 0.060 0.880
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.5155 0.5048 0.000 0.528 0.004 0.468
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0188 0.7931 0.996 0.000 0.004 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.5694 0.3159 0.080 0.000 0.224 0.696
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3769 0.7434 0.860 0.020 0.024 0.096
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4043 0.7129 0.008 0.812 0.012 0.168
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0188 0.7931 0.996 0.000 0.004 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.4304 0.7399 0.000 0.716 0.000 0.284
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.5779 0.2854 0.008 0.040 0.292 0.660
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0524 0.7927 0.988 0.000 0.004 0.008
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0188 0.7931 0.996 0.000 0.004 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.4304 0.7460 0.000 0.716 0.000 0.284
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.4790 0.6688 0.000 0.620 0.000 0.380
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0188 0.7127 0.000 0.996 0.004 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.3444 0.4867 0.000 0.816 0.184 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.4718 0.7397 0.008 0.716 0.004 0.272
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0524 0.7150 0.000 0.988 0.004 0.008
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1296 0.7843 0.964 0.004 0.004 0.028
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.4356 0.7376 0.000 0.708 0.000 0.292
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.4277 0.7478 0.000 0.720 0.000 0.280
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0188 0.7127 0.000 0.996 0.004 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.3751 0.6506 0.800 0.000 0.196 0.004
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 4 0.8103 0.0432 0.292 0.292 0.008 0.408
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0469 0.7177 0.000 0.988 0.000 0.012
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.6076 -0.0918 0.396 0.560 0.040 0.004
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.5691 0.5297 0.564 0.000 0.028 0.408
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.4799 0.7350 0.008 0.704 0.004 0.284
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.3444 0.4867 0.000 0.816 0.184 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0921 0.6952 0.000 0.972 0.028 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0469 0.7085 0.000 0.988 0.012 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.6127 -0.1667 0.016 0.408 0.024 0.552
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.8726 -0.0524 0.280 0.332 0.036 0.352
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.7717 -0.0418 0.000 0.384 0.392 0.224
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2921 0.7387 0.000 0.860 0.000 0.140
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2917 0.5977 0.008 0.048 0.040 0.904
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.4605 0.7156 0.000 0.664 0.000 0.336
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.3615 0.6157 0.024 0.036 0.064 0.876
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0188 0.7127 0.000 0.996 0.004 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.5399 0.5108 0.140 0.036 0.052 0.772
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.4304 0.7460 0.000 0.716 0.000 0.284
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.4304 0.7394 0.000 0.716 0.000 0.284
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.4605 0.7156 0.000 0.664 0.000 0.336
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1296 0.7843 0.964 0.004 0.004 0.028
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.3306 0.7558 0.000 0.840 0.004 0.156
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0188 0.7127 0.000 0.996 0.004 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5626 0.5622 0.588 0.000 0.028 0.384
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0376 0.7137 0.000 0.992 0.004 0.004
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.5638 0.5567 0.584 0.000 0.028 0.388
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.5573 0.4694 0.012 0.508 0.004 0.476
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.4584 0.7362 0.000 0.696 0.004 0.300
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.4769 0.7339 0.000 0.684 0.008 0.308
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.5875 0.3062 0.092 0.000 0.224 0.684
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.5691 0.5297 0.564 0.000 0.028 0.408
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1305 0.7270 0.000 0.960 0.004 0.036
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.1940 0.6446 0.000 0.924 0.076 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.3894 0.6171 0.028 0.044 0.064 0.864
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.7303 0.0906 0.036 0.084 0.568 0.312
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.4872 0.6995 0.000 0.640 0.004 0.356
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0188 0.7127 0.000 0.996 0.004 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.4509 0.7440 0.000 0.708 0.004 0.288
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0188 0.7127 0.000 0.996 0.004 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.5779 0.2854 0.008 0.040 0.292 0.660
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.3245 0.7595 0.872 0.000 0.028 0.100
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.4291 0.5994 0.028 0.040 0.092 0.840
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0188 0.7127 0.000 0.996 0.004 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.4331 0.7448 0.000 0.712 0.000 0.288
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.4790 0.6688 0.000 0.620 0.000 0.380
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.3529 0.1670 0.152 0.000 0.836 0.012
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.5097 0.5430 0.032 0.044 0.136 0.788
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0188 0.7127 0.000 0.996 0.004 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0921 0.6952 0.000 0.972 0.028 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1557 0.7638 0.944 0.000 0.056 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.2456 0.5895 0.008 0.040 0.028 0.924
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.4103 0.7570 0.000 0.744 0.000 0.256
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.4509 0.7440 0.000 0.708 0.004 0.288
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.5616 0.6515 0.012 0.604 0.012 0.372
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0188 0.7127 0.000 0.996 0.004 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.4164 0.7554 0.000 0.736 0.000 0.264
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.4304 0.7460 0.000 0.716 0.000 0.284
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3705 0.6168 0.024 0.040 0.064 0.872
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.4161 0.5226 0.000 0.608 0.000 0.000 0.392
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.4565 0.4824 0.000 0.580 0.000 0.012 0.408
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0162 0.6992 0.996 0.000 0.004 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.4951 0.4619 0.012 0.556 0.000 0.012 0.420
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.5122 0.4910 0.556 0.000 0.032 0.408 0.004
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.5810 0.3136 0.004 0.492 0.016 0.044 0.444
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.6995 1.000 0.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3906 0.7498 0.004 0.000 0.000 0.704 0.292
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.5996 -0.1088 0.000 0.388 0.000 0.116 0.496
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.6995 1.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2151 0.4892 0.040 0.000 0.016 0.924 0.020
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3981 0.6381 0.832 0.008 0.024 0.088 0.048
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4575 0.5075 0.000 0.736 0.012 0.040 0.212
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.6995 1.000 0.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.4658 0.4786 0.000 0.576 0.000 0.016 0.408
#> 0EA8288E-824D-4304-A053-5A833361F5C5 5 0.5234 -0.3163 0.004 0.000 0.052 0.332 0.612
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0290 0.6983 0.992 0.000 0.000 0.000 0.008
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.6995 1.000 0.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.4321 0.5062 0.000 0.600 0.000 0.004 0.396
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.5178 0.3322 0.000 0.484 0.000 0.040 0.476
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.5641 0.000 1.000 0.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4631 0.3400 0.000 0.752 0.076 0.008 0.164
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.4940 0.4847 0.008 0.584 0.004 0.012 0.392
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1082 0.5612 0.000 0.964 0.008 0.000 0.028
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0955 0.6825 0.968 0.000 0.000 0.004 0.028
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.4590 0.4727 0.000 0.568 0.000 0.012 0.420
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.4310 0.5103 0.000 0.604 0.000 0.004 0.392
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.5641 0.000 1.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 3 0.3689 0.0000 0.256 0.000 0.740 0.000 0.004
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.7607 0.3249 0.288 0.192 0.004 0.060 0.456
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.2054 0.5633 0.000 0.916 0.008 0.004 0.072
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.5787 -0.1121 0.092 0.564 0.340 0.000 0.004
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.5218 0.4716 0.536 0.000 0.036 0.424 0.004
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.4936 0.4666 0.012 0.564 0.000 0.012 0.412
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.4631 0.3400 0.000 0.752 0.076 0.008 0.164
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.2077 0.5197 0.000 0.920 0.040 0.000 0.040
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.1442 0.5385 0.000 0.952 0.032 0.004 0.012
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 5 0.6840 0.2104 0.004 0.324 0.000 0.256 0.416
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 5 0.8820 0.2774 0.084 0.252 0.224 0.056 0.384
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.6270 0.2125 0.000 0.292 0.064 0.056 0.588
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.3642 0.5441 0.000 0.760 0.000 0.008 0.232
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.4402 0.6660 0.004 0.004 0.000 0.620 0.372
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.4815 0.4241 0.000 0.524 0.000 0.020 0.456
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.3884 0.7509 0.004 0.000 0.000 0.708 0.288
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.5641 0.000 1.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.5685 0.6750 0.116 0.000 0.012 0.652 0.220
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.4321 0.5062 0.000 0.600 0.000 0.004 0.396
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.4574 0.4786 0.000 0.576 0.000 0.012 0.412
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.4815 0.4241 0.000 0.524 0.000 0.020 0.456
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0955 0.6825 0.968 0.000 0.000 0.004 0.028
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.3395 0.5630 0.000 0.764 0.000 0.000 0.236
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.5641 0.000 1.000 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5176 0.4963 0.560 0.000 0.036 0.400 0.004
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0898 0.5582 0.000 0.972 0.008 0.000 0.020
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.5289 0.4941 0.556 0.000 0.036 0.400 0.008
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 5 0.6283 -0.0937 0.008 0.388 0.004 0.104 0.496
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.4565 0.4824 0.000 0.580 0.000 0.012 0.408
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.4817 0.4731 0.000 0.572 0.000 0.024 0.404
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2605 0.4574 0.060 0.000 0.024 0.900 0.016
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.5218 0.4716 0.536 0.000 0.036 0.424 0.004
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1608 0.5709 0.000 0.928 0.000 0.000 0.072
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2790 0.4746 0.000 0.880 0.052 0.000 0.068
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.4127 0.7443 0.008 0.000 0.000 0.680 0.312
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.7025 0.1203 0.004 0.064 0.084 0.460 0.388
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.5036 0.4054 0.000 0.516 0.000 0.032 0.452
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.1444 0.5362 0.000 0.948 0.040 0.000 0.012
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.4527 0.5019 0.000 0.596 0.000 0.012 0.392
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.5641 0.000 1.000 0.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 5 0.5234 -0.3163 0.004 0.000 0.052 0.332 0.612
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.3250 0.6568 0.844 0.000 0.020 0.128 0.008
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.3928 0.7446 0.004 0.000 0.000 0.700 0.296
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.5641 0.000 1.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.4425 0.5051 0.000 0.600 0.000 0.008 0.392
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.5178 0.3322 0.000 0.484 0.000 0.040 0.476
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.7412 -0.5430 0.048 0.000 0.324 0.196 0.432
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.3579 0.7229 0.004 0.000 0.000 0.756 0.240
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.1331 0.5372 0.000 0.952 0.040 0.000 0.008
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2390 0.5138 0.000 0.908 0.044 0.004 0.044
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1792 0.6175 0.916 0.000 0.084 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.4480 0.6311 0.004 0.000 0.004 0.592 0.400
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.4138 0.5284 0.000 0.616 0.000 0.000 0.384
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.4527 0.5019 0.000 0.596 0.000 0.012 0.392
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.5810 0.3136 0.004 0.492 0.016 0.044 0.444
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.5641 0.000 1.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.4161 0.5226 0.000 0.608 0.000 0.000 0.392
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.4321 0.5062 0.000 0.600 0.000 0.004 0.396
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.4009 0.7447 0.004 0.000 0.000 0.684 0.312
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.3243 0.704 0.000 0.208 0.780 0.004 0.000 0.008
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.4193 0.694 0.000 0.228 0.724 0.036 0.004 0.008
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0146 0.755 0.996 0.000 0.000 0.000 0.004 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.2865 0.666 0.012 0.116 0.856 0.008 0.000 0.008
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.5466 0.547 0.528 0.036 0.000 0.396 0.024 0.016
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.4963 0.692 0.004 0.152 0.720 0.096 0.012 0.016
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.756 1.000 0.000 0.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.2178 0.743 0.000 0.000 0.132 0.868 0.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.3663 0.560 0.000 0.040 0.792 0.156 0.000 0.012
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.756 1.000 0.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.3837 0.444 0.012 0.024 0.008 0.788 0.004 0.164
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.4163 0.695 0.804 0.020 0.040 0.104 0.008 0.024
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.4958 0.159 0.000 0.392 0.556 0.036 0.012 0.004
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.756 1.000 0.000 0.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.2742 0.663 0.000 0.128 0.852 0.008 0.000 0.012
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.5977 0.303 0.000 0.004 0.224 0.476 0.000 0.296
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0260 0.755 0.992 0.000 0.008 0.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.756 1.000 0.000 0.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.3692 0.687 0.000 0.244 0.736 0.012 0.000 0.008
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.2475 0.672 0.000 0.036 0.892 0.060 0.000 0.012
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.2260 0.800 0.000 0.860 0.140 0.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4911 0.558 0.000 0.680 0.112 0.000 0.012 0.196
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.3077 0.664 0.008 0.136 0.836 0.008 0.000 0.012
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.3259 0.784 0.000 0.772 0.216 0.000 0.012 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0858 0.742 0.968 0.000 0.028 0.004 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.2656 0.667 0.000 0.120 0.860 0.008 0.000 0.012
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.3716 0.686 0.000 0.248 0.732 0.012 0.000 0.008
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.2668 0.787 0.000 0.828 0.168 0.000 0.004 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 5 0.1082 0.000 0.040 0.000 0.000 0.000 0.956 0.004
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.6878 0.166 0.284 0.044 0.476 0.176 0.000 0.020
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4238 0.618 0.000 0.636 0.340 0.000 0.008 0.016
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.5193 0.229 0.004 0.496 0.064 0.000 0.432 0.004
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.5689 0.523 0.508 0.036 0.004 0.408 0.024 0.020
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.3052 0.661 0.012 0.124 0.844 0.008 0.000 0.012
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.4911 0.558 0.000 0.680 0.112 0.000 0.012 0.196
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.2679 0.766 0.000 0.868 0.096 0.000 0.004 0.032
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2633 0.801 0.000 0.864 0.112 0.000 0.004 0.020
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.4397 0.168 0.000 0.024 0.596 0.376 0.000 0.004
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.7318 0.206 0.008 0.084 0.452 0.144 0.296 0.016
#> FF7021BB-414B-4466-BC43-372D856E6A1D 6 0.6677 0.140 0.000 0.144 0.396 0.048 0.008 0.404
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.4314 0.167 0.000 0.500 0.484 0.004 0.000 0.012
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3950 0.649 0.000 0.004 0.264 0.708 0.000 0.024
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.2186 0.687 0.000 0.056 0.908 0.024 0.000 0.012
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.2092 0.740 0.000 0.000 0.124 0.876 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.2260 0.800 0.000 0.860 0.140 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.4145 0.647 0.104 0.000 0.104 0.776 0.004 0.012
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.3692 0.687 0.000 0.244 0.736 0.012 0.000 0.008
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.2742 0.663 0.000 0.128 0.852 0.008 0.000 0.012
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.2186 0.687 0.000 0.056 0.908 0.024 0.000 0.012
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0858 0.742 0.968 0.000 0.028 0.004 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.4103 0.320 0.000 0.448 0.544 0.000 0.004 0.004
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.2260 0.800 0.000 0.860 0.140 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5521 0.555 0.536 0.036 0.000 0.384 0.024 0.020
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.3171 0.788 0.000 0.784 0.204 0.000 0.012 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.5660 0.550 0.528 0.036 0.004 0.388 0.024 0.020
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.4662 0.560 0.004 0.064 0.708 0.208 0.000 0.016
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.4193 0.694 0.000 0.228 0.724 0.036 0.004 0.008
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.4099 0.691 0.000 0.228 0.728 0.028 0.000 0.016
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.4327 0.403 0.028 0.032 0.000 0.764 0.016 0.160
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.5689 0.523 0.508 0.036 0.004 0.408 0.024 0.020
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.3380 0.703 0.000 0.748 0.244 0.000 0.004 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3318 0.738 0.000 0.828 0.084 0.000 0.004 0.084
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2695 0.745 0.004 0.000 0.144 0.844 0.000 0.008
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 6 0.5666 0.117 0.000 0.056 0.056 0.332 0.000 0.556
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.2670 0.708 0.000 0.064 0.880 0.048 0.004 0.004
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.2113 0.778 0.000 0.896 0.092 0.000 0.008 0.004
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.3934 0.689 0.000 0.240 0.728 0.020 0.000 0.012
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.2260 0.800 0.000 0.860 0.140 0.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.5977 0.303 0.000 0.004 0.224 0.476 0.000 0.296
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.3570 0.709 0.816 0.024 0.000 0.132 0.008 0.020
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.3646 0.728 0.000 0.000 0.172 0.776 0.000 0.052
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.2668 0.787 0.000 0.828 0.168 0.000 0.004 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.3780 0.687 0.000 0.244 0.732 0.016 0.000 0.008
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.2475 0.672 0.000 0.036 0.892 0.060 0.000 0.012
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.2179 -0.122 0.024 0.008 0.000 0.012 0.040 0.916
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.4302 0.700 0.000 0.000 0.156 0.728 0.000 0.116
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.2163 0.781 0.000 0.892 0.096 0.000 0.008 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2752 0.759 0.000 0.864 0.096 0.000 0.004 0.036
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1753 0.699 0.912 0.000 0.000 0.000 0.084 0.004
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.3680 0.683 0.000 0.004 0.232 0.744 0.000 0.020
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.3301 0.696 0.000 0.216 0.772 0.004 0.000 0.008
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.3934 0.689 0.000 0.240 0.728 0.020 0.000 0.012
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.4963 0.692 0.004 0.152 0.720 0.096 0.012 0.016
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.2668 0.787 0.000 0.828 0.168 0.000 0.004 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.3243 0.704 0.000 0.208 0.780 0.004 0.000 0.008
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.3692 0.687 0.000 0.244 0.736 0.012 0.000 0.008
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.2553 0.744 0.000 0.000 0.144 0.848 0.000 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17471 rows and 87 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 0.976 0.990 0.4928 0.509 0.509
#> 3 3 0.528 0.695 0.813 0.2931 0.638 0.410
#> 4 4 0.878 0.886 0.910 0.1467 0.823 0.555
#> 5 5 0.761 0.618 0.829 0.0736 0.957 0.841
#> 6 6 0.744 0.671 0.791 0.0448 0.908 0.643
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0000 0.986 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.986 0.000 1.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.993 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0376 0.992 0.996 0.004
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.993 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0000 0.986 0.000 1.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.993 1.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0376 0.992 0.996 0.004
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.986 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.993 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.993 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.993 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.986 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.993 1.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.986 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0376 0.992 0.996 0.004
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.993 1.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.993 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.986 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.0000 0.986 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.986 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.986 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0000 0.986 0.000 1.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.986 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.993 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0000 0.986 0.000 1.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0000 0.986 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.986 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.993 1.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.0376 0.992 0.996 0.004
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.986 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.986 0.000 1.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.993 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.7674 0.712 0.224 0.776
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.986 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.986 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.986 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.993 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0376 0.992 0.996 0.004
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0000 0.986 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.986 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.6801 0.777 0.820 0.180
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.986 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.993 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.986 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.993 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0000 0.986 0.000 1.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.986 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.3274 0.929 0.060 0.940
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.993 1.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.986 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.986 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.993 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.986 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.993 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0376 0.992 0.996 0.004
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.986 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0000 0.986 0.000 1.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.993 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.993 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.986 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.986 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0376 0.992 0.996 0.004
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.9358 0.458 0.352 0.648
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.2236 0.953 0.036 0.964
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.986 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0000 0.986 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.986 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0376 0.992 0.996 0.004
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.993 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0376 0.992 0.996 0.004
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.986 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0000 0.986 0.000 1.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.986 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.993 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0376 0.992 0.996 0.004
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.986 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.986 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.993 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0376 0.992 0.996 0.004
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.986 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0000 0.986 0.000 1.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0000 0.986 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.986 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.986 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.986 0.000 1.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0376 0.992 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.5431 0.6496 0.000 0.284 0.716
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.5178 0.6872 0.000 0.256 0.744
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.8825 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.6529 0.4392 0.368 0.012 0.620
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.3412 0.8385 0.876 0.000 0.124
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.5216 0.6857 0.000 0.260 0.740
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0237 0.8817 0.996 0.000 0.004
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.5591 0.2896 0.304 0.000 0.696
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.4887 0.6978 0.000 0.228 0.772
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.8825 1.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.5968 0.6476 0.636 0.000 0.364
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.8825 1.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4974 0.5824 0.000 0.764 0.236
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0237 0.8817 0.996 0.000 0.004
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.5591 0.6380 0.000 0.304 0.696
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.4291 0.4711 0.180 0.000 0.820
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0237 0.8817 0.996 0.000 0.004
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0237 0.8817 0.996 0.000 0.004
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.5621 0.6349 0.000 0.308 0.692
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3619 0.6902 0.000 0.136 0.864
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.9201 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0747 0.9112 0.000 0.984 0.016
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.6252 0.0224 0.000 0.556 0.444
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.9201 0.000 1.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0237 0.8817 0.996 0.000 0.004
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.5016 0.6946 0.000 0.240 0.760
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.6026 0.5197 0.000 0.376 0.624
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.9201 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.8825 1.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.5529 0.5028 0.296 0.000 0.704
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0237 0.9176 0.000 0.996 0.004
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.9201 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.5968 0.6476 0.636 0.000 0.364
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.7872 0.5417 0.296 0.084 0.620
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0747 0.9112 0.000 0.984 0.016
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0592 0.9138 0.000 0.988 0.012
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0592 0.9138 0.000 0.988 0.012
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.5591 0.2896 0.304 0.000 0.696
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.6140 0.2463 0.404 0.000 0.596
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.4796 0.6967 0.000 0.220 0.780
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.4842 0.6231 0.000 0.776 0.224
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.1031 0.6267 0.024 0.000 0.976
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.6008 0.5240 0.000 0.372 0.628
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.5591 0.2896 0.304 0.000 0.696
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.9201 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.5706 0.7064 0.680 0.000 0.320
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.5785 0.5992 0.000 0.332 0.668
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.6235 0.0531 0.000 0.564 0.436
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4555 0.6994 0.000 0.200 0.800
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0237 0.8817 0.996 0.000 0.004
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0892 0.9043 0.000 0.980 0.020
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9201 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3192 0.8440 0.888 0.000 0.112
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.9201 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.5216 0.7574 0.740 0.000 0.260
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.1031 0.6256 0.024 0.000 0.976
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.5216 0.6857 0.000 0.260 0.740
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.5216 0.6857 0.000 0.260 0.740
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.5785 0.6916 0.668 0.000 0.332
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.5810 0.6879 0.664 0.000 0.336
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.9201 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0747 0.9112 0.000 0.984 0.016
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.5529 0.3074 0.296 0.000 0.704
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.8033 0.4457 0.120 0.240 0.640
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.5365 0.6891 0.004 0.252 0.744
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.9201 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.4452 0.6979 0.000 0.192 0.808
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.9201 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.5397 0.3190 0.280 0.000 0.720
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.8825 1.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.5397 0.3375 0.280 0.000 0.720
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.9201 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.5178 0.6872 0.000 0.256 0.744
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4887 0.6976 0.000 0.228 0.772
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0747 0.8727 0.984 0.000 0.016
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.5591 0.2896 0.304 0.000 0.696
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9201 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0747 0.9112 0.000 0.984 0.016
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.8825 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.5431 0.3304 0.284 0.000 0.716
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.5327 0.6756 0.000 0.272 0.728
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.4842 0.6983 0.000 0.224 0.776
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.4974 0.6957 0.000 0.236 0.764
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.9201 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.5591 0.6380 0.000 0.304 0.696
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.5254 0.6827 0.000 0.264 0.736
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.5497 0.3157 0.292 0.000 0.708
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.2218 0.909 0.004 0.036 0.932 0.028
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1114 0.922 0.004 0.008 0.972 0.016
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1305 0.960 0.960 0.004 0.000 0.036
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.3188 0.855 0.112 0.008 0.872 0.008
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.4153 0.778 0.784 0.008 0.004 0.204
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.1124 0.922 0.004 0.012 0.972 0.012
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.1396 0.960 0.960 0.004 0.004 0.032
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.2319 0.946 0.036 0.000 0.040 0.924
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.1724 0.919 0.020 0.000 0.948 0.032
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.1305 0.960 0.960 0.004 0.000 0.036
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2384 0.925 0.072 0.004 0.008 0.916
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1822 0.952 0.944 0.004 0.008 0.044
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.6016 0.129 0.016 0.504 0.464 0.016
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1396 0.960 0.960 0.004 0.004 0.032
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.2089 0.910 0.020 0.028 0.940 0.012
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.2207 0.915 0.004 0.024 0.040 0.932
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1543 0.960 0.956 0.008 0.004 0.032
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1543 0.960 0.956 0.008 0.004 0.032
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.1471 0.918 0.004 0.024 0.960 0.012
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1118 0.918 0.000 0.000 0.964 0.036
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.2256 0.933 0.000 0.924 0.056 0.020
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.3120 0.888 0.012 0.896 0.036 0.056
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.2421 0.898 0.020 0.048 0.924 0.008
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1807 0.935 0.000 0.940 0.052 0.008
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1543 0.960 0.956 0.008 0.004 0.032
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.1520 0.917 0.024 0.000 0.956 0.020
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1575 0.914 0.004 0.028 0.956 0.012
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.1661 0.935 0.000 0.944 0.052 0.004
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1639 0.958 0.952 0.008 0.004 0.036
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.5099 0.792 0.116 0.024 0.792 0.068
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.3466 0.908 0.020 0.876 0.084 0.020
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.1635 0.933 0.000 0.948 0.044 0.008
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.2515 0.929 0.072 0.004 0.012 0.912
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.4218 0.784 0.184 0.012 0.796 0.008
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.3039 0.890 0.012 0.900 0.036 0.052
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.1488 0.926 0.000 0.956 0.032 0.012
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2261 0.921 0.008 0.932 0.036 0.024
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.2505 0.945 0.036 0.004 0.040 0.920
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.7945 0.159 0.216 0.012 0.468 0.304
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.3365 0.882 0.024 0.032 0.888 0.056
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.5443 0.477 0.020 0.312 0.660 0.008
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2593 0.883 0.004 0.000 0.104 0.892
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.1854 0.913 0.020 0.024 0.948 0.008
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.2227 0.945 0.036 0.000 0.036 0.928
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1661 0.935 0.000 0.944 0.052 0.004
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.2515 0.929 0.072 0.004 0.012 0.912
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.1593 0.918 0.004 0.024 0.956 0.016
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.2405 0.901 0.020 0.036 0.928 0.016
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.1706 0.917 0.016 0.000 0.948 0.036
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1443 0.957 0.960 0.008 0.004 0.028
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.5250 0.530 0.004 0.640 0.344 0.012
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.1938 0.935 0.000 0.936 0.052 0.012
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3727 0.833 0.824 0.008 0.004 0.164
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1807 0.935 0.000 0.940 0.052 0.008
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.4533 0.729 0.232 0.004 0.012 0.752
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.4677 0.555 0.000 0.004 0.680 0.316
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.1229 0.922 0.004 0.008 0.968 0.020
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.1229 0.922 0.004 0.008 0.968 0.020
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2860 0.904 0.100 0.004 0.008 0.888
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.2515 0.929 0.072 0.004 0.012 0.912
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1807 0.935 0.000 0.940 0.052 0.008
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2165 0.913 0.008 0.936 0.032 0.024
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2319 0.946 0.036 0.000 0.040 0.924
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.2781 0.877 0.012 0.040 0.036 0.912
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0967 0.923 0.004 0.004 0.976 0.016
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.1474 0.935 0.000 0.948 0.052 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1543 0.918 0.004 0.008 0.956 0.032
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.2174 0.934 0.000 0.928 0.052 0.020
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.2207 0.916 0.004 0.024 0.040 0.932
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1585 0.957 0.952 0.004 0.004 0.040
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.2319 0.946 0.036 0.000 0.040 0.924
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.1661 0.935 0.000 0.944 0.052 0.004
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.1229 0.922 0.004 0.008 0.968 0.020
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.1388 0.921 0.012 0.000 0.960 0.028
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.3330 0.891 0.884 0.032 0.012 0.072
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.2319 0.946 0.036 0.000 0.040 0.924
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.1661 0.935 0.000 0.944 0.052 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.1843 0.915 0.008 0.948 0.028 0.016
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1305 0.959 0.960 0.000 0.004 0.036
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.2499 0.944 0.032 0.004 0.044 0.920
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.1229 0.921 0.004 0.020 0.968 0.008
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.1109 0.920 0.004 0.000 0.968 0.028
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.1191 0.921 0.004 0.004 0.968 0.024
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.1661 0.935 0.000 0.944 0.052 0.004
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1004 0.918 0.004 0.024 0.972 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.1484 0.921 0.004 0.016 0.960 0.020
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.2319 0.946 0.036 0.000 0.040 0.924
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0510 0.60587 0.000 0.000 0.984 0.000 0.016
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0451 0.60943 0.000 0.008 0.988 0.000 0.004
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0451 0.88667 0.988 0.000 0.000 0.004 0.008
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.6120 0.62250 0.112 0.000 0.400 0.004 0.484
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.6229 0.45597 0.540 0.000 0.000 0.268 0.192
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0579 0.60888 0.000 0.008 0.984 0.000 0.008
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0671 0.88618 0.980 0.000 0.000 0.004 0.016
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.1267 0.90081 0.004 0.000 0.012 0.960 0.024
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.4908 -0.33014 0.000 0.004 0.560 0.020 0.416
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0451 0.88667 0.988 0.000 0.000 0.004 0.008
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2777 0.86975 0.016 0.000 0.000 0.864 0.120
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3526 0.82895 0.832 0.000 0.000 0.096 0.072
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.6451 -0.06817 0.000 0.496 0.172 0.004 0.328
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1041 0.88368 0.964 0.000 0.000 0.004 0.032
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.4692 -0.40890 0.000 0.004 0.528 0.008 0.460
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.2900 0.87230 0.000 0.000 0.028 0.864 0.108
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1357 0.87994 0.948 0.000 0.000 0.004 0.048
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0865 0.88546 0.972 0.000 0.000 0.004 0.024
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0451 0.60869 0.000 0.008 0.988 0.000 0.004
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3047 0.45324 0.000 0.004 0.832 0.004 0.160
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.2536 0.85595 0.000 0.868 0.000 0.004 0.128
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4074 0.77356 0.004 0.720 0.004 0.004 0.268
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.5372 -0.46869 0.000 0.044 0.504 0.004 0.448
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0324 0.89101 0.000 0.992 0.000 0.004 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2389 0.84002 0.880 0.000 0.000 0.004 0.116
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.4692 -0.40798 0.000 0.004 0.528 0.008 0.460
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1731 0.57245 0.000 0.004 0.932 0.004 0.060
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0324 0.89101 0.000 0.992 0.000 0.004 0.004
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2513 0.86294 0.876 0.000 0.000 0.008 0.116
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.6827 -0.41149 0.076 0.000 0.480 0.068 0.376
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4604 0.44896 0.000 0.584 0.008 0.004 0.404
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.1121 0.87596 0.000 0.956 0.000 0.000 0.044
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.2408 0.87785 0.016 0.000 0.000 0.892 0.092
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.6249 0.62290 0.128 0.000 0.392 0.004 0.476
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.3972 0.78505 0.004 0.736 0.004 0.004 0.252
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.1952 0.87788 0.000 0.912 0.000 0.004 0.084
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3280 0.83504 0.000 0.808 0.004 0.004 0.184
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.2054 0.89854 0.004 0.000 0.008 0.916 0.072
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.6897 0.13444 0.080 0.000 0.580 0.212 0.128
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.4510 0.51131 0.000 0.000 0.432 0.008 0.560
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 5 0.6897 0.41674 0.000 0.292 0.304 0.004 0.400
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3752 0.82676 0.000 0.000 0.124 0.812 0.064
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.4695 -0.41817 0.000 0.008 0.524 0.004 0.464
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1740 0.89477 0.000 0.000 0.012 0.932 0.056
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0162 0.89164 0.000 0.996 0.000 0.000 0.004
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.2408 0.87785 0.016 0.000 0.000 0.892 0.092
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0290 0.60938 0.000 0.008 0.992 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.5114 -0.46236 0.000 0.028 0.512 0.004 0.456
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4814 -0.28562 0.000 0.004 0.568 0.016 0.412
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2389 0.84002 0.880 0.000 0.000 0.004 0.116
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.4318 0.10803 0.000 0.348 0.644 0.004 0.004
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0865 0.88966 0.000 0.972 0.000 0.004 0.024
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5502 0.64910 0.652 0.000 0.000 0.192 0.156
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0324 0.89101 0.000 0.992 0.000 0.004 0.004
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.4545 0.74433 0.116 0.000 0.000 0.752 0.132
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.6681 -0.11089 0.004 0.000 0.448 0.340 0.208
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0290 0.60938 0.000 0.008 0.992 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0324 0.60671 0.000 0.004 0.992 0.000 0.004
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.4152 0.82118 0.060 0.000 0.000 0.772 0.168
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.2408 0.87785 0.016 0.000 0.000 0.892 0.092
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0324 0.89101 0.000 0.992 0.000 0.004 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3128 0.84391 0.000 0.824 0.004 0.004 0.168
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.1412 0.90108 0.004 0.000 0.008 0.952 0.036
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.4810 0.75951 0.004 0.016 0.020 0.692 0.268
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.3353 0.40001 0.000 0.008 0.796 0.000 0.196
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0566 0.89170 0.000 0.984 0.000 0.004 0.012
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0324 0.60537 0.000 0.000 0.992 0.004 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1571 0.88252 0.000 0.936 0.000 0.004 0.060
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.2582 0.88043 0.004 0.000 0.024 0.892 0.080
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1502 0.87938 0.940 0.000 0.000 0.004 0.056
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.2351 0.89175 0.000 0.000 0.016 0.896 0.088
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0162 0.89128 0.000 0.996 0.000 0.004 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0162 0.60878 0.000 0.004 0.996 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4317 0.00488 0.000 0.004 0.668 0.008 0.320
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.4111 0.73805 0.708 0.000 0.008 0.004 0.280
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.2305 0.89044 0.000 0.000 0.012 0.896 0.092
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0162 0.89128 0.000 0.996 0.000 0.004 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.3006 0.85292 0.000 0.836 0.004 0.004 0.156
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1764 0.87549 0.928 0.000 0.000 0.008 0.064
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1934 0.89631 0.004 0.000 0.016 0.928 0.052
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.1412 0.59366 0.000 0.008 0.952 0.004 0.036
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0566 0.60436 0.000 0.000 0.984 0.004 0.012
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0579 0.60860 0.000 0.008 0.984 0.000 0.008
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0162 0.89128 0.000 0.996 0.000 0.004 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1443 0.58748 0.000 0.004 0.948 0.004 0.044
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0162 0.60878 0.000 0.004 0.996 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.1830 0.89523 0.000 0.000 0.008 0.924 0.068
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0820 0.8728 0.000 0.000 0.972 0.000 0.016 0.012
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0146 0.8804 0.000 0.000 0.996 0.000 0.000 0.004
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0547 0.7517 0.980 0.000 0.000 0.000 0.000 0.020
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.4668 0.6696 0.160 0.000 0.136 0.000 0.700 0.004
#> 2F38E3B1-4975-4877-9DCC-C00270602180 6 0.6086 0.2702 0.336 0.000 0.000 0.280 0.000 0.384
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0622 0.8760 0.000 0.000 0.980 0.000 0.012 0.008
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0777 0.7566 0.972 0.000 0.000 0.000 0.024 0.004
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0858 0.7346 0.000 0.000 0.000 0.968 0.028 0.004
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.5215 0.7189 0.000 0.016 0.280 0.048 0.636 0.020
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0547 0.7517 0.980 0.000 0.000 0.000 0.000 0.020
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.3645 0.6270 0.000 0.000 0.000 0.740 0.024 0.236
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.4876 0.4542 0.720 0.000 0.000 0.112 0.040 0.128
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 5 0.5726 0.3089 0.000 0.356 0.060 0.000 0.532 0.052
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0972 0.7568 0.964 0.000 0.000 0.000 0.028 0.008
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.3875 0.7360 0.000 0.016 0.280 0.000 0.700 0.004
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.3472 0.6740 0.000 0.000 0.000 0.808 0.100 0.092
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1225 0.7520 0.952 0.000 0.000 0.000 0.036 0.012
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0632 0.7559 0.976 0.000 0.000 0.000 0.024 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0458 0.8786 0.000 0.000 0.984 0.000 0.000 0.016
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3566 0.5087 0.000 0.000 0.744 0.000 0.236 0.020
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.4390 0.7780 0.000 0.720 0.000 0.000 0.148 0.132
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.5814 0.5024 0.000 0.448 0.000 0.000 0.188 0.364
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.4950 0.7403 0.004 0.092 0.200 0.000 0.688 0.016
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1418 0.8499 0.000 0.944 0.000 0.000 0.024 0.032
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2402 0.6410 0.856 0.000 0.000 0.000 0.140 0.004
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.4078 0.7406 0.000 0.016 0.272 0.008 0.700 0.004
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1398 0.8400 0.000 0.000 0.940 0.000 0.052 0.008
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0260 0.8516 0.000 0.992 0.000 0.000 0.000 0.008
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.3432 0.6306 0.800 0.000 0.000 0.000 0.052 0.148
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.7186 0.5467 0.048 0.000 0.240 0.120 0.512 0.080
#> 36EDD202-A845-4CE7-95D5-A515C471262E 5 0.5399 0.0784 0.000 0.360 0.004 0.000 0.528 0.108
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2088 0.8324 0.000 0.904 0.000 0.000 0.028 0.068
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.3043 0.6635 0.000 0.000 0.000 0.792 0.008 0.200
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.4769 0.6861 0.140 0.000 0.144 0.000 0.704 0.012
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.5590 0.5575 0.000 0.496 0.000 0.000 0.152 0.352
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.3394 0.8201 0.000 0.804 0.000 0.000 0.052 0.144
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.4704 0.7432 0.000 0.664 0.000 0.000 0.100 0.236
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1983 0.7263 0.000 0.000 0.000 0.908 0.020 0.072
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.7653 0.0891 0.060 0.000 0.456 0.244 0.088 0.152
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.4798 0.6573 0.000 0.000 0.172 0.000 0.672 0.156
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 5 0.5486 0.6437 0.000 0.232 0.132 0.000 0.616 0.020
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.4585 0.6283 0.000 0.000 0.116 0.752 0.076 0.056
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.4077 0.7360 0.000 0.016 0.280 0.000 0.692 0.012
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.2003 0.7154 0.000 0.000 0.000 0.884 0.000 0.116
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1391 0.8534 0.000 0.944 0.000 0.000 0.016 0.040
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.2933 0.6660 0.000 0.000 0.000 0.796 0.004 0.200
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0146 0.8798 0.000 0.000 0.996 0.000 0.000 0.004
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.4022 0.7416 0.000 0.020 0.272 0.000 0.700 0.008
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.4809 0.6975 0.000 0.000 0.296 0.044 0.640 0.020
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2402 0.6421 0.856 0.000 0.000 0.000 0.140 0.004
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3913 0.5920 0.000 0.200 0.756 0.000 0.020 0.024
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.2433 0.8466 0.000 0.884 0.000 0.000 0.044 0.072
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.6346 -0.3885 0.448 0.000 0.000 0.168 0.032 0.352
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1261 0.8509 0.000 0.952 0.000 0.000 0.024 0.024
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.5152 0.4448 0.052 0.000 0.000 0.628 0.036 0.284
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.7267 0.0636 0.000 0.000 0.228 0.416 0.228 0.128
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0363 0.8791 0.000 0.000 0.988 0.000 0.000 0.012
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0146 0.8798 0.000 0.000 0.996 0.000 0.000 0.004
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.4284 0.4787 0.016 0.000 0.000 0.588 0.004 0.392
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.2964 0.6631 0.000 0.000 0.000 0.792 0.004 0.204
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1148 0.8493 0.000 0.960 0.004 0.000 0.020 0.016
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.4662 0.7505 0.000 0.668 0.000 0.000 0.096 0.236
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0993 0.7354 0.000 0.000 0.000 0.964 0.012 0.024
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.5391 0.3096 0.000 0.000 0.000 0.492 0.116 0.392
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.4078 0.2020 0.000 0.000 0.640 0.000 0.340 0.020
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0520 0.8527 0.000 0.984 0.000 0.000 0.008 0.008
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0458 0.8786 0.000 0.000 0.984 0.000 0.000 0.016
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3453 0.8263 0.000 0.804 0.000 0.000 0.064 0.132
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3566 0.6745 0.000 0.000 0.000 0.800 0.104 0.096
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.2762 0.5646 0.804 0.000 0.000 0.000 0.000 0.196
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.3123 0.7052 0.000 0.000 0.000 0.836 0.076 0.088
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0260 0.8516 0.000 0.992 0.000 0.000 0.000 0.008
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0146 0.8798 0.000 0.000 0.996 0.000 0.000 0.004
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 5 0.4336 0.4032 0.000 0.000 0.476 0.000 0.504 0.020
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.5294 0.0355 0.436 0.000 0.000 0.000 0.100 0.464
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.3268 0.7009 0.000 0.000 0.000 0.824 0.076 0.100
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0260 0.8516 0.000 0.992 0.000 0.000 0.000 0.008
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.4305 0.7450 0.000 0.684 0.000 0.000 0.056 0.260
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2560 0.6891 0.872 0.000 0.000 0.000 0.036 0.092
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1995 0.7223 0.000 0.000 0.000 0.912 0.052 0.036
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0622 0.8760 0.000 0.000 0.980 0.000 0.012 0.008
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0520 0.8794 0.000 0.000 0.984 0.000 0.008 0.008
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0806 0.8770 0.000 0.000 0.972 0.000 0.008 0.020
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0260 0.8516 0.000 0.992 0.000 0.000 0.000 0.008
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0820 0.8728 0.000 0.000 0.972 0.000 0.016 0.012
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0458 0.8786 0.000 0.000 0.984 0.000 0.000 0.016
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.2145 0.7178 0.000 0.000 0.000 0.900 0.028 0.072
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 17471 rows and 87 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.953 0.982 0.4992 0.500 0.500
#> 3 3 0.965 0.926 0.968 0.3275 0.809 0.628
#> 4 4 0.914 0.899 0.955 0.1374 0.869 0.635
#> 5 5 0.875 0.852 0.923 0.0615 0.928 0.723
#> 6 6 0.818 0.661 0.821 0.0344 0.983 0.918
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0000 0.989 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.989 0.000 1.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.971 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.971 1.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.971 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0000 0.989 0.000 1.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.971 1.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0000 0.971 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.989 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.971 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.971 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.971 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.989 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.971 1.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.989 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0000 0.971 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.971 1.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.971 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.989 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.4431 0.888 0.092 0.908
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.989 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.989 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0000 0.989 0.000 1.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.989 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.971 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0000 0.989 0.000 1.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0000 0.989 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.989 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.971 1.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.0000 0.971 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.989 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.989 0.000 1.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.971 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.9427 0.458 0.640 0.360
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.989 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.989 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.989 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.971 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.971 1.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0000 0.989 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.989 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.0000 0.971 1.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.989 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.971 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.989 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.971 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0000 0.989 0.000 1.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.989 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.9732 0.349 0.596 0.404
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.971 1.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.989 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.989 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.971 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.989 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.971 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.971 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.989 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0000 0.989 0.000 1.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.971 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.971 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.989 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.989 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0000 0.971 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.9775 0.283 0.412 0.588
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.9044 0.540 0.680 0.320
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.989 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0000 0.989 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.989 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0000 0.971 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.971 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.971 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.989 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0376 0.985 0.004 0.996
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.989 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.971 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0000 0.971 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.989 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.989 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.971 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.971 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.989 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0000 0.989 0.000 1.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0000 0.989 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.989 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.989 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.989 0.000 1.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0000 0.971 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0592 0.9769 0.000 0.012 0.988
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0592 0.9769 0.000 0.012 0.988
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.9776 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0892 0.9643 0.980 0.000 0.020
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.9776 1.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0592 0.9769 0.000 0.012 0.988
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.9776 1.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0592 0.9767 0.988 0.000 0.012
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.4796 0.7274 0.000 0.780 0.220
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.9776 1.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0592 0.9767 0.988 0.000 0.012
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.9776 1.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.9433 0.000 1.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.9776 1.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.2959 0.8627 0.000 0.900 0.100
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.1411 0.9595 0.964 0.000 0.036
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.9776 1.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.9776 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0592 0.9769 0.000 0.012 0.988
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.9725 0.000 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.9433 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.9433 0.000 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0592 0.9333 0.012 0.988 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.9433 0.000 1.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.9776 1.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.6309 0.0855 0.000 0.504 0.496
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0592 0.9769 0.000 0.012 0.988
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.9433 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.9776 1.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.0424 0.9745 0.992 0.000 0.008
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.9433 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.9433 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0592 0.9767 0.988 0.000 0.012
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.8068 -0.0245 0.480 0.456 0.064
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.9433 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.9433 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.9433 0.000 1.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0592 0.9767 0.988 0.000 0.012
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.9776 1.000 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.1411 0.9497 0.000 0.036 0.964
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.9433 0.000 1.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.2711 0.8890 0.088 0.000 0.912
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.4654 0.7365 0.000 0.792 0.208
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0592 0.9767 0.988 0.000 0.012
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.9433 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0592 0.9767 0.988 0.000 0.012
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0592 0.9769 0.000 0.012 0.988
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.2066 0.8984 0.000 0.940 0.060
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.1529 0.9419 0.040 0.000 0.960
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.9776 1.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.4452 0.7669 0.000 0.192 0.808
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9433 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.9776 1.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.9433 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.9776 1.000 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.1289 0.9627 0.968 0.000 0.032
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0592 0.9769 0.000 0.012 0.988
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0592 0.9769 0.000 0.012 0.988
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0592 0.9767 0.988 0.000 0.012
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0592 0.9767 0.988 0.000 0.012
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.9433 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.9433 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0592 0.9767 0.988 0.000 0.012
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.6896 0.3096 0.392 0.588 0.020
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0747 0.9689 0.016 0.000 0.984
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.9433 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.9725 0.000 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.9433 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0892 0.9725 0.980 0.000 0.020
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.9776 1.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0592 0.9767 0.988 0.000 0.012
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.9433 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0592 0.9769 0.000 0.012 0.988
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0000 0.9725 0.000 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0237 0.9762 0.996 0.000 0.004
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0592 0.9767 0.988 0.000 0.012
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9433 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.9433 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.9776 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0592 0.9767 0.988 0.000 0.012
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0592 0.9769 0.000 0.012 0.988
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0000 0.9725 0.000 0.000 1.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.9725 0.000 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.9433 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0592 0.9769 0.000 0.012 0.988
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0592 0.9769 0.000 0.012 0.988
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0592 0.9767 0.988 0.000 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.970 0.000 0.000 1.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0188 0.972 0.000 0.004 0.996 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.4605 0.488 0.664 0.000 0.000 0.336
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0188 0.972 0.000 0.004 0.996 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.6477 0.513 0.000 0.264 0.116 0.620
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0921 0.912 0.028 0.000 0.000 0.972
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1302 0.910 0.956 0.000 0.000 0.044
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.3486 0.776 0.000 0.812 0.188 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0188 0.972 0.000 0.004 0.996 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0188 0.970 0.000 0.000 0.996 0.004
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0188 0.963 0.000 0.996 0.004 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.2814 0.838 0.132 0.868 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.6029 0.386 0.016 0.588 0.372 0.024
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0188 0.969 0.000 0.004 0.996 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.1940 0.886 0.924 0.000 0.000 0.076
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0188 0.963 0.000 0.996 0.004 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.0188 0.934 0.996 0.004 0.000 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0188 0.963 0.000 0.996 0.004 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0188 0.963 0.000 0.996 0.004 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3726 0.739 0.788 0.000 0.000 0.212
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.1042 0.951 0.000 0.008 0.972 0.020
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0188 0.963 0.000 0.996 0.004 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.1792 0.881 0.000 0.000 0.068 0.932
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.2647 0.855 0.000 0.880 0.120 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0188 0.972 0.000 0.004 0.996 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.1474 0.927 0.000 0.948 0.052 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.4164 0.634 0.000 0.000 0.264 0.736
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3074 0.810 0.000 0.152 0.848 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.4817 0.383 0.612 0.000 0.000 0.388
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.4877 0.201 0.408 0.000 0.000 0.592
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.1042 0.912 0.020 0.000 0.008 0.972
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0188 0.972 0.000 0.004 0.996 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0188 0.972 0.000 0.004 0.996 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2216 0.860 0.092 0.000 0.000 0.908
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0188 0.963 0.000 0.996 0.004 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.2310 0.886 0.028 0.040 0.004 0.928
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.4304 0.601 0.284 0.000 0.716 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.970 0.000 0.000 1.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0188 0.972 0.000 0.004 0.996 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0000 0.970 0.000 0.000 1.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0188 0.935 0.996 0.000 0.004 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0921 0.912 0.028 0.000 0.000 0.972
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0188 0.963 0.000 0.996 0.004 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.937 1.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0000 0.925 0.000 0.000 0.000 1.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0592 0.963 0.000 0.016 0.984 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0188 0.970 0.000 0.000 0.996 0.004
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0188 0.972 0.000 0.004 0.996 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0000 0.970 0.000 0.000 1.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0188 0.972 0.000 0.004 0.996 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.925 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0963 0.905 0.000 0.000 0.964 0.000 0.036
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.921 0.000 0.000 1.000 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.882 1.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.2329 0.782 0.124 0.000 0.000 0.000 0.876
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.3480 0.679 0.752 0.000 0.000 0.248 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0000 0.921 0.000 0.000 1.000 0.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0609 0.880 0.980 0.000 0.000 0.000 0.020
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0162 0.936 0.000 0.000 0.000 0.996 0.004
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.5475 0.480 0.000 0.076 0.004 0.308 0.612
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.882 1.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.1800 0.912 0.048 0.000 0.000 0.932 0.020
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1697 0.857 0.932 0.000 0.000 0.060 0.008
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0609 0.945 0.000 0.980 0.000 0.000 0.020
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0703 0.879 0.976 0.000 0.000 0.000 0.024
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.2193 0.831 0.000 0.060 0.028 0.000 0.912
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.1270 0.922 0.000 0.000 0.000 0.948 0.052
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0703 0.879 0.976 0.000 0.000 0.000 0.024
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0609 0.880 0.980 0.000 0.000 0.000 0.020
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.921 0.000 0.000 1.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3305 0.702 0.000 0.000 0.776 0.000 0.224
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.2020 0.885 0.000 0.900 0.000 0.000 0.100
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.4958 0.343 0.032 0.400 0.000 0.000 0.568
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2280 0.814 0.880 0.000 0.000 0.000 0.120
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.2073 0.828 0.016 0.008 0.032 0.012 0.932
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1544 0.889 0.000 0.000 0.932 0.000 0.068
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.882 1.000 0.000 0.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.4054 0.797 0.800 0.000 0.004 0.080 0.116
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4192 0.253 0.000 0.596 0.000 0.000 0.404
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0798 0.933 0.016 0.000 0.000 0.976 0.008
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.2732 0.749 0.160 0.000 0.000 0.000 0.840
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.1544 0.913 0.000 0.932 0.000 0.000 0.068
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0609 0.947 0.000 0.980 0.000 0.000 0.020
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0404 0.935 0.000 0.000 0.000 0.988 0.012
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4147 0.772 0.796 0.000 0.056 0.136 0.012
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.1892 0.791 0.000 0.000 0.080 0.004 0.916
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2605 0.798 0.000 0.852 0.000 0.000 0.148
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2597 0.864 0.000 0.000 0.092 0.884 0.024
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.2046 0.833 0.000 0.068 0.016 0.000 0.916
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.936 0.000 0.000 0.000 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0992 0.931 0.024 0.000 0.000 0.968 0.008
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.921 0.000 0.000 1.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.2408 0.822 0.004 0.096 0.008 0.000 0.892
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.2900 0.794 0.000 0.000 0.028 0.108 0.864
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2471 0.798 0.864 0.000 0.000 0.000 0.136
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.1608 0.862 0.000 0.072 0.928 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3728 0.703 0.748 0.000 0.000 0.244 0.008
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.4464 0.398 0.584 0.000 0.000 0.408 0.008
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.4687 0.712 0.168 0.000 0.024 0.756 0.052
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.921 0.000 0.000 1.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0404 0.918 0.000 0.000 0.988 0.000 0.012
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2629 0.854 0.136 0.000 0.000 0.860 0.004
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0992 0.931 0.024 0.000 0.000 0.968 0.008
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0794 0.943 0.000 0.972 0.000 0.000 0.028
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.936 0.000 0.000 0.000 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.4056 0.830 0.044 0.052 0.000 0.824 0.080
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.5391 0.526 0.116 0.000 0.652 0.000 0.232
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0703 0.914 0.000 0.000 0.976 0.000 0.024
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.1357 0.925 0.004 0.000 0.000 0.948 0.048
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0162 0.882 0.996 0.000 0.000 0.000 0.004
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0703 0.933 0.000 0.000 0.000 0.976 0.024
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.921 0.000 0.000 1.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4287 0.177 0.000 0.000 0.540 0.000 0.460
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.1341 0.864 0.944 0.000 0.000 0.000 0.056
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1818 0.914 0.044 0.000 0.000 0.932 0.024
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0963 0.937 0.000 0.964 0.000 0.000 0.036
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.882 1.000 0.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0566 0.935 0.004 0.000 0.000 0.984 0.012
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.1117 0.908 0.000 0.020 0.964 0.000 0.016
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0510 0.918 0.000 0.000 0.984 0.000 0.016
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.921 0.000 0.000 1.000 0.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.957 0.000 1.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0963 0.908 0.000 0.000 0.964 0.000 0.036
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.921 0.000 0.000 1.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0703 0.933 0.000 0.000 0.000 0.976 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.2146 0.8495 0.000 0.000 0.880 0.000 0.004 0.116
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0146 0.9056 0.000 0.000 0.996 0.000 0.000 0.004
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0713 0.7682 0.972 0.000 0.000 0.000 0.000 0.028
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.2431 0.6981 0.132 0.000 0.000 0.000 0.860 0.008
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.5850 -0.3779 0.452 0.000 0.000 0.348 0.000 0.200
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.1141 0.8968 0.000 0.000 0.948 0.000 0.000 0.052
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0146 0.7687 0.996 0.000 0.000 0.000 0.004 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.2595 0.5682 0.000 0.000 0.000 0.836 0.004 0.160
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.6485 0.4376 0.000 0.068 0.000 0.140 0.504 0.288
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0632 0.7688 0.976 0.000 0.000 0.000 0.000 0.024
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0547 0.5916 0.000 0.000 0.000 0.980 0.000 0.020
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3050 0.6169 0.764 0.000 0.000 0.000 0.000 0.236
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.1908 0.8831 0.000 0.916 0.000 0.000 0.028 0.056
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0405 0.7680 0.988 0.000 0.000 0.000 0.008 0.004
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.3242 0.7184 0.000 0.032 0.004 0.000 0.816 0.148
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.3937 0.3652 0.000 0.000 0.000 0.572 0.004 0.424
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1010 0.7586 0.960 0.000 0.000 0.000 0.036 0.004
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0405 0.7680 0.988 0.000 0.000 0.000 0.008 0.004
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.5120 0.4113 0.000 0.000 0.600 0.000 0.280 0.120
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.1858 0.8940 0.000 0.912 0.000 0.000 0.012 0.076
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4563 0.6271 0.000 0.628 0.000 0.000 0.056 0.316
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.5346 0.4712 0.056 0.288 0.000 0.000 0.612 0.044
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0547 0.9014 0.000 0.980 0.000 0.000 0.000 0.020
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2473 0.6971 0.856 0.000 0.000 0.000 0.136 0.008
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.0291 0.7374 0.004 0.004 0.000 0.000 0.992 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1908 0.8721 0.000 0.000 0.916 0.000 0.056 0.028
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.9035 0.000 1.000 0.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1556 0.7596 0.920 0.000 0.000 0.000 0.000 0.080
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.4864 0.3893 0.552 0.000 0.000 0.000 0.064 0.384
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.5409 0.3263 0.000 0.540 0.000 0.000 0.324 0.136
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0777 0.9001 0.000 0.972 0.000 0.000 0.004 0.024
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.3547 0.3293 0.000 0.000 0.000 0.668 0.000 0.332
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.3354 0.6598 0.168 0.000 0.000 0.000 0.796 0.036
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.3934 0.7287 0.000 0.708 0.000 0.000 0.032 0.260
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.1444 0.8925 0.000 0.928 0.000 0.000 0.000 0.072
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2623 0.8618 0.000 0.852 0.000 0.000 0.016 0.132
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.3859 0.3863 0.000 0.000 0.000 0.692 0.020 0.288
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4838 0.4105 0.608 0.000 0.020 0.036 0.000 0.336
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.4801 0.6001 0.000 0.004 0.016 0.028 0.604 0.348
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2911 0.7788 0.000 0.832 0.000 0.000 0.144 0.024
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3076 0.5480 0.000 0.000 0.044 0.840 0.004 0.112
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.1078 0.7387 0.008 0.012 0.000 0.000 0.964 0.016
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.2793 0.5316 0.000 0.000 0.000 0.800 0.000 0.200
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0692 0.9028 0.000 0.976 0.000 0.000 0.004 0.020
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.3847 0.2451 0.008 0.000 0.000 0.644 0.000 0.348
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.1789 0.7354 0.000 0.044 0.000 0.000 0.924 0.032
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.2988 0.6706 0.000 0.000 0.000 0.024 0.824 0.152
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2743 0.6740 0.828 0.000 0.000 0.000 0.164 0.008
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.1610 0.8445 0.000 0.084 0.916 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0858 0.9011 0.000 0.968 0.000 0.000 0.004 0.028
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5951 -0.4641 0.412 0.000 0.000 0.220 0.000 0.368
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0260 0.9029 0.000 0.992 0.000 0.000 0.000 0.008
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 6 0.6041 0.4935 0.272 0.000 0.000 0.312 0.000 0.416
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 6 0.6126 0.2925 0.040 0.000 0.016 0.400 0.068 0.476
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0260 0.9058 0.000 0.000 0.992 0.000 0.000 0.008
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0146 0.9055 0.000 0.000 0.996 0.000 0.000 0.004
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.3468 0.4353 0.068 0.000 0.000 0.804 0.000 0.128
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.3756 0.2555 0.004 0.000 0.000 0.644 0.000 0.352
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.9035 0.000 1.000 0.000 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2692 0.8561 0.000 0.840 0.000 0.000 0.012 0.148
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2730 0.5375 0.000 0.000 0.000 0.808 0.000 0.192
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.3952 0.3428 0.000 0.020 0.000 0.672 0.000 0.308
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.7044 -0.0116 0.152 0.000 0.412 0.000 0.324 0.112
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0632 0.9029 0.000 0.976 0.000 0.000 0.000 0.024
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0692 0.9023 0.000 0.000 0.976 0.004 0.000 0.020
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1644 0.8955 0.000 0.920 0.000 0.000 0.004 0.076
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3868 0.3061 0.000 0.000 0.000 0.508 0.000 0.492
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1327 0.7608 0.936 0.000 0.000 0.000 0.000 0.064
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1501 0.5867 0.000 0.000 0.000 0.924 0.000 0.076
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.9035 0.000 1.000 0.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 5 0.5508 -0.0034 0.000 0.000 0.428 0.000 0.444 0.128
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.3990 0.5799 0.688 0.000 0.000 0.028 0.000 0.284
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1327 0.5915 0.000 0.000 0.000 0.936 0.000 0.064
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9035 0.000 1.000 0.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2631 0.8282 0.000 0.820 0.000 0.000 0.000 0.180
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1387 0.7603 0.932 0.000 0.000 0.000 0.000 0.068
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.3565 0.4920 0.000 0.000 0.000 0.692 0.004 0.304
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.2094 0.8833 0.000 0.028 0.920 0.004 0.016 0.032
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0858 0.9019 0.000 0.000 0.968 0.000 0.004 0.028
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.1265 0.8976 0.000 0.000 0.948 0.000 0.008 0.044
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.9035 0.000 1.000 0.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.2001 0.8813 0.000 0.000 0.912 0.000 0.040 0.048
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.9056 0.000 0.000 1.000 0.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0632 0.5976 0.000 0.000 0.000 0.976 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["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 17471 rows and 87 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.637 0.796 0.904 0.4669 0.536 0.536
#> 3 3 0.513 0.583 0.810 0.4118 0.590 0.362
#> 4 4 0.783 0.831 0.918 0.1386 0.814 0.515
#> 5 5 0.747 0.697 0.868 0.0672 0.920 0.691
#> 6 6 0.841 0.840 0.915 0.0359 0.922 0.645
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.6343 0.781 0.160 0.840
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.1414 0.896 0.020 0.980
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.862 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.9580 0.519 0.620 0.380
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.862 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.9580 0.492 0.380 0.620
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 2 0.9710 0.456 0.400 0.600
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 2 0.9710 0.456 0.400 0.600
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.1414 0.896 0.020 0.980
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.862 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.9209 0.587 0.664 0.336
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.862 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.5629 0.782 0.132 0.868
#> 694B0504-095E-431A-84F2-B4387072138E 2 0.9427 0.525 0.360 0.640
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.3879 0.850 0.076 0.924
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0000 0.862 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 2 0.9635 0.478 0.388 0.612
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.862 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.1414 0.896 0.020 0.980
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.9209 0.587 0.664 0.336
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.1414 0.896 0.020 0.980
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.894 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1414 0.896 0.020 0.980
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.894 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.862 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.1414 0.896 0.020 0.980
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0938 0.895 0.012 0.988
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.894 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 2 0.9710 0.456 0.400 0.600
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.9833 0.404 0.424 0.576
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.894 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.894 0.000 1.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.862 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.1414 0.896 0.020 0.980
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.894 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.894 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.894 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.862 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.9460 0.519 0.364 0.636
#> FF7021BB-414B-4466-BC43-372D856E6A1D 1 0.9580 0.519 0.620 0.380
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.1414 0.896 0.020 0.980
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.9580 0.519 0.620 0.380
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.1414 0.896 0.020 0.980
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.862 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.894 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.862 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.9044 0.586 0.320 0.680
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.1414 0.896 0.020 0.980
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.1414 0.896 0.020 0.980
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.8608 0.475 0.716 0.284
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.894 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.894 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.862 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.894 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.862 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.862 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.1414 0.896 0.020 0.980
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.1414 0.896 0.020 0.980
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.862 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.862 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.894 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.894 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.8608 0.643 0.716 0.284
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.1414 0.896 0.020 0.980
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.8144 0.676 0.252 0.748
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.894 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 1 0.9580 0.519 0.620 0.380
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.894 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0000 0.862 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.862 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.9209 0.587 0.664 0.336
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.894 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.4022 0.856 0.080 0.920
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.1414 0.896 0.020 0.980
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.862 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 2 0.3114 0.873 0.056 0.944
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.894 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.894 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.862 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.862 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.1414 0.896 0.020 0.980
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.1414 0.896 0.020 0.980
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.9129 0.575 0.328 0.672
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.894 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.1414 0.896 0.020 0.980
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.1414 0.896 0.020 0.980
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.9209 0.587 0.664 0.336
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0237 0.7138 0.004 0.000 0.996
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.4931 0.5121 0.232 0.000 0.768
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0424 0.6499 0.992 0.000 0.008
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.6235 -0.1721 0.436 0.000 0.564
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0237 0.6488 0.996 0.000 0.004
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.6286 0.1933 0.536 0.000 0.464
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.5560 0.4732 0.700 0.000 0.300
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.5835 0.3982 0.660 0.000 0.340
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.5560 0.4014 0.300 0.000 0.700
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.2261 0.6329 0.932 0.000 0.068
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.5016 0.5290 0.760 0.000 0.240
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.5560 0.4732 0.700 0.000 0.300
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.5785 0.3995 0.300 0.004 0.696
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.5706 0.4547 0.680 0.000 0.320
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.6527 0.3538 0.320 0.020 0.660
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.6274 -0.0906 0.544 0.000 0.456
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.5706 0.4547 0.680 0.000 0.320
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0424 0.6499 0.992 0.000 0.008
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0592 0.7161 0.000 0.012 0.988
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0592 0.7125 0.012 0.000 0.988
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 3 0.4291 0.5893 0.000 0.180 0.820
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4931 0.6860 0.000 0.768 0.232
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.8982 0.2242 0.308 0.156 0.536
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.4931 0.6860 0.000 0.768 0.232
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.5529 0.4748 0.704 0.000 0.296
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.0424 0.7158 0.000 0.008 0.992
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1411 0.7061 0.000 0.036 0.964
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.9359 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.5706 0.4547 0.680 0.000 0.320
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.6286 0.1933 0.536 0.000 0.464
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.2711 0.8676 0.000 0.912 0.088
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.9359 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0237 0.6488 0.996 0.000 0.004
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.6302 0.1629 0.520 0.000 0.480
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.9359 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.9359 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.9359 0.000 1.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.5098 0.5233 0.752 0.000 0.248
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.6286 0.1933 0.536 0.000 0.464
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.5560 0.4014 0.300 0.000 0.700
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.6126 0.2642 0.000 0.400 0.600
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.0424 0.7132 0.008 0.000 0.992
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.0592 0.7161 0.000 0.012 0.988
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.5016 0.5289 0.760 0.000 0.240
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.9359 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.2796 0.6243 0.908 0.000 0.092
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.5016 0.5026 0.240 0.000 0.760
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.3752 0.8132 0.000 0.856 0.144
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.0237 0.7138 0.004 0.000 0.996
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.5810 0.4401 0.664 0.000 0.336
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.6140 0.2787 0.000 0.596 0.404
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9359 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0237 0.6488 0.996 0.000 0.004
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.9359 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0424 0.6499 0.992 0.000 0.008
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.5178 0.5240 0.744 0.000 0.256
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0592 0.7161 0.000 0.012 0.988
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.4931 0.5121 0.232 0.000 0.768
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.4931 0.5338 0.768 0.000 0.232
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.2165 0.6356 0.936 0.000 0.064
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0237 0.9337 0.000 0.996 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.9359 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.5138 0.5195 0.748 0.000 0.252
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.9016 0.2986 0.192 0.252 0.556
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.5621 0.3929 0.308 0.000 0.692
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.9359 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.5291 0.4480 0.268 0.000 0.732
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.9359 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.6309 0.0111 0.500 0.000 0.500
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0424 0.6499 0.992 0.000 0.008
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.6244 0.1899 0.560 0.000 0.440
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.9359 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.5058 0.4981 0.244 0.000 0.756
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0424 0.7132 0.008 0.000 0.992
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0424 0.6499 0.992 0.000 0.008
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.5216 0.5139 0.740 0.000 0.260
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9359 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0237 0.9337 0.000 0.996 0.004
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0424 0.6499 0.992 0.000 0.008
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.5497 0.3622 0.708 0.000 0.292
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0592 0.7161 0.000 0.012 0.988
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0237 0.7138 0.004 0.000 0.996
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.4842 0.5216 0.224 0.000 0.776
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.9359 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0424 0.7160 0.000 0.008 0.992
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0592 0.7161 0.000 0.012 0.988
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.6286 0.0337 0.464 0.000 0.536
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.3024 0.764 0.852 0.000 0.000 0.148
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.4730 0.503 0.364 0.000 0.000 0.636
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.4730 0.519 0.636 0.000 0.364 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.798 1.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0707 0.798 0.980 0.000 0.000 0.020
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.4730 0.569 0.636 0.000 0.000 0.364
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 4 0.4585 0.518 0.000 0.000 0.332 0.668
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.798 1.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 4 0.5912 0.668 0.148 0.012 0.116 0.724
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.5057 0.496 0.012 0.000 0.340 0.648
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.798 1.000 0.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.798 1.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.4722 0.541 0.008 0.692 0.300 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 1 0.4852 0.691 0.776 0.152 0.072 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0817 0.790 0.976 0.000 0.000 0.024
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.3024 0.786 0.148 0.000 0.852 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2973 0.765 0.856 0.000 0.000 0.144
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.4730 0.519 0.636 0.000 0.364 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.3024 0.834 0.148 0.852 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.0000 0.798 1.000 0.000 0.000 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4730 0.519 0.636 0.000 0.364 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.3219 0.787 0.000 0.836 0.164 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.2589 0.823 0.000 0.000 0.884 0.116
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.3024 0.786 0.148 0.000 0.852 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.3024 0.834 0.148 0.852 0.000 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.798 1.000 0.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.4277 0.598 0.000 0.280 0.720 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.4730 0.569 0.636 0.000 0.000 0.364
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.0336 0.894 0.008 0.000 0.000 0.992
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.7375 0.210 0.000 0.348 0.480 0.172
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.4830 0.463 0.608 0.000 0.392 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.4730 0.569 0.636 0.000 0.000 0.364
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0000 0.900 0.000 0.000 0.000 1.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.3400 0.746 0.820 0.000 0.000 0.180
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1118 0.876 0.036 0.000 0.000 0.964
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.3024 0.764 0.852 0.000 0.000 0.148
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.4767 0.636 0.020 0.000 0.256 0.724
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0188 0.936 0.004 0.000 0.996 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.939 0.000 0.000 1.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.900 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.3242 0.6773 0.000 0.000 0.784 0.000 0.216
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.8688 0.000 0.000 1.000 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.7808 1.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.5644 0.4280 0.144 0.000 0.000 0.228 0.628
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.0162 0.8786 0.004 0.000 0.000 0.996 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.6222 0.3760 0.548 0.000 0.216 0.000 0.236
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0162 0.7795 0.996 0.000 0.000 0.000 0.004
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3395 0.6335 0.000 0.000 0.000 0.764 0.236
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.4235 -0.0478 0.000 0.000 0.424 0.000 0.576
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.7808 1.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0000 0.8783 0.000 0.000 0.000 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3336 0.6883 0.772 0.000 0.000 0.228 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 4 0.6442 0.2503 0.000 0.000 0.196 0.480 0.324
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0880 0.7726 0.968 0.000 0.000 0.000 0.032
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.0880 0.6491 0.000 0.000 0.000 0.032 0.968
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.4591 0.4728 0.012 0.000 0.332 0.648 0.008
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.2280 0.7072 0.880 0.000 0.000 0.000 0.120
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.3837 0.3998 0.692 0.000 0.000 0.000 0.308
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.8688 0.000 0.000 1.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.8688 0.000 0.000 1.000 0.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 5 0.5609 0.1988 0.004 0.344 0.076 0.000 0.576
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.3210 0.7071 0.000 0.788 0.000 0.000 0.212
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.0162 0.6507 0.004 0.000 0.000 0.000 0.996
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2179 0.8258 0.000 0.888 0.000 0.000 0.112
#> 932C8E98-A352-409C-8744-3D49FABCE425 5 0.4906 0.0194 0.480 0.000 0.000 0.024 0.496
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.1043 0.6429 0.000 0.000 0.040 0.000 0.960
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1478 0.8338 0.000 0.000 0.936 0.000 0.064
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8972 0.000 1.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0807 0.7813 0.976 0.000 0.000 0.012 0.012
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.3812 0.6693 0.772 0.000 0.204 0.000 0.024
#> 36EDD202-A845-4CE7-95D5-A515C471262E 5 0.2127 0.6205 0.000 0.108 0.000 0.000 0.892
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.8972 0.000 1.000 0.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0162 0.8786 0.004 0.000 0.000 0.996 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.3508 0.4413 0.252 0.000 0.000 0.000 0.748
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.8972 0.000 1.000 0.000 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.8972 0.000 1.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.4171 0.3399 0.000 0.604 0.000 0.000 0.396
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0451 0.8756 0.004 0.000 0.000 0.988 0.008
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3336 0.6609 0.772 0.000 0.228 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.2377 0.7687 0.000 0.000 0.872 0.000 0.128
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 5 0.4893 0.0666 0.000 0.404 0.028 0.000 0.568
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.2389 0.7771 0.000 0.000 0.880 0.116 0.004
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.0290 0.6514 0.000 0.000 0.008 0.000 0.992
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.8783 0.000 0.000 0.000 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.8972 0.000 1.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0162 0.8786 0.004 0.000 0.000 0.996 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.8688 0.000 0.000 1.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.3395 0.5111 0.000 0.236 0.000 0.000 0.764
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4291 0.2456 0.000 0.000 0.536 0.000 0.464
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 5 0.4235 0.2082 0.424 0.000 0.000 0.000 0.576
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3684 0.5594 0.000 0.280 0.720 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.3395 0.6564 0.000 0.764 0.000 0.000 0.236
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.0162 0.8786 0.004 0.000 0.000 0.996 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.8972 0.000 1.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.3336 0.6883 0.772 0.000 0.000 0.228 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.0912 0.8674 0.016 0.000 0.000 0.972 0.012
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.8688 0.000 0.000 1.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.8688 0.000 0.000 1.000 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0000 0.8783 0.000 0.000 0.000 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0162 0.8786 0.004 0.000 0.000 0.996 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1671 0.8542 0.000 0.924 0.000 0.000 0.076
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0290 0.8938 0.000 0.992 0.000 0.000 0.008
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.8783 0.000 0.000 0.000 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.7342 0.1555 0.000 0.132 0.456 0.072 0.340
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.6345 0.3223 0.524 0.000 0.252 0.000 0.224
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.8972 0.000 1.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.8688 0.000 0.000 1.000 0.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3395 0.6564 0.000 0.764 0.000 0.000 0.236
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0162 0.8786 0.004 0.000 0.000 0.996 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.2966 0.7165 0.816 0.000 0.000 0.184 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0510 0.8709 0.000 0.000 0.000 0.984 0.016
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8972 0.000 1.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.8688 0.000 0.000 1.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0794 0.8589 0.000 0.000 0.972 0.000 0.028
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.1851 0.7651 0.912 0.000 0.000 0.088 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.5535 0.2637 0.072 0.000 0.000 0.536 0.392
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8972 0.000 1.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2179 0.8258 0.000 0.888 0.000 0.000 0.112
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0162 0.7816 0.996 0.000 0.000 0.004 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.5601 0.5837 0.088 0.000 0.216 0.672 0.024
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0162 0.8675 0.000 0.000 0.996 0.000 0.004
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.4150 0.4047 0.000 0.000 0.612 0.000 0.388
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.8688 0.000 0.000 1.000 0.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8972 0.000 1.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1043 0.8515 0.000 0.000 0.960 0.000 0.040
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.8688 0.000 0.000 1.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.8783 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.3175 0.632 0.000 0.000 0.744 0.000 0.000 0.256
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0458 0.791 0.984 0.000 0.000 0.000 0.016 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.0909 0.899 0.012 0.000 0.000 0.000 0.968 0.020
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.0146 0.940 0.004 0.000 0.000 0.996 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 6 0.2365 0.836 0.040 0.000 0.072 0.000 0.000 0.888
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.1007 0.782 0.956 0.000 0.000 0.000 0.044 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 6 0.2854 0.755 0.000 0.000 0.000 0.208 0.000 0.792
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 6 0.0717 0.858 0.000 0.000 0.008 0.000 0.016 0.976
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0458 0.791 0.984 0.000 0.000 0.000 0.016 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0260 0.940 0.000 0.000 0.000 0.992 0.000 0.008
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3101 0.682 0.756 0.000 0.000 0.244 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 6 0.3051 0.802 0.000 0.000 0.036 0.112 0.008 0.844
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.2854 0.704 0.792 0.000 0.000 0.000 0.208 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.1714 0.863 0.000 0.000 0.000 0.000 0.908 0.092
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.4170 0.472 0.020 0.000 0.328 0.648 0.000 0.004
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.3684 0.490 0.628 0.000 0.000 0.000 0.372 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 5 0.3828 0.293 0.440 0.000 0.000 0.000 0.560 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 6 0.0603 0.857 0.000 0.000 0.004 0.000 0.016 0.980
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.3659 0.523 0.000 0.636 0.000 0.000 0.000 0.364
#> F9C23182-91C4-4145-AE52-526FE8EB199D 6 0.1863 0.827 0.000 0.000 0.000 0.000 0.104 0.896
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1387 0.903 0.000 0.932 0.000 0.000 0.000 0.068
#> 932C8E98-A352-409C-8744-3D49FABCE425 5 0.0458 0.890 0.016 0.000 0.000 0.000 0.984 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.1124 0.898 0.000 0.000 0.008 0.000 0.956 0.036
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0146 0.938 0.000 0.000 0.996 0.000 0.000 0.004
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.943 0.000 1.000 0.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.3210 0.727 0.812 0.000 0.000 0.036 0.000 0.152
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.3928 0.693 0.756 0.000 0.196 0.004 0.040 0.004
#> 36EDD202-A845-4CE7-95D5-A515C471262E 6 0.1349 0.849 0.000 0.004 0.000 0.000 0.056 0.940
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.943 0.000 1.000 0.000 0.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.0865 0.901 0.000 0.000 0.000 0.000 0.964 0.036
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0363 0.937 0.000 0.988 0.000 0.000 0.000 0.012
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.943 0.000 1.000 0.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 6 0.1663 0.842 0.000 0.088 0.000 0.000 0.000 0.912
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0363 0.936 0.000 0.000 0.000 0.988 0.012 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3101 0.670 0.756 0.000 0.244 0.000 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.1501 0.883 0.000 0.000 0.924 0.000 0.000 0.076
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 6 0.0870 0.859 0.000 0.004 0.012 0.000 0.012 0.972
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.2146 0.833 0.000 0.000 0.880 0.116 0.000 0.004
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 6 0.1910 0.824 0.000 0.000 0.000 0.000 0.108 0.892
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0260 0.940 0.000 0.000 0.000 0.992 0.000 0.008
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.943 0.000 1.000 0.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.1082 0.899 0.000 0.004 0.000 0.000 0.956 0.040
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 6 0.3431 0.692 0.000 0.000 0.228 0.000 0.016 0.756
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 5 0.0458 0.890 0.016 0.000 0.000 0.000 0.984 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3309 0.605 0.000 0.280 0.720 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 6 0.1957 0.833 0.000 0.112 0.000 0.000 0.000 0.888
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.943 0.000 1.000 0.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.3101 0.682 0.756 0.000 0.000 0.244 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.1036 0.919 0.024 0.000 0.000 0.964 0.008 0.004
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0260 0.940 0.000 0.000 0.000 0.992 0.000 0.008
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1075 0.917 0.000 0.952 0.000 0.000 0.000 0.048
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2730 0.773 0.000 0.808 0.000 0.000 0.000 0.192
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0291 0.940 0.000 0.000 0.000 0.992 0.004 0.004
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 6 0.3883 0.742 0.000 0.000 0.144 0.088 0.000 0.768
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.4536 0.484 0.608 0.000 0.356 0.000 0.012 0.024
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.943 0.000 1.000 0.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 6 0.1957 0.833 0.000 0.112 0.000 0.000 0.000 0.888
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0000 0.941 0.000 0.000 0.000 1.000 0.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0937 0.791 0.960 0.000 0.000 0.040 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0622 0.935 0.000 0.000 0.000 0.980 0.008 0.012
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.943 0.000 1.000 0.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0717 0.925 0.000 0.000 0.976 0.000 0.016 0.008
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.1075 0.790 0.952 0.000 0.000 0.048 0.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 6 0.3731 0.728 0.008 0.000 0.000 0.212 0.024 0.756
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.943 0.000 1.000 0.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.1387 0.903 0.000 0.932 0.000 0.000 0.000 0.068
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.791 1.000 0.000 0.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.5080 0.593 0.104 0.000 0.200 0.676 0.016 0.004
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0146 0.937 0.000 0.000 0.996 0.000 0.000 0.004
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.2980 0.774 0.000 0.000 0.808 0.000 0.012 0.180
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.943 0.000 1.000 0.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.940 0.000 0.000 1.000 0.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0260 0.940 0.000 0.000 0.000 0.992 0.000 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17471 rows and 87 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.212 0.811 0.863 0.4590 0.505 0.505
#> 3 3 0.468 0.607 0.784 0.3175 0.760 0.574
#> 4 4 0.694 0.803 0.841 0.1550 0.752 0.450
#> 5 5 0.672 0.650 0.809 0.0956 0.962 0.863
#> 6 6 0.691 0.578 0.785 0.0459 0.907 0.651
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.653 0.78472 0.168 0.832
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.358 0.84254 0.068 0.932
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.373 0.89368 0.928 0.072
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.844 0.75539 0.728 0.272
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.343 0.89376 0.936 0.064
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.866 0.71411 0.288 0.712
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.373 0.89368 0.928 0.072
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.402 0.89076 0.920 0.080
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.760 0.83494 0.220 0.780
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.373 0.89368 0.928 0.072
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.327 0.88399 0.940 0.060
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.402 0.89451 0.920 0.080
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.552 0.86304 0.128 0.872
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.373 0.89368 0.928 0.072
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.327 0.85340 0.060 0.940
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.430 0.87388 0.912 0.088
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.373 0.89368 0.928 0.072
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.373 0.89368 0.928 0.072
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.443 0.83298 0.092 0.908
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.932 0.56740 0.348 0.652
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.541 0.86339 0.124 0.876
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.855 0.71338 0.280 0.720
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.563 0.86249 0.132 0.868
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.662 0.85085 0.172 0.828
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.373 0.89368 0.928 0.072
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.343 0.84754 0.064 0.936
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.260 0.84655 0.044 0.956
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.443 0.85557 0.092 0.908
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.430 0.88668 0.912 0.088
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.775 0.81903 0.772 0.228
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.358 0.85766 0.068 0.932
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.541 0.86325 0.124 0.876
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.482 0.89096 0.896 0.104
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.992 0.14073 0.448 0.552
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.795 0.76876 0.240 0.760
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.469 0.85946 0.100 0.900
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.541 0.86339 0.124 0.876
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.311 0.88443 0.944 0.056
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.563 0.88195 0.868 0.132
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.850 0.62766 0.276 0.724
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.563 0.86242 0.132 0.868
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.767 0.68157 0.776 0.224
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.260 0.84655 0.044 0.956
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.343 0.88577 0.936 0.064
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.456 0.85786 0.096 0.904
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.327 0.88886 0.940 0.060
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.373 0.85958 0.072 0.928
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.260 0.84655 0.044 0.956
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.946 0.54872 0.364 0.636
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.402 0.89063 0.920 0.080
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.574 0.86509 0.136 0.864
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.518 0.86349 0.116 0.884
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.358 0.89294 0.932 0.068
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.541 0.86339 0.124 0.876
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.529 0.88493 0.880 0.120
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.469 0.86533 0.900 0.100
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.529 0.82325 0.120 0.880
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.584 0.85746 0.140 0.860
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.295 0.88621 0.948 0.052
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.327 0.88643 0.940 0.060
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.518 0.86349 0.116 0.884
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.482 0.86082 0.104 0.896
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.552 0.83217 0.872 0.128
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 1 0.983 -0.00704 0.576 0.424
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.958 0.37054 0.380 0.620
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.518 0.86349 0.116 0.884
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.971 0.58047 0.400 0.600
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.518 0.86349 0.116 0.884
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.738 0.72637 0.792 0.208
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.373 0.89368 0.928 0.072
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.373 0.88074 0.928 0.072
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.443 0.85557 0.092 0.908
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.541 0.82883 0.124 0.876
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.653 0.80406 0.168 0.832
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.402 0.89336 0.920 0.080
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.518 0.86070 0.884 0.116
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.482 0.86091 0.104 0.896
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.541 0.86339 0.124 0.876
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.373 0.89368 0.928 0.072
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.327 0.88637 0.940 0.060
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.494 0.86001 0.108 0.892
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.895 0.63785 0.312 0.688
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.913 0.61072 0.328 0.672
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.456 0.85791 0.096 0.904
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.278 0.84705 0.048 0.952
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.541 0.82027 0.124 0.876
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.775 0.66904 0.772 0.228
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.6305 0.5586 0.000 0.516 0.484
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.6302 0.5648 0.000 0.520 0.480
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.9683 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.9211 0.2594 0.224 0.240 0.536
#> 2F38E3B1-4975-4877-9DCC-C00270602180 3 0.6280 0.1023 0.460 0.000 0.540
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.8659 0.3875 0.104 0.488 0.408
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.9683 1.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.4750 0.6761 0.216 0.000 0.784
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.6521 -0.5393 0.004 0.492 0.504
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.9683 1.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.4796 0.6752 0.220 0.000 0.780
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1289 0.9377 0.968 0.000 0.032
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4748 0.6684 0.024 0.832 0.144
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.9683 1.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.6295 0.5746 0.000 0.528 0.472
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.4750 0.6761 0.216 0.000 0.784
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.9683 1.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.9683 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.6204 0.6279 0.000 0.576 0.424
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.5724 0.000 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.2878 0.7109 0.000 0.904 0.096
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4121 0.7065 0.000 0.832 0.168
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.6630 0.6796 0.028 0.672 0.300
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0424 0.7141 0.000 0.992 0.008
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.3116 0.8655 0.892 0.000 0.108
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.6302 0.5633 0.000 0.520 0.480
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.5968 0.6689 0.000 0.636 0.364
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.7157 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.9683 1.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.9598 0.2683 0.304 0.228 0.468
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.5810 0.6795 0.000 0.664 0.336
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.7157 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 3 0.4842 0.6711 0.224 0.000 0.776
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.6793 0.5893 0.012 0.536 0.452
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0237 0.7161 0.000 0.996 0.004
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.7157 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0237 0.7161 0.000 0.996 0.004
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.4796 0.6752 0.220 0.000 0.780
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.4796 0.6752 0.220 0.000 0.780
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.6309 0.5381 0.000 0.504 0.496
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.5621 0.6862 0.000 0.692 0.308
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.4002 0.6530 0.160 0.000 0.840
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.5988 0.6670 0.000 0.632 0.368
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.4796 0.6752 0.220 0.000 0.780
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.7157 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 3 0.5178 0.6218 0.256 0.000 0.744
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.6008 0.6655 0.000 0.628 0.372
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5926 0.6723 0.000 0.644 0.356
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4504 0.2625 0.000 0.196 0.804
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.9683 1.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.1031 0.7167 0.000 0.976 0.024
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.7157 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3752 0.8191 0.856 0.000 0.144
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.7157 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 3 0.4842 0.6711 0.224 0.000 0.776
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.4750 0.6756 0.216 0.000 0.784
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.6302 0.5647 0.000 0.520 0.480
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.6309 0.5308 0.000 0.500 0.500
#> C41F3064-4483-4796-B860-82155BAA5157 3 0.5706 0.4950 0.320 0.000 0.680
#> F4232B90-51B9-43EE-9971-35B3A318758F 3 0.4842 0.6711 0.224 0.000 0.776
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.7157 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.7157 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.4796 0.6752 0.220 0.000 0.780
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.9601 0.0964 0.216 0.456 0.328
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.6309 0.5308 0.000 0.500 0.500
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.7157 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.6252 -0.4532 0.000 0.444 0.556
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.7157 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.4796 0.6752 0.220 0.000 0.780
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.9683 1.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.4750 0.6761 0.216 0.000 0.784
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.7157 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.6309 -0.5492 0.000 0.496 0.504
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.6307 -0.5344 0.000 0.488 0.512
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.1529 0.9292 0.960 0.000 0.040
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.4750 0.6761 0.216 0.000 0.784
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.7157 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.7157 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.9683 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.4796 0.6752 0.220 0.000 0.780
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.6215 0.6244 0.000 0.572 0.428
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.5760 -0.1464 0.000 0.328 0.672
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.5291 0.0573 0.000 0.268 0.732
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.7157 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.6140 0.6433 0.000 0.596 0.404
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.6274 0.5951 0.000 0.544 0.456
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.4750 0.6761 0.216 0.000 0.784
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1302 0.832 0.000 0.044 0.956 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0336 0.836 0.000 0.008 0.992 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.4473 0.718 0.008 0.036 0.804 0.152
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.3726 0.717 0.212 0.000 0.000 0.788
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0469 0.840 0.000 0.000 0.988 0.012
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0188 0.919 0.000 0.000 0.004 0.996
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.0921 0.839 0.000 0.000 0.972 0.028
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0188 0.919 0.000 0.000 0.004 0.996
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3636 0.774 0.820 0.008 0.000 0.172
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.3074 0.624 0.000 0.152 0.848 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.1661 0.829 0.000 0.052 0.944 0.004
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.0469 0.917 0.000 0.000 0.012 0.988
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0469 0.833 0.000 0.012 0.988 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.4855 0.319 0.000 0.000 0.600 0.400
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 3 0.4989 -0.545 0.000 0.472 0.528 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4955 -0.212 0.000 0.556 0.444 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.1118 0.813 0.000 0.036 0.964 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.4817 0.834 0.000 0.612 0.388 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0336 0.950 0.992 0.000 0.000 0.008
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.1474 0.829 0.000 0.052 0.948 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0817 0.838 0.000 0.024 0.976 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0336 0.952 0.992 0.008 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.6732 0.586 0.096 0.180 0.680 0.044
#> 36EDD202-A845-4CE7-95D5-A515C471262E 3 0.2469 0.714 0.000 0.108 0.892 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.4543 0.910 0.000 0.676 0.324 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.2908 0.796 0.040 0.064 0.896 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.2704 0.507 0.000 0.876 0.124 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.4624 0.896 0.000 0.660 0.340 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0469 0.917 0.000 0.000 0.012 0.988
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.1109 0.907 0.004 0.000 0.028 0.968
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.4769 0.578 0.000 0.308 0.684 0.008
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.0817 0.825 0.000 0.024 0.976 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3873 0.694 0.000 0.000 0.228 0.772
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.0469 0.833 0.000 0.012 0.988 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0188 0.919 0.000 0.000 0.004 0.996
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0469 0.833 0.000 0.012 0.988 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.1637 0.828 0.000 0.060 0.940 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.3400 0.722 0.000 0.000 0.820 0.180
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0188 0.954 0.996 0.004 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.4730 0.868 0.000 0.636 0.364 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.5250 0.159 0.440 0.008 0.000 0.552
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.0188 0.915 0.000 0.004 0.000 0.996
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.1211 0.899 0.000 0.000 0.040 0.960
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0524 0.838 0.000 0.008 0.988 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0592 0.840 0.000 0.000 0.984 0.016
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0469 0.914 0.012 0.000 0.000 0.988
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.917 0.000 0.000 0.000 1.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0188 0.919 0.000 0.000 0.004 0.996
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.6293 0.488 0.000 0.096 0.628 0.276
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0592 0.840 0.000 0.000 0.984 0.016
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1792 0.820 0.000 0.000 0.932 0.068
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.4543 0.910 0.000 0.676 0.324 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0188 0.919 0.000 0.000 0.004 0.996
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0188 0.954 0.996 0.004 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1302 0.896 0.000 0.000 0.044 0.956
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0895 0.841 0.000 0.004 0.976 0.020
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.1474 0.830 0.000 0.000 0.948 0.052
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.6853 0.618 0.584 0.312 0.092 0.012
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.4477 0.508 0.000 0.000 0.312 0.688
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.4843 0.820 0.000 0.604 0.396 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0336 0.952 0.992 0.008 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0188 0.919 0.000 0.000 0.004 0.996
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0336 0.836 0.000 0.008 0.992 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.3400 0.722 0.000 0.000 0.820 0.180
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.3024 0.754 0.000 0.000 0.852 0.148
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.4522 0.913 0.000 0.680 0.320 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0469 0.838 0.000 0.012 0.988 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0336 0.836 0.000 0.008 0.992 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.1118 0.903 0.000 0.000 0.036 0.964
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.4243 0.7609 0.264 0.000 0.712 0.000 0.024
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.3013 0.7859 0.160 0.008 0.832 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.4182 0.6141 0.600 0.000 0.000 0.000 0.400
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.5114 0.6826 0.404 0.000 0.560 0.004 0.032
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.4024 0.6607 0.028 0.000 0.000 0.752 0.220
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0740 0.7776 0.008 0.008 0.980 0.004 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.4114 0.6458 0.624 0.000 0.000 0.000 0.376
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.1478 0.8384 0.000 0.000 0.064 0.936 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.0992 0.7648 0.000 0.000 0.968 0.008 0.024
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.4114 0.6458 0.624 0.000 0.000 0.000 0.376
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0451 0.8403 0.000 0.000 0.008 0.988 0.004
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.6275 0.2010 0.520 0.000 0.000 0.180 0.300
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.3554 0.6501 0.004 0.216 0.776 0.000 0.004
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.4114 0.6458 0.624 0.000 0.000 0.000 0.376
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.3983 0.7363 0.340 0.000 0.660 0.000 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.2179 0.8267 0.000 0.000 0.100 0.896 0.004
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.4114 0.6458 0.624 0.000 0.000 0.000 0.376
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.4114 0.6458 0.624 0.000 0.000 0.000 0.376
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.2193 0.7877 0.092 0.008 0.900 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3574 0.5957 0.000 0.000 0.804 0.168 0.028
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.4489 0.2392 0.008 0.572 0.420 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.8240 -0.1108 0.204 0.232 0.164 0.000 0.400
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.5758 0.6948 0.284 0.124 0.592 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.4287 0.1043 0.000 0.540 0.460 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.4114 0.6458 0.624 0.000 0.000 0.000 0.376
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.4414 0.7158 0.376 0.004 0.616 0.000 0.004
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.6426 0.6252 0.376 0.020 0.496 0.000 0.108
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8607 0.000 1.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 5 0.4306 -0.5370 0.492 0.000 0.000 0.000 0.508
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.5958 0.6841 0.236 0.000 0.612 0.008 0.144
#> 36EDD202-A845-4CE7-95D5-A515C471262E 3 0.7236 0.5281 0.352 0.176 0.432 0.000 0.040
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2074 0.8094 0.000 0.896 0.104 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0794 0.8342 0.000 0.000 0.000 0.972 0.028
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.6124 -0.6065 0.460 0.000 0.412 0.000 0.128
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.5850 0.3977 0.040 0.532 0.032 0.000 0.396
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.8607 0.000 1.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2362 0.8074 0.008 0.900 0.008 0.000 0.084
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1121 0.8419 0.000 0.000 0.044 0.956 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.4956 0.4548 0.004 0.000 0.312 0.644 0.040
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.6411 0.2889 0.172 0.000 0.436 0.000 0.392
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.4630 0.7631 0.176 0.088 0.736 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.4703 0.5744 0.000 0.000 0.340 0.632 0.028
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.4895 0.7073 0.376 0.024 0.596 0.000 0.004
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0162 0.8376 0.000 0.000 0.000 0.996 0.004
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.8607 0.000 1.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0290 0.8371 0.000 0.000 0.000 0.992 0.008
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.3513 0.7827 0.180 0.020 0.800 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.6846 0.5962 0.376 0.040 0.468 0.000 0.116
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.0898 0.7794 0.020 0.000 0.972 0.008 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.4126 0.6411 0.620 0.000 0.000 0.000 0.380
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.2488 0.7967 0.000 0.872 0.124 0.000 0.004
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.1341 0.8415 0.000 0.944 0.056 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.5612 0.4831 0.128 0.000 0.000 0.624 0.248
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.8607 0.000 1.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.1121 0.8294 0.000 0.000 0.000 0.956 0.044
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.2193 0.8301 0.000 0.000 0.092 0.900 0.008
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0865 0.7687 0.000 0.004 0.972 0.000 0.024
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0324 0.7749 0.004 0.000 0.992 0.000 0.004
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0865 0.8354 0.004 0.000 0.000 0.972 0.024
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0290 0.8371 0.000 0.000 0.000 0.992 0.008
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.8607 0.000 1.000 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.8607 0.000 1.000 0.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.1270 0.8420 0.000 0.000 0.052 0.948 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.4010 0.6199 0.000 0.000 0.760 0.208 0.032
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.3783 0.7682 0.252 0.008 0.740 0.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.8607 0.000 1.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0955 0.7644 0.000 0.000 0.968 0.004 0.028
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.2516 0.7743 0.000 0.860 0.140 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3861 0.5635 0.000 0.000 0.284 0.712 0.004
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.4192 0.6112 0.596 0.000 0.000 0.000 0.404
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.2233 0.8217 0.000 0.000 0.104 0.892 0.004
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8607 0.000 1.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.2852 0.7846 0.172 0.000 0.828 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0794 0.7665 0.000 0.000 0.972 0.000 0.028
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.3563 -0.0538 0.208 0.000 0.000 0.012 0.780
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.4437 0.2201 0.000 0.000 0.464 0.532 0.004
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8607 0.000 1.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.1638 0.8370 0.004 0.932 0.064 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 5 0.4304 -0.5270 0.484 0.000 0.000 0.000 0.516
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0162 0.8379 0.000 0.000 0.000 0.996 0.004
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.1628 0.7852 0.056 0.008 0.936 0.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0865 0.7660 0.000 0.000 0.972 0.004 0.024
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0798 0.7691 0.000 0.000 0.976 0.008 0.016
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8607 0.000 1.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.5010 0.7033 0.376 0.008 0.592 0.000 0.024
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0865 0.7695 0.004 0.000 0.972 0.000 0.024
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.2179 0.8240 0.000 0.000 0.100 0.896 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 5 0.4299 0.4025 0.000 0.000 0.308 0.000 0.652 0.040
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.4405 0.2612 0.000 0.000 0.504 0.000 0.472 0.024
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.2003 0.8201 0.884 0.000 0.000 0.000 0.000 0.116
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.3420 0.5910 0.040 0.000 0.060 0.000 0.840 0.060
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.4358 0.0426 0.092 0.000 0.000 0.712 0.000 0.196
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.4504 0.5309 0.000 0.000 0.576 0.004 0.392 0.028
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.8552 1.000 0.000 0.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0790 0.7590 0.000 0.000 0.032 0.968 0.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.2964 0.6880 0.000 0.000 0.792 0.000 0.204 0.004
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0146 0.8549 0.996 0.000 0.000 0.000 0.000 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0291 0.7598 0.000 0.000 0.004 0.992 0.000 0.004
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.4191 0.5754 0.732 0.000 0.000 0.180 0.000 0.088
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.6656 0.3304 0.000 0.176 0.476 0.000 0.284 0.064
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.8552 1.000 0.000 0.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.2871 0.5755 0.000 0.000 0.192 0.000 0.804 0.004
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.2730 0.5682 0.000 0.000 0.192 0.808 0.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.8552 1.000 0.000 0.000 0.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.8552 1.000 0.000 0.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.3482 0.6155 0.000 0.000 0.684 0.000 0.316 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3817 0.4010 0.000 0.000 0.720 0.252 0.028 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.6098 0.3024 0.000 0.532 0.108 0.000 0.308 0.052
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.6349 0.1200 0.000 0.212 0.024 0.000 0.460 0.304
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.4680 0.5196 0.000 0.092 0.112 0.000 0.744 0.052
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.7229 -0.1062 0.000 0.368 0.324 0.000 0.200 0.108
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0937 0.8425 0.960 0.000 0.000 0.000 0.000 0.040
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.2783 0.6068 0.000 0.000 0.148 0.000 0.836 0.016
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.2308 0.6084 0.000 0.000 0.068 0.000 0.892 0.040
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0458 0.8046 0.000 0.984 0.000 0.000 0.000 0.016
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.3360 0.7114 0.732 0.000 0.000 0.004 0.000 0.264
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.6549 0.2772 0.060 0.000 0.380 0.012 0.452 0.096
#> 36EDD202-A845-4CE7-95D5-A515C471262E 5 0.4218 0.5573 0.000 0.128 0.068 0.000 0.772 0.032
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3576 0.6771 0.000 0.764 0.012 0.000 0.212 0.012
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.1444 0.6977 0.000 0.000 0.000 0.928 0.000 0.072
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.3623 0.5154 0.100 0.000 0.008 0.000 0.808 0.084
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.6312 0.3555 0.000 0.432 0.016 0.000 0.228 0.324
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0508 0.8066 0.000 0.984 0.000 0.000 0.004 0.012
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3835 0.6766 0.000 0.756 0.000 0.000 0.188 0.056
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0632 0.7616 0.000 0.000 0.024 0.976 0.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.5841 -0.0164 0.000 0.000 0.060 0.588 0.264 0.088
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.5418 -0.1139 0.000 0.000 0.520 0.000 0.352 0.128
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 5 0.6033 0.1857 0.000 0.088 0.316 0.000 0.536 0.060
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3607 0.1783 0.000 0.000 0.348 0.652 0.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.2218 0.6045 0.000 0.000 0.104 0.000 0.884 0.012
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0260 0.7598 0.000 0.000 0.008 0.992 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0146 0.8055 0.000 0.996 0.000 0.000 0.000 0.004
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0790 0.7416 0.000 0.000 0.000 0.968 0.000 0.032
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 5 0.4917 -0.1241 0.000 0.020 0.432 0.000 0.520 0.028
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.2426 0.6018 0.000 0.012 0.044 0.000 0.896 0.048
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4209 0.5434 0.000 0.000 0.596 0.000 0.384 0.020
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1391 0.8358 0.944 0.000 0.000 0.000 0.016 0.040
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.4266 0.7414 0.000 0.776 0.040 0.000 0.088 0.096
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.3593 0.7095 0.000 0.788 0.004 0.000 0.164 0.044
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 6 0.5328 0.0000 0.104 0.000 0.000 0.440 0.000 0.456
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1429 0.8000 0.000 0.940 0.004 0.000 0.004 0.052
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.2092 0.6207 0.000 0.000 0.000 0.876 0.000 0.124
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.1219 0.7507 0.000 0.000 0.048 0.948 0.000 0.004
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.2823 0.6879 0.000 0.000 0.796 0.000 0.204 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.3952 0.6258 0.000 0.000 0.672 0.000 0.308 0.020
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0806 0.7474 0.008 0.000 0.000 0.972 0.000 0.020
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0291 0.7582 0.000 0.000 0.004 0.992 0.000 0.004
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1364 0.7992 0.000 0.944 0.004 0.000 0.004 0.048
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0914 0.8056 0.000 0.968 0.000 0.000 0.016 0.016
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0363 0.7613 0.000 0.000 0.012 0.988 0.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.3878 0.3500 0.000 0.000 0.748 0.212 0.032 0.008
#> 1CC36859-357A-49E0-A367-4F57D47288BA 5 0.4094 0.2400 0.000 0.000 0.324 0.000 0.652 0.024
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0692 0.8036 0.000 0.976 0.000 0.000 0.004 0.020
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1610 0.5844 0.000 0.000 0.916 0.000 0.084 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.4354 0.6583 0.000 0.740 0.028 0.000 0.184 0.048
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3187 0.4949 0.000 0.000 0.188 0.796 0.012 0.004
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.2494 0.8164 0.864 0.000 0.000 0.000 0.016 0.120
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1444 0.7227 0.000 0.000 0.072 0.928 0.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0458 0.8046 0.000 0.984 0.000 0.000 0.000 0.016
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 5 0.4335 -0.2115 0.000 0.000 0.472 0.000 0.508 0.020
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.2762 0.6854 0.000 0.000 0.804 0.000 0.196 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.5828 0.3834 0.516 0.000 0.032 0.000 0.096 0.356
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.3485 0.4317 0.000 0.000 0.204 0.772 0.020 0.004
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0547 0.8043 0.000 0.980 0.000 0.000 0.000 0.020
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.3324 0.7786 0.000 0.832 0.008 0.000 0.076 0.084
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.3547 0.6811 0.696 0.000 0.000 0.004 0.000 0.300
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0146 0.7545 0.000 0.000 0.000 0.996 0.000 0.004
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.4333 0.5350 0.000 0.000 0.596 0.000 0.376 0.028
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.2854 0.6879 0.000 0.000 0.792 0.000 0.208 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.3368 0.6797 0.000 0.000 0.756 0.000 0.232 0.012
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0458 0.8046 0.000 0.984 0.000 0.000 0.000 0.016
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 5 0.2909 0.6110 0.000 0.000 0.136 0.000 0.836 0.028
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.2793 0.6870 0.000 0.000 0.800 0.000 0.200 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.1267 0.7371 0.000 0.000 0.060 0.940 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", "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 17471 rows and 87 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 0.976 0.963 0.984 0.4968 0.502 0.502
#> 3 3 0.747 0.866 0.937 0.3198 0.720 0.502
#> 4 4 0.731 0.821 0.884 0.1363 0.765 0.432
#> 5 5 0.749 0.683 0.820 0.0612 0.918 0.701
#> 6 6 0.793 0.676 0.834 0.0353 0.941 0.741
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0000 0.987 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.987 0.000 1.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.978 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.978 1.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.978 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.5737 0.842 0.136 0.864
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.978 1.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0000 0.978 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.987 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.978 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.978 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.978 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.987 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.978 1.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.987 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0000 0.978 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.978 1.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.978 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.987 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.7883 0.695 0.236 0.764
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.987 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.987 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0000 0.987 0.000 1.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.987 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.978 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0376 0.984 0.004 0.996
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0000 0.987 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.987 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.978 1.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.0000 0.978 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.987 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.987 0.000 1.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.978 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.8955 0.555 0.688 0.312
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.987 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.987 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.987 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.978 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.978 1.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0000 0.987 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.987 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.5629 0.839 0.868 0.132
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.987 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.978 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.987 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.978 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0000 0.987 0.000 1.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.987 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.6531 0.799 0.168 0.832
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.978 1.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.987 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.987 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.978 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.987 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.978 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.978 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.987 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0000 0.987 0.000 1.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.978 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.978 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.987 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.987 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0000 0.978 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.1843 0.963 0.028 0.972
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.9427 0.441 0.640 0.360
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.987 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0000 0.987 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.987 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0376 0.974 0.996 0.004
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.978 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.978 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.987 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0376 0.984 0.004 0.996
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.987 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.978 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0000 0.978 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.987 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.987 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.978 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.978 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.987 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0376 0.984 0.004 0.996
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0672 0.981 0.008 0.992
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.987 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.987 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.987 0.000 1.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0000 0.978 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.4346 0.780 0.000 0.816 0.184
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.6140 0.354 0.000 0.596 0.404
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0237 0.922 0.996 0.000 0.004
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0237 0.922 0.996 0.000 0.004
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.5760 0.544 0.672 0.000 0.328
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.4784 0.762 0.004 0.796 0.200
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.922 1.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.0000 0.921 0.000 0.000 1.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.1964 0.898 0.000 0.056 0.944
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0237 0.922 0.996 0.000 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.0000 0.921 0.000 0.000 1.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0237 0.922 0.996 0.000 0.004
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.934 0.000 1.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.922 1.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.1860 0.906 0.000 0.948 0.052
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.0000 0.921 0.000 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.922 1.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.922 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.5760 0.543 0.000 0.672 0.328
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.921 0.000 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.934 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0237 0.932 0.004 0.996 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5431 0.573 0.284 0.716 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1411 0.918 0.000 0.964 0.036
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0747 0.917 0.984 0.000 0.016
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.5291 0.659 0.000 0.732 0.268
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.1411 0.917 0.000 0.964 0.036
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.934 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.922 1.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.2878 0.852 0.904 0.000 0.096
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.934 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.934 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 3 0.3038 0.835 0.104 0.000 0.896
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.4399 0.746 0.812 0.188 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.934 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.934 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.1163 0.915 0.028 0.972 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.0000 0.921 0.000 0.000 1.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.1964 0.896 0.944 0.000 0.056
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.3340 0.850 0.000 0.120 0.880
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.934 0.000 1.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.0000 0.921 0.000 0.000 1.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.934 0.000 1.000 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.0000 0.921 0.000 0.000 1.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.934 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 3 0.5291 0.594 0.268 0.000 0.732
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.1964 0.905 0.000 0.944 0.056
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0237 0.932 0.004 0.996 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.3482 0.841 0.000 0.128 0.872
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.922 1.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0237 0.933 0.000 0.996 0.004
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.934 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3116 0.852 0.892 0.000 0.108
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.934 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.6180 0.330 0.584 0.000 0.416
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.0000 0.921 0.000 0.000 1.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.4750 0.733 0.000 0.216 0.784
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.3482 0.845 0.000 0.128 0.872
#> C41F3064-4483-4796-B860-82155BAA5157 3 0.4062 0.762 0.164 0.000 0.836
#> F4232B90-51B9-43EE-9971-35B3A318758F 3 0.2537 0.859 0.080 0.000 0.920
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.934 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.934 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.0237 0.919 0.004 0.000 0.996
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.1163 0.913 0.000 0.028 0.972
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.7079 0.672 0.720 0.104 0.176
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.934 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1163 0.913 0.000 0.028 0.972
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.934 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.0000 0.921 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0237 0.922 0.996 0.000 0.004
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.0000 0.921 0.000 0.000 1.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.934 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.5529 0.587 0.000 0.296 0.704
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.2066 0.896 0.000 0.060 0.940
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.922 1.000 0.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.0000 0.921 0.000 0.000 1.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.934 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.934 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0237 0.922 0.996 0.000 0.004
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.0000 0.921 0.000 0.000 1.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.3412 0.849 0.000 0.876 0.124
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0424 0.920 0.000 0.008 0.992
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0592 0.919 0.000 0.012 0.988
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.934 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.2537 0.887 0.000 0.920 0.080
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.4235 0.794 0.000 0.176 0.824
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.0000 0.921 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.2124 0.8190 0.008 0.068 0.924 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.3991 0.8190 0.000 0.172 0.808 0.020
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0336 0.9320 0.992 0.000 0.000 0.008
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.2704 0.8823 0.876 0.000 0.124 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.5108 0.4884 0.308 0.000 0.020 0.672
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.5610 0.7766 0.008 0.216 0.716 0.060
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0188 0.9326 0.996 0.000 0.004 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0376 0.8831 0.000 0.004 0.004 0.992
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.4839 0.6300 0.000 0.044 0.200 0.756
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0779 0.9335 0.980 0.000 0.016 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0657 0.8832 0.000 0.004 0.012 0.984
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1109 0.9282 0.968 0.000 0.004 0.028
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0188 0.9471 0.000 0.996 0.004 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.9324 1.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.2635 0.8066 0.000 0.076 0.904 0.020
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.4989 0.0985 0.000 0.000 0.528 0.472
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0376 0.9326 0.992 0.000 0.004 0.004
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1022 0.9308 0.968 0.000 0.032 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.3991 0.8185 0.000 0.172 0.808 0.020
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3569 0.7818 0.000 0.000 0.804 0.196
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.9462 0.000 1.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.3311 0.8268 0.000 0.828 0.172 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5007 0.4346 0.356 0.636 0.008 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0376 0.9438 0.000 0.992 0.004 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1902 0.9213 0.932 0.000 0.064 0.004
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.1706 0.8085 0.016 0.000 0.948 0.036
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.2342 0.8204 0.008 0.080 0.912 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0188 0.9471 0.000 0.996 0.004 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2011 0.8952 0.920 0.000 0.000 0.080
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.3831 0.6047 0.204 0.000 0.792 0.004
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.1557 0.9014 0.000 0.944 0.056 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.1398 0.9224 0.040 0.956 0.004 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0188 0.8802 0.004 0.000 0.000 0.996
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.3448 0.8399 0.828 0.004 0.168 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.3074 0.8454 0.000 0.848 0.152 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0188 0.9453 0.000 0.996 0.004 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2480 0.8967 0.008 0.904 0.088 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1209 0.8791 0.000 0.004 0.032 0.964
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4267 0.7645 0.788 0.000 0.024 0.188
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0524 0.8056 0.000 0.008 0.988 0.004
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0188 0.9471 0.000 0.996 0.004 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.4456 0.5128 0.000 0.004 0.280 0.716
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.5668 0.6874 0.048 0.300 0.652 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0469 0.8836 0.000 0.000 0.012 0.988
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0188 0.9471 0.000 0.996 0.004 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0779 0.8735 0.016 0.000 0.004 0.980
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.3837 0.7839 0.000 0.224 0.776 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.4644 0.7227 0.024 0.228 0.748 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4578 0.8129 0.000 0.052 0.788 0.160
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1867 0.9192 0.928 0.000 0.072 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.4972 0.3991 0.000 0.456 0.544 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9462 0.000 1.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.4996 0.0228 0.484 0.000 0.000 0.516
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0188 0.9471 0.000 0.996 0.004 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.4761 0.4593 0.332 0.000 0.004 0.664
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.3688 0.7731 0.000 0.000 0.792 0.208
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.4635 0.8256 0.000 0.080 0.796 0.124
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.4719 0.8012 0.000 0.048 0.772 0.180
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0592 0.8837 0.000 0.000 0.016 0.984
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.8815 0.000 0.000 0.000 1.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0336 0.9452 0.000 0.992 0.008 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2334 0.8983 0.004 0.908 0.088 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0657 0.8832 0.000 0.004 0.012 0.984
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.2480 0.8387 0.000 0.008 0.088 0.904
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.4982 0.7347 0.188 0.048 0.760 0.004
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0188 0.9471 0.000 0.996 0.004 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.3668 0.7911 0.000 0.004 0.808 0.188
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0188 0.9453 0.000 0.996 0.004 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0592 0.8829 0.000 0.000 0.016 0.984
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1610 0.9324 0.952 0.000 0.032 0.016
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1824 0.8523 0.000 0.004 0.060 0.936
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0188 0.9471 0.000 0.996 0.004 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.3216 0.8302 0.000 0.044 0.880 0.076
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.3962 0.8115 0.000 0.028 0.820 0.152
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.5100 0.7849 0.756 0.000 0.168 0.076
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1305 0.8713 0.000 0.004 0.036 0.960
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0188 0.9471 0.000 0.996 0.004 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2011 0.9036 0.000 0.920 0.080 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1118 0.9242 0.964 0.000 0.000 0.036
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0469 0.8836 0.000 0.000 0.012 0.988
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.4262 0.7765 0.000 0.236 0.756 0.008
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.3969 0.7963 0.000 0.016 0.804 0.180
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.4244 0.8084 0.000 0.032 0.800 0.168
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0188 0.9471 0.000 0.996 0.004 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.2546 0.8250 0.008 0.092 0.900 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.4227 0.8270 0.000 0.060 0.820 0.120
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.1004 0.8786 0.000 0.004 0.024 0.972
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1732 0.8679 0.000 0.000 0.920 0.000 0.080
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1924 0.8998 0.000 0.064 0.924 0.004 0.008
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1792 0.4366 0.916 0.000 0.000 0.000 0.084
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.4818 0.5382 0.720 0.000 0.100 0.000 0.180
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.6851 0.2319 0.096 0.000 0.060 0.512 0.332
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.4245 0.8074 0.000 0.092 0.792 0.008 0.108
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.1341 0.4752 0.944 0.000 0.000 0.000 0.056
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0703 0.8155 0.000 0.000 0.000 0.976 0.024
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.3393 0.7367 0.000 0.044 0.008 0.848 0.100
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.1444 0.4855 0.948 0.000 0.012 0.000 0.040
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0162 0.8157 0.000 0.000 0.000 0.996 0.004
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.4305 -0.4508 0.512 0.000 0.000 0.000 0.488
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.9291 0.000 1.000 0.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.2179 0.4118 0.888 0.000 0.000 0.000 0.112
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.8396 -0.1676 0.232 0.100 0.416 0.020 0.232
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.5759 0.4161 0.000 0.000 0.128 0.596 0.276
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1478 0.4804 0.936 0.000 0.000 0.000 0.064
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.2144 0.5271 0.912 0.000 0.020 0.000 0.068
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.1704 0.8985 0.000 0.068 0.928 0.000 0.004
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1549 0.8717 0.000 0.000 0.944 0.016 0.040
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.9291 0.000 1.000 0.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4852 0.6326 0.000 0.644 0.016 0.016 0.324
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.2824 0.8206 0.116 0.864 0.000 0.000 0.020
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0566 0.9234 0.000 0.984 0.000 0.012 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.4277 0.5476 0.768 0.000 0.076 0.000 0.156
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.6706 0.3730 0.488 0.000 0.284 0.008 0.220
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1893 0.9007 0.000 0.048 0.928 0.000 0.024
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0290 0.9268 0.000 0.992 0.000 0.000 0.008
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 5 0.4656 0.3687 0.480 0.000 0.000 0.012 0.508
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.6825 0.1113 0.340 0.000 0.332 0.000 0.328
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.3956 0.7788 0.004 0.808 0.080 0.000 0.108
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0992 0.9169 0.008 0.968 0.000 0.000 0.024
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.3707 0.6131 0.000 0.000 0.000 0.716 0.284
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.4732 0.5361 0.716 0.000 0.076 0.000 0.208
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.4440 0.6533 0.000 0.660 0.012 0.004 0.324
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0510 0.9229 0.000 0.984 0.000 0.000 0.016
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2865 0.8494 0.008 0.856 0.004 0.000 0.132
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.3089 0.7586 0.012 0.000 0.040 0.872 0.076
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 5 0.6563 0.3703 0.368 0.000 0.116 0.024 0.492
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.7472 -0.0488 0.064 0.000 0.268 0.196 0.472
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.9291 0.000 1.000 0.000 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2488 0.7294 0.000 0.000 0.124 0.872 0.004
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 1 0.7956 0.3194 0.460 0.200 0.184 0.000 0.156
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0510 0.8156 0.000 0.000 0.000 0.984 0.016
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.9291 0.000 1.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.4301 0.6846 0.020 0.000 0.020 0.756 0.204
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.2017 0.8926 0.000 0.080 0.912 0.000 0.008
#> 117673A3-2918-4702-8583-B66ADE6E4338 1 0.8308 0.2896 0.364 0.152 0.272 0.000 0.212
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4594 0.8157 0.008 0.044 0.800 0.068 0.080
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.4395 0.5441 0.748 0.000 0.064 0.000 0.188
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.2612 0.8600 0.000 0.124 0.868 0.000 0.008
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9291 0.000 1.000 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 5 0.6540 0.4483 0.288 0.000 0.000 0.236 0.476
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.9291 0.000 1.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 5 0.6439 0.2164 0.180 0.000 0.000 0.372 0.448
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.1741 0.8678 0.000 0.000 0.936 0.024 0.040
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.2054 0.8973 0.000 0.072 0.916 0.008 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.2364 0.8776 0.000 0.020 0.908 0.064 0.008
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2124 0.7867 0.004 0.000 0.000 0.900 0.096
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.3177 0.7048 0.000 0.000 0.000 0.792 0.208
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1082 0.9096 0.000 0.964 0.028 0.000 0.008
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2674 0.8474 0.000 0.856 0.004 0.000 0.140
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0404 0.8159 0.000 0.000 0.000 0.988 0.012
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.3353 0.6818 0.000 0.000 0.008 0.796 0.196
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.2060 0.8995 0.008 0.052 0.924 0.000 0.016
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.9291 0.000 1.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1942 0.8729 0.000 0.000 0.920 0.068 0.012
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.9291 0.000 1.000 0.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3790 0.6407 0.000 0.000 0.004 0.724 0.272
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.4102 0.0454 0.692 0.000 0.004 0.004 0.300
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0771 0.8109 0.000 0.000 0.004 0.976 0.020
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0162 0.9282 0.000 0.996 0.000 0.000 0.004
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.1904 0.8991 0.000 0.028 0.936 0.016 0.020
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.3191 0.8345 0.000 0.012 0.868 0.060 0.060
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.4655 0.0409 0.384 0.000 0.012 0.004 0.600
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0955 0.8089 0.000 0.000 0.004 0.968 0.028
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9291 0.000 1.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2629 0.8500 0.000 0.860 0.004 0.000 0.136
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 5 0.4307 0.3427 0.500 0.000 0.000 0.000 0.500
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1571 0.8086 0.000 0.000 0.004 0.936 0.060
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.2112 0.8928 0.000 0.084 0.908 0.004 0.004
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.1981 0.8775 0.000 0.000 0.920 0.064 0.016
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.1591 0.9015 0.000 0.052 0.940 0.004 0.004
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0162 0.9282 0.000 0.996 0.000 0.000 0.004
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1836 0.8883 0.000 0.032 0.932 0.000 0.036
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.2060 0.9013 0.000 0.052 0.924 0.016 0.008
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0404 0.8134 0.000 0.000 0.000 0.988 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0291 0.9386 0.000 0.000 0.992 0.000 0.004 0.004
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.9399 0.000 0.000 1.000 0.000 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 5 0.5728 0.1643 0.380 0.000 0.000 0.000 0.452 0.168
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.0146 0.7084 0.004 0.000 0.000 0.000 0.996 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.6034 0.0611 0.400 0.000 0.000 0.252 0.348 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.1745 0.8827 0.068 0.012 0.920 0.000 0.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.6056 0.0187 0.412 0.000 0.000 0.000 0.296 0.292
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0632 0.7658 0.024 0.000 0.000 0.976 0.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.4533 0.6406 0.008 0.044 0.000 0.768 0.088 0.092
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 5 0.4952 0.4849 0.252 0.000 0.000 0.000 0.632 0.116
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0458 0.7655 0.016 0.000 0.000 0.984 0.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1296 0.5521 0.948 0.000 0.004 0.000 0.004 0.044
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.9112 0.000 1.000 0.000 0.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.5933 0.1446 0.452 0.000 0.000 0.000 0.236 0.312
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.3608 0.6028 0.000 0.060 0.068 0.004 0.832 0.036
#> 0EA8288E-824D-4304-A053-5A833361F5C5 6 0.4695 0.1660 0.008 0.000 0.028 0.460 0.000 0.504
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 5 0.5393 0.4217 0.256 0.000 0.000 0.000 0.576 0.168
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 5 0.4141 0.6030 0.092 0.000 0.000 0.000 0.740 0.168
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.9399 0.000 0.000 1.000 0.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.2101 0.8674 0.000 0.000 0.892 0.004 0.100 0.004
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.1082 0.8876 0.000 0.956 0.000 0.000 0.004 0.040
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 6 0.5093 0.4889 0.000 0.260 0.000 0.072 0.024 0.644
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0000 0.9112 0.000 1.000 0.000 0.000 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1267 0.8625 0.000 0.940 0.000 0.060 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 5 0.1461 0.7074 0.016 0.000 0.000 0.000 0.940 0.044
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.0820 0.6992 0.000 0.000 0.012 0.000 0.972 0.016
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0146 0.9391 0.000 0.000 0.996 0.000 0.000 0.004
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.9112 0.000 1.000 0.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.3273 0.5242 0.776 0.000 0.004 0.008 0.000 0.212
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 6 0.6556 0.2200 0.056 0.000 0.364 0.000 0.148 0.432
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.3703 0.7214 0.000 0.788 0.000 0.000 0.104 0.108
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0146 0.9101 0.004 0.996 0.000 0.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.3782 0.4927 0.412 0.000 0.000 0.588 0.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.0622 0.7099 0.008 0.000 0.000 0.000 0.980 0.012
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 6 0.4393 0.3417 0.000 0.340 0.000 0.008 0.024 0.628
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0603 0.9035 0.004 0.980 0.000 0.000 0.000 0.016
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3878 0.5250 0.004 0.668 0.000 0.000 0.008 0.320
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.5900 0.4400 0.080 0.000 0.000 0.548 0.316 0.056
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4725 0.2673 0.604 0.000 0.332 0.000 0.000 0.064
#> FF7021BB-414B-4466-BC43-372D856E6A1D 6 0.5835 0.4766 0.000 0.000 0.052 0.144 0.188 0.616
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.9112 0.000 1.000 0.000 0.000 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2378 0.6392 0.000 0.000 0.152 0.848 0.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.4291 0.3078 0.000 0.356 0.008 0.000 0.620 0.016
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1610 0.7566 0.084 0.000 0.000 0.916 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0291 0.9089 0.004 0.992 0.000 0.000 0.000 0.004
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.4799 0.5317 0.340 0.000 0.000 0.592 0.068 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.9399 0.000 0.000 1.000 0.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.3056 0.6176 0.000 0.140 0.012 0.000 0.832 0.016
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4257 0.6608 0.000 0.012 0.724 0.008 0.228 0.028
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 5 0.1657 0.7081 0.016 0.000 0.000 0.000 0.928 0.056
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.1267 0.8954 0.000 0.060 0.940 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9112 0.000 1.000 0.000 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3163 0.3565 0.780 0.000 0.004 0.212 0.004 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.9112 0.000 1.000 0.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.3835 0.0516 0.656 0.000 0.004 0.336 0.004 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.4485 0.6139 0.224 0.000 0.708 0.020 0.048 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.9399 0.000 0.000 1.000 0.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.9399 0.000 0.000 1.000 0.000 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.3348 0.6932 0.216 0.000 0.000 0.768 0.016 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.3911 0.5445 0.368 0.000 0.000 0.624 0.008 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0458 0.8994 0.000 0.984 0.016 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3707 0.5369 0.000 0.680 0.000 0.000 0.008 0.312
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.3523 0.7118 0.180 0.000 0.000 0.780 0.040 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.3023 0.5248 0.000 0.000 0.000 0.768 0.000 0.232
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0909 0.9302 0.000 0.012 0.968 0.000 0.020 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0146 0.9102 0.000 0.996 0.000 0.000 0.000 0.004
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0146 0.9391 0.000 0.000 0.996 0.000 0.000 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1946 0.8602 0.000 0.912 0.000 0.012 0.004 0.072
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.4453 0.5312 0.332 0.000 0.000 0.624 0.000 0.044
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.4391 0.4096 0.720 0.000 0.000 0.004 0.188 0.088
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0632 0.7555 0.000 0.000 0.000 0.976 0.000 0.024
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.9112 0.000 1.000 0.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.9399 0.000 0.000 1.000 0.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.2800 0.8369 0.000 0.004 0.860 0.016 0.112 0.008
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.2147 0.2633 0.084 0.000 0.000 0.000 0.020 0.896
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1074 0.7527 0.000 0.000 0.000 0.960 0.012 0.028
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9112 0.000 1.000 0.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.4285 0.2147 0.000 0.552 0.000 0.008 0.008 0.432
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2595 0.5399 0.836 0.000 0.000 0.000 0.004 0.160
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1492 0.7551 0.036 0.000 0.000 0.940 0.000 0.024
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0458 0.9336 0.000 0.016 0.984 0.000 0.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0146 0.9392 0.000 0.000 0.996 0.000 0.004 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.9399 0.000 0.000 1.000 0.000 0.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.9112 0.000 1.000 0.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1285 0.9120 0.000 0.004 0.944 0.000 0.052 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0146 0.9388 0.000 0.004 0.996 0.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0717 0.7581 0.000 0.000 0.000 0.976 0.008 0.016
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17471 rows and 87 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 0.859 0.896 0.951 0.1452 0.933 0.933
#> 3 3 0.411 0.652 0.853 1.2992 0.769 0.752
#> 4 4 0.368 0.649 0.793 0.4357 0.834 0.764
#> 5 5 0.439 0.643 0.785 0.1821 0.886 0.795
#> 6 6 0.425 0.649 0.792 0.0504 0.967 0.930
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0000 0.948 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.948 0.000 1.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 2 0.2043 0.933 0.032 0.968
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.0000 0.948 0.000 1.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 2 0.0376 0.947 0.004 0.996
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0000 0.948 0.000 1.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 2 0.2043 0.933 0.032 0.968
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 2 0.7219 0.761 0.200 0.800
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.1414 0.940 0.020 0.980
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 2 0.2043 0.933 0.032 0.968
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 2 0.9286 0.507 0.344 0.656
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 2 0.0672 0.946 0.008 0.992
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.948 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 2 0.2043 0.933 0.032 0.968
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.948 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.2423 0.991 0.960 0.040
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 2 0.2043 0.933 0.032 0.968
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 2 0.2043 0.933 0.032 0.968
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.948 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.0376 0.947 0.004 0.996
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.948 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.948 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0000 0.948 0.000 1.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.948 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 2 0.2043 0.933 0.032 0.968
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0000 0.948 0.000 1.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0000 0.948 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.948 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 2 0.2423 0.929 0.040 0.960
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.0672 0.947 0.008 0.992
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.948 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0376 0.947 0.004 0.996
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 2 0.2043 0.936 0.032 0.968
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.0000 0.948 0.000 1.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.948 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.948 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.948 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.4815 0.867 0.104 0.896
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.7056 0.771 0.192 0.808
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0000 0.948 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.948 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.2043 0.995 0.968 0.032
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.948 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 2 0.9661 0.402 0.392 0.608
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.948 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 2 0.3431 0.909 0.064 0.936
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0000 0.948 0.000 1.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.948 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.1414 0.940 0.020 0.980
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 2 0.2043 0.933 0.032 0.968
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.948 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.948 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 2 0.0672 0.946 0.008 0.992
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.948 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 2 0.0672 0.946 0.008 0.992
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.0672 0.947 0.008 0.992
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.948 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.2603 0.924 0.044 0.956
#> C41F3064-4483-4796-B860-82155BAA5157 2 0.3274 0.913 0.060 0.940
#> F4232B90-51B9-43EE-9971-35B3A318758F 2 0.0938 0.946 0.012 0.988
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.948 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.948 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 2 0.6887 0.777 0.184 0.816
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.9866 0.298 0.432 0.568
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.0000 0.948 0.000 1.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.948 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.1184 0.942 0.016 0.984
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0672 0.946 0.008 0.992
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 2 0.8608 0.635 0.284 0.716
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 2 0.2043 0.933 0.032 0.968
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 2 0.9795 0.342 0.416 0.584
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.948 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.1184 0.942 0.016 0.984
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0376 0.947 0.004 0.996
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 2 0.1184 0.944 0.016 0.984
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 2 0.9866 0.298 0.432 0.568
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.948 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.948 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 2 0.2423 0.929 0.040 0.960
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.2043 0.995 0.968 0.032
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0376 0.947 0.004 0.996
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.1184 0.942 0.016 0.984
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0000 0.948 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.948 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.948 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.1414 0.940 0.020 0.980
#> 0D907A63-D23F-4684-80D9-0BA552435B86 2 0.9661 0.402 0.392 0.608
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0237 0.81994 0.004 0.996 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0829 0.81746 0.012 0.984 0.004
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.6295 0.72380 0.528 0.472 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.0237 0.82002 0.004 0.996 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 2 0.2356 0.77142 0.072 0.928 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.3686 0.68656 0.140 0.860 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.6215 0.73668 0.572 0.428 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 2 0.9648 -0.55349 0.384 0.408 0.208
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.1337 0.81305 0.012 0.972 0.016
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.6299 0.71945 0.524 0.476 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 2 0.6629 0.22632 0.016 0.624 0.360
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 2 0.5397 0.35410 0.280 0.720 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4555 0.56241 0.200 0.800 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.6215 0.73668 0.572 0.428 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0237 0.82002 0.004 0.996 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.0237 0.98970 0.000 0.004 0.996
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.6168 0.72940 0.588 0.412 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.6295 0.72380 0.528 0.472 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0829 0.81746 0.012 0.984 0.004
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.0424 0.81948 0.008 0.992 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3038 0.72347 0.104 0.896 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0237 0.82007 0.004 0.996 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0237 0.82002 0.004 0.996 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0424 0.81958 0.008 0.992 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.6302 0.71378 0.520 0.480 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0237 0.82002 0.004 0.996 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0424 0.82053 0.008 0.992 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.82019 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1031 -0.08058 0.976 0.024 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.1643 0.80206 0.044 0.956 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0237 0.82007 0.004 0.996 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.5397 0.36323 0.280 0.720 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 2 0.5858 0.45591 0.240 0.740 0.020
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.0237 0.82002 0.004 0.996 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0237 0.82007 0.004 0.996 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.82019 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0892 0.81252 0.020 0.980 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.4563 0.69408 0.036 0.852 0.112
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.9584 0.53933 0.428 0.372 0.200
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0237 0.82007 0.004 0.996 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0237 0.82002 0.004 0.996 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.0237 0.99488 0.004 0.000 0.996
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0237 0.82002 0.004 0.996 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 2 0.6836 0.10933 0.016 0.572 0.412
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.3686 0.66275 0.140 0.860 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 2 0.6715 0.39106 0.228 0.716 0.056
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0829 0.81746 0.012 0.984 0.004
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0237 0.82002 0.004 0.996 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.2297 0.79506 0.036 0.944 0.020
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.6299 0.71774 0.524 0.476 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.82019 0.000 1.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.3686 0.66275 0.140 0.860 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 2 0.4974 0.47487 0.236 0.764 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.82019 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 2 0.4887 0.49904 0.228 0.772 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.2261 0.78165 0.068 0.932 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0829 0.81746 0.012 0.984 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.2773 0.78057 0.024 0.928 0.048
#> C41F3064-4483-4796-B860-82155BAA5157 2 0.3253 0.76967 0.036 0.912 0.052
#> F4232B90-51B9-43EE-9971-35B3A318758F 2 0.4974 0.48461 0.236 0.764 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.82019 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0237 0.82007 0.004 0.996 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 2 0.6984 0.46387 0.088 0.720 0.192
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.6925 0.00671 0.016 0.532 0.452
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.0424 0.82053 0.008 0.992 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.82019 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.1482 0.81028 0.020 0.968 0.012
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.5529 0.30740 0.296 0.704 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 2 0.9889 -0.47461 0.296 0.408 0.296
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.6307 0.69835 0.512 0.488 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 2 0.6897 0.04634 0.016 0.548 0.436
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.82019 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.1482 0.81028 0.020 0.968 0.012
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0829 0.81731 0.012 0.984 0.004
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 2 0.4974 0.48816 0.236 0.764 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 2 0.6925 0.00671 0.016 0.532 0.452
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.82019 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.82019 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1031 -0.08058 0.976 0.024 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.0237 0.99488 0.004 0.000 0.996
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0424 0.81948 0.008 0.992 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.1482 0.81028 0.020 0.968 0.012
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0237 0.81994 0.004 0.996 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.82019 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0237 0.81994 0.004 0.996 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.1636 0.80811 0.020 0.964 0.016
#> 0D907A63-D23F-4684-80D9-0BA552435B86 2 0.6836 0.10933 0.016 0.572 0.412
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0967 0.8312 0.004 0.976 0.004 0.016
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.1484 0.8269 0.016 0.960 0.004 0.020
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.7563 0.7353 0.560 0.208 0.016 0.216
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.1209 0.8373 0.004 0.964 0.000 0.032
#> 2F38E3B1-4975-4877-9DCC-C00270602180 2 0.8615 -0.1505 0.100 0.488 0.120 0.292
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.5432 0.6582 0.136 0.768 0.024 0.072
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.6790 0.7288 0.608 0.196 0.000 0.196
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.8686 -0.1649 0.364 0.216 0.044 0.376
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.1994 0.8317 0.008 0.936 0.004 0.052
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.7591 0.7331 0.556 0.212 0.016 0.216
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.4879 0.6405 0.012 0.228 0.016 0.744
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 2 0.7235 0.2581 0.272 0.596 0.032 0.100
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4728 0.6497 0.188 0.776 0.016 0.020
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.6790 0.7309 0.608 0.200 0.000 0.192
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.1004 0.8371 0.004 0.972 0.000 0.024
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.5317 0.9851 0.004 0.004 0.532 0.460
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.6655 0.7184 0.624 0.192 0.000 0.184
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.7563 0.7353 0.560 0.208 0.016 0.216
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.1697 0.8205 0.016 0.952 0.004 0.028
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.1191 0.8320 0.004 0.968 0.004 0.024
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3996 0.7415 0.104 0.836 0.000 0.060
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.1305 0.8352 0.004 0.960 0.000 0.036
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1042 0.8376 0.008 0.972 0.000 0.020
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1807 0.8333 0.008 0.940 0.000 0.052
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.7590 0.7303 0.556 0.220 0.016 0.208
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.1004 0.8371 0.004 0.972 0.000 0.024
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.1114 0.8322 0.008 0.972 0.004 0.016
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0779 0.8370 0.004 0.980 0.000 0.016
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2522 0.2624 0.920 0.016 0.052 0.012
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.3170 0.7994 0.044 0.896 0.016 0.044
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.1489 0.8341 0.004 0.952 0.000 0.044
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.6242 0.4519 0.260 0.664 0.024 0.052
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 2 0.8835 -0.4049 0.228 0.384 0.052 0.336
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.1004 0.8371 0.004 0.972 0.000 0.024
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.1305 0.8352 0.004 0.960 0.000 0.036
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0817 0.8368 0.000 0.976 0.000 0.024
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2256 0.8210 0.020 0.924 0.000 0.056
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.4817 0.6460 0.040 0.768 0.004 0.188
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.8756 0.1489 0.408 0.312 0.048 0.232
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.1004 0.8363 0.004 0.972 0.000 0.024
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.1256 0.8372 0.008 0.964 0.000 0.028
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.4977 0.9926 0.000 0.000 0.540 0.460
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.1109 0.8371 0.004 0.968 0.000 0.028
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.3791 0.6497 0.000 0.200 0.004 0.796
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.4541 0.6862 0.144 0.796 0.000 0.060
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 2 0.8610 -0.2821 0.220 0.436 0.044 0.300
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.1697 0.8205 0.016 0.952 0.004 0.028
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.1004 0.8371 0.004 0.972 0.000 0.024
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.3243 0.7849 0.036 0.876 0.000 0.088
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.7591 0.7329 0.556 0.216 0.016 0.212
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.1191 0.8305 0.004 0.968 0.004 0.024
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4541 0.6862 0.144 0.796 0.000 0.060
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 2 0.8439 -0.0951 0.236 0.500 0.052 0.212
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1305 0.8326 0.004 0.960 0.000 0.036
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 2 0.7394 0.2866 0.224 0.604 0.032 0.140
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.4466 0.7292 0.068 0.828 0.016 0.088
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.1484 0.8269 0.016 0.960 0.004 0.020
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.3027 0.7780 0.020 0.888 0.004 0.088
#> C41F3064-4483-4796-B860-82155BAA5157 2 0.8212 -0.4048 0.052 0.416 0.120 0.412
#> F4232B90-51B9-43EE-9971-35B3A318758F 2 0.8533 -0.1414 0.232 0.484 0.052 0.232
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1004 0.8363 0.004 0.972 0.000 0.024
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.1743 0.8268 0.004 0.940 0.000 0.056
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.7081 0.2789 0.088 0.408 0.012 0.492
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.3219 0.6084 0.000 0.164 0.000 0.836
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.1484 0.8331 0.016 0.960 0.004 0.020
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0895 0.8376 0.004 0.976 0.000 0.020
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.2161 0.8125 0.016 0.932 0.004 0.048
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.5697 0.4821 0.292 0.656 0.000 0.052
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.8375 0.1570 0.272 0.196 0.044 0.488
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.7630 0.7018 0.548 0.240 0.016 0.196
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.3356 0.6256 0.000 0.176 0.000 0.824
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0779 0.8370 0.004 0.980 0.000 0.016
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.2161 0.8125 0.016 0.932 0.004 0.048
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.1732 0.8323 0.008 0.948 0.004 0.040
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.8114 0.1408 0.084 0.072 0.404 0.440
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.3219 0.6084 0.000 0.164 0.000 0.836
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0779 0.8370 0.004 0.980 0.000 0.016
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0921 0.8366 0.000 0.972 0.000 0.028
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2161 0.2737 0.932 0.016 0.048 0.004
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.4977 0.9926 0.000 0.000 0.540 0.460
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.1082 0.8308 0.004 0.972 0.004 0.020
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.2161 0.8108 0.016 0.932 0.004 0.048
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.1082 0.8302 0.004 0.972 0.004 0.020
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0779 0.8370 0.004 0.980 0.000 0.016
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0967 0.8312 0.004 0.976 0.004 0.016
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.2328 0.8058 0.016 0.924 0.004 0.056
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3791 0.6497 0.000 0.200 0.004 0.796
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0865 0.86162 0.000 0.972 0.000 0.004 0.024
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.2295 0.83783 0.008 0.900 0.000 0.004 0.088
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.4890 0.68270 0.720 0.140 0.000 0.140 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.1168 0.86664 0.008 0.960 0.000 0.032 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.5838 0.18788 0.248 0.124 0.000 0.620 0.008
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.5647 0.61797 0.152 0.700 0.000 0.104 0.044
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.4334 0.68425 0.768 0.140 0.000 0.092 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.8831 0.18038 0.384 0.152 0.184 0.248 0.032
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.2199 0.86067 0.008 0.924 0.016 0.044 0.008
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.4930 0.68044 0.716 0.140 0.000 0.144 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.5188 0.38001 0.024 0.028 0.304 0.644 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 2 0.7085 -0.12390 0.328 0.468 0.000 0.168 0.036
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4180 0.64210 0.220 0.744 0.000 0.036 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.4376 0.68489 0.764 0.144 0.000 0.092 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0865 0.86663 0.004 0.972 0.000 0.024 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.0798 0.97318 0.000 0.000 0.976 0.008 0.016
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.4514 0.67772 0.768 0.136 0.000 0.088 0.008
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.4890 0.68270 0.720 0.140 0.000 0.140 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.2612 0.81390 0.008 0.868 0.000 0.000 0.124
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.1670 0.85552 0.000 0.936 0.000 0.012 0.052
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3589 0.77044 0.132 0.824 0.000 0.040 0.004
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.1267 0.86642 0.012 0.960 0.000 0.024 0.004
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1216 0.86774 0.020 0.960 0.000 0.020 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2095 0.86070 0.012 0.920 0.000 0.060 0.008
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.4968 0.68003 0.712 0.152 0.000 0.136 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0955 0.86643 0.004 0.968 0.000 0.028 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.1798 0.85050 0.004 0.928 0.000 0.004 0.064
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.1012 0.86611 0.020 0.968 0.000 0.012 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.4449 0.02202 0.752 0.000 0.000 0.080 0.168
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.3445 0.80970 0.052 0.856 0.000 0.072 0.020
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.1547 0.86493 0.016 0.948 0.000 0.032 0.004
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.5663 0.40953 0.300 0.620 0.000 0.052 0.028
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.5503 0.22293 0.300 0.072 0.000 0.620 0.008
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.0955 0.86643 0.004 0.968 0.000 0.028 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.1372 0.86605 0.016 0.956 0.000 0.024 0.004
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.1117 0.86564 0.016 0.964 0.000 0.020 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2227 0.85198 0.048 0.916 0.000 0.032 0.004
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.5861 0.58742 0.036 0.692 0.108 0.156 0.008
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.8878 0.28097 0.420 0.228 0.184 0.088 0.080
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0865 0.86644 0.000 0.972 0.000 0.024 0.004
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.1300 0.86631 0.016 0.956 0.000 0.028 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.0000 0.98664 0.000 0.000 1.000 0.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.1082 0.86721 0.008 0.964 0.000 0.028 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.4963 0.36590 0.000 0.040 0.352 0.608 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.4232 0.70776 0.180 0.772 0.000 0.036 0.012
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.7552 0.04721 0.324 0.152 0.052 0.460 0.012
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.2612 0.81390 0.008 0.868 0.000 0.000 0.124
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0955 0.86643 0.004 0.968 0.000 0.028 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.3804 0.78803 0.032 0.836 0.020 0.104 0.008
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.4970 0.68169 0.712 0.148 0.000 0.140 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.1892 0.84395 0.004 0.916 0.000 0.000 0.080
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4232 0.70776 0.180 0.772 0.000 0.036 0.012
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.6630 -0.05152 0.348 0.176 0.000 0.468 0.008
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1780 0.86224 0.028 0.940 0.000 0.024 0.008
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 2 0.7273 -0.31806 0.304 0.404 0.000 0.268 0.024
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.4919 0.67510 0.100 0.744 0.000 0.140 0.016
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.2295 0.83783 0.008 0.900 0.000 0.004 0.088
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.5145 0.71418 0.020 0.748 0.036 0.036 0.160
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.3896 0.24890 0.116 0.052 0.004 0.820 0.008
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.6518 -0.00459 0.344 0.160 0.000 0.488 0.008
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1377 0.86591 0.020 0.956 0.000 0.020 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.1990 0.85610 0.028 0.928 0.000 0.040 0.004
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.7711 0.30674 0.120 0.232 0.164 0.484 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.5142 0.32742 0.000 0.044 0.392 0.564 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.2482 0.84820 0.016 0.904 0.000 0.016 0.064
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.1117 0.86643 0.020 0.964 0.000 0.016 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.2955 0.81773 0.008 0.864 0.004 0.008 0.116
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.5661 0.54243 0.264 0.644 0.000 0.028 0.064
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.9090 -0.05123 0.296 0.148 0.268 0.252 0.036
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.5144 0.65439 0.692 0.176 0.000 0.132 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.5619 0.32788 0.000 0.048 0.376 0.560 0.016
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.1012 0.86611 0.020 0.968 0.000 0.012 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.3052 0.81114 0.008 0.856 0.004 0.008 0.124
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.1579 0.86454 0.000 0.944 0.000 0.024 0.032
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.5901 0.00000 0.156 0.020 0.000 0.172 0.652
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.5142 0.32742 0.000 0.044 0.392 0.564 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.1012 0.86611 0.020 0.968 0.000 0.012 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.1211 0.86520 0.016 0.960 0.000 0.024 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.4179 0.05257 0.776 0.000 0.000 0.072 0.152
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.0000 0.98664 0.000 0.000 1.000 0.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.1082 0.86125 0.000 0.964 0.000 0.008 0.028
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.3368 0.77529 0.008 0.820 0.004 0.004 0.164
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.1877 0.84944 0.000 0.924 0.000 0.012 0.064
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.1012 0.86611 0.020 0.968 0.000 0.012 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0865 0.86162 0.000 0.972 0.000 0.004 0.024
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.3490 0.77067 0.008 0.816 0.008 0.004 0.164
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.4963 0.36590 0.000 0.040 0.352 0.608 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0935 0.849 0.004 0.964 0.000 0.000 0.000 0.032
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.2135 0.810 0.000 0.872 0.000 0.000 0.000 0.128
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.2473 0.635 0.856 0.136 0.000 0.008 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.1074 0.852 0.028 0.960 0.000 0.000 0.000 0.012
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.7913 0.124 0.360 0.092 0.044 0.272 0.232 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.5264 0.612 0.168 0.700 0.000 0.020 0.076 0.036
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.3123 0.610 0.824 0.136 0.000 0.000 0.040 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.8904 0.122 0.336 0.140 0.140 0.228 0.140 0.016
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.2495 0.844 0.040 0.904 0.004 0.016 0.008 0.028
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.2615 0.634 0.852 0.136 0.000 0.008 0.004 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2113 0.565 0.048 0.000 0.008 0.912 0.032 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 2 0.6681 -0.140 0.348 0.464 0.000 0.032 0.128 0.028
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4594 0.625 0.192 0.728 0.012 0.008 0.056 0.004
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.3163 0.612 0.820 0.140 0.000 0.000 0.040 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0806 0.852 0.020 0.972 0.000 0.000 0.000 0.008
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.3159 0.929 0.004 0.000 0.812 0.168 0.012 0.004
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.3316 0.596 0.812 0.136 0.000 0.000 0.052 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.2473 0.635 0.856 0.136 0.000 0.008 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.2527 0.779 0.000 0.832 0.000 0.000 0.000 0.168
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.1615 0.842 0.004 0.928 0.000 0.004 0.000 0.064
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3624 0.751 0.160 0.800 0.008 0.008 0.004 0.020
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.1552 0.850 0.036 0.940 0.000 0.000 0.004 0.020
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1225 0.853 0.032 0.956 0.000 0.004 0.004 0.004
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2620 0.841 0.048 0.892 0.000 0.032 0.004 0.024
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2593 0.637 0.844 0.148 0.000 0.008 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0909 0.851 0.020 0.968 0.000 0.000 0.000 0.012
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.1588 0.835 0.004 0.924 0.000 0.000 0.000 0.072
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.1147 0.851 0.028 0.960 0.000 0.004 0.004 0.004
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 5 0.4061 0.384 0.164 0.000 0.088 0.000 0.748 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.3018 0.797 0.100 0.856 0.000 0.008 0.008 0.028
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.1857 0.848 0.044 0.924 0.000 0.000 0.004 0.028
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.6011 0.380 0.244 0.596 0.012 0.008 0.120 0.020
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.6884 -0.048 0.312 0.048 0.000 0.360 0.280 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.0909 0.851 0.020 0.968 0.000 0.000 0.000 0.012
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.1624 0.850 0.040 0.936 0.000 0.000 0.004 0.020
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.1338 0.850 0.032 0.952 0.000 0.004 0.004 0.008
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2320 0.834 0.080 0.892 0.000 0.000 0.004 0.024
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.5948 0.540 0.080 0.660 0.020 0.184 0.024 0.032
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.9045 0.141 0.340 0.208 0.140 0.072 0.192 0.048
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.1176 0.851 0.020 0.956 0.000 0.000 0.000 0.024
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.1299 0.852 0.036 0.952 0.000 0.004 0.004 0.004
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.2527 0.965 0.000 0.000 0.832 0.168 0.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0972 0.852 0.028 0.964 0.000 0.000 0.000 0.008
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1129 0.582 0.008 0.004 0.012 0.964 0.012 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.4170 0.686 0.200 0.752 0.012 0.008 0.012 0.016
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.7380 0.247 0.352 0.128 0.000 0.308 0.212 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.2527 0.779 0.000 0.832 0.000 0.000 0.000 0.168
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0909 0.851 0.020 0.968 0.000 0.000 0.000 0.012
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.4111 0.767 0.068 0.812 0.008 0.068 0.012 0.032
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2553 0.638 0.848 0.144 0.000 0.008 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.1970 0.827 0.008 0.900 0.000 0.000 0.000 0.092
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4170 0.686 0.200 0.752 0.012 0.008 0.012 0.016
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.7455 0.335 0.360 0.148 0.000 0.212 0.280 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1723 0.848 0.048 0.932 0.000 0.004 0.004 0.012
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 2 0.7461 -0.357 0.316 0.396 0.000 0.096 0.172 0.020
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.4591 0.661 0.172 0.744 0.000 0.032 0.028 0.024
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.2135 0.810 0.000 0.872 0.000 0.000 0.000 0.128
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.5427 0.633 0.036 0.684 0.028 0.028 0.016 0.208
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.6819 0.256 0.200 0.020 0.044 0.504 0.232 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.7437 0.309 0.356 0.136 0.000 0.228 0.280 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1514 0.851 0.036 0.944 0.000 0.004 0.004 0.012
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2154 0.840 0.064 0.908 0.000 0.004 0.004 0.020
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.6681 0.251 0.172 0.200 0.020 0.556 0.048 0.004
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.1692 0.567 0.008 0.012 0.048 0.932 0.000 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.2126 0.833 0.020 0.904 0.000 0.000 0.004 0.072
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.1363 0.851 0.028 0.952 0.000 0.004 0.004 0.012
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.2706 0.785 0.000 0.832 0.008 0.000 0.000 0.160
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.5967 0.510 0.188 0.612 0.012 0.008 0.164 0.016
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.8992 -0.129 0.264 0.136 0.192 0.276 0.116 0.016
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.3109 0.622 0.812 0.168 0.000 0.016 0.004 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1956 0.563 0.008 0.016 0.032 0.928 0.000 0.016
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.1147 0.851 0.028 0.960 0.000 0.004 0.004 0.004
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.2778 0.778 0.000 0.824 0.008 0.000 0.000 0.168
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.1760 0.850 0.020 0.928 0.000 0.004 0.000 0.048
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.3814 0.000 0.220 0.012 0.004 0.008 0.004 0.752
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1692 0.567 0.008 0.012 0.048 0.932 0.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.1147 0.851 0.028 0.960 0.000 0.004 0.004 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.1440 0.850 0.032 0.948 0.000 0.004 0.004 0.012
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 5 0.4086 0.489 0.464 0.000 0.008 0.000 0.528 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.2527 0.965 0.000 0.000 0.832 0.168 0.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.1082 0.848 0.004 0.956 0.000 0.000 0.000 0.040
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.3702 0.716 0.024 0.760 0.008 0.000 0.000 0.208
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.1788 0.837 0.004 0.916 0.000 0.004 0.000 0.076
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.1147 0.851 0.028 0.960 0.000 0.004 0.004 0.004
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0935 0.849 0.004 0.964 0.000 0.000 0.000 0.032
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.3888 0.710 0.028 0.756 0.008 0.004 0.000 0.204
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.1129 0.582 0.008 0.004 0.012 0.964 0.012 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17471 rows and 87 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.641 0.900 0.937 0.4574 0.518 0.518
#> 3 3 0.474 0.776 0.814 0.3120 0.923 0.851
#> 4 4 0.716 0.836 0.870 0.1944 0.793 0.541
#> 5 5 0.767 0.699 0.828 0.0807 0.994 0.976
#> 6 6 0.730 0.598 0.769 0.0420 0.886 0.577
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0938 0.968 0.012 0.988
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.1184 0.967 0.016 0.984
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.6343 0.866 0.840 0.160
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.1414 0.966 0.020 0.980
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.6343 0.866 0.840 0.160
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0672 0.969 0.008 0.992
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.7883 0.815 0.764 0.236
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0672 0.877 0.992 0.008
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0672 0.970 0.008 0.992
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.7883 0.815 0.764 0.236
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0376 0.876 0.996 0.004
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.7815 0.818 0.768 0.232
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0376 0.971 0.004 0.996
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.8861 0.720 0.696 0.304
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0672 0.969 0.008 0.992
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0938 0.875 0.988 0.012
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.7453 0.834 0.788 0.212
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.7883 0.815 0.764 0.236
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.1184 0.967 0.016 0.984
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.1414 0.965 0.020 0.980
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0376 0.971 0.004 0.996
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0376 0.971 0.004 0.996
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0672 0.969 0.008 0.992
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0938 0.967 0.012 0.988
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.7883 0.815 0.764 0.236
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.1184 0.968 0.016 0.984
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0376 0.969 0.004 0.996
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0376 0.971 0.004 0.996
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.4161 0.881 0.916 0.084
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.2043 0.959 0.032 0.968
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0376 0.971 0.004 0.996
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0376 0.971 0.004 0.996
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0376 0.876 0.996 0.004
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.1184 0.968 0.016 0.984
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0376 0.971 0.004 0.996
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0376 0.971 0.004 0.996
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0376 0.971 0.004 0.996
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.6148 0.872 0.848 0.152
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4815 0.879 0.896 0.104
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0672 0.969 0.008 0.992
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0376 0.971 0.004 0.996
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.0938 0.875 0.988 0.012
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0672 0.970 0.008 0.992
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0672 0.877 0.992 0.008
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0376 0.971 0.004 0.996
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.875 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.1184 0.967 0.016 0.984
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0672 0.969 0.008 0.992
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.2236 0.951 0.036 0.964
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.7883 0.815 0.764 0.236
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.970 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0376 0.971 0.004 0.996
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.6343 0.866 0.840 0.160
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.970 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.6247 0.867 0.844 0.156
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.9963 -0.105 0.464 0.536
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0938 0.967 0.012 0.988
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.6148 0.825 0.152 0.848
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0376 0.876 0.996 0.004
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.5737 0.874 0.864 0.136
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.970 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0376 0.971 0.004 0.996
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0672 0.877 0.992 0.008
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 1 0.9661 0.333 0.608 0.392
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.0938 0.968 0.012 0.988
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0376 0.971 0.004 0.996
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.2948 0.936 0.052 0.948
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.5946 0.826 0.144 0.856
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0672 0.877 0.992 0.008
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.7950 0.810 0.760 0.240
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0672 0.877 0.992 0.008
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0376 0.971 0.004 0.996
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.1184 0.967 0.016 0.984
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.1633 0.960 0.024 0.976
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.3584 0.881 0.932 0.068
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0672 0.877 0.992 0.008
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0376 0.971 0.004 0.996
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0376 0.971 0.004 0.996
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.5294 0.877 0.880 0.120
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0938 0.875 0.988 0.012
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0938 0.967 0.012 0.988
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.6247 0.820 0.156 0.844
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0938 0.967 0.012 0.988
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0376 0.971 0.004 0.996
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0672 0.969 0.008 0.992
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.4161 0.900 0.084 0.916
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0672 0.877 0.992 0.008
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.7558 0.761 0.144 0.692 0.164
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.8722 0.705 0.152 0.576 0.272
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.4206 0.901 0.872 0.088 0.040
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.7491 0.476 0.324 0.620 0.056
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.4384 0.888 0.868 0.068 0.064
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.7282 0.768 0.144 0.712 0.144
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.3192 0.907 0.888 0.112 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.4605 0.871 0.204 0.000 0.796
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.2703 0.802 0.016 0.928 0.056
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.3192 0.907 0.888 0.112 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.4504 0.876 0.196 0.000 0.804
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1765 0.853 0.956 0.040 0.004
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.799 0.000 1.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.3425 0.905 0.884 0.112 0.004
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.4095 0.795 0.064 0.880 0.056
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.3116 0.844 0.108 0.000 0.892
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.3112 0.902 0.900 0.096 0.004
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.3192 0.907 0.888 0.112 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.7869 0.755 0.152 0.668 0.180
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.8801 0.694 0.152 0.564 0.284
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.799 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.799 0.000 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.3499 0.796 0.072 0.900 0.028
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0892 0.794 0.020 0.980 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.3192 0.907 0.888 0.112 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.4628 0.788 0.088 0.856 0.056
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.7160 0.773 0.148 0.720 0.132
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.799 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.3846 0.817 0.876 0.016 0.108
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.9680 0.535 0.300 0.456 0.244
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.799 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.1525 0.781 0.032 0.964 0.004
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 3 0.4654 0.867 0.208 0.000 0.792
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.5932 0.736 0.164 0.780 0.056
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.799 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.799 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.799 0.000 1.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.8382 0.299 0.424 0.084 0.492
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.8824 0.431 0.364 0.124 0.512
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.6783 0.777 0.088 0.736 0.176
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.799 0.000 1.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.3116 0.844 0.108 0.000 0.892
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.4189 0.794 0.068 0.876 0.056
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.4399 0.880 0.188 0.000 0.812
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0592 0.794 0.012 0.988 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4504 0.678 0.804 0.000 0.196
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.7970 0.753 0.156 0.660 0.184
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.3899 0.797 0.056 0.888 0.056
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.8801 0.689 0.148 0.560 0.292
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.3192 0.907 0.888 0.112 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.5981 0.785 0.080 0.788 0.132
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.1129 0.792 0.020 0.976 0.004
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.4384 0.888 0.868 0.068 0.064
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0424 0.799 0.008 0.992 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.2926 0.865 0.924 0.040 0.036
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.9883 0.382 0.360 0.380 0.260
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.8722 0.705 0.152 0.576 0.272
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.9049 0.557 0.136 0.464 0.400
#> C41F3064-4483-4796-B860-82155BAA5157 3 0.4654 0.868 0.208 0.000 0.792
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.5656 0.826 0.804 0.068 0.128
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0424 0.799 0.008 0.992 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.799 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.4504 0.876 0.196 0.000 0.804
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.4446 0.846 0.112 0.032 0.856
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.8525 0.717 0.148 0.600 0.252
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.799 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.8801 0.689 0.148 0.560 0.292
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3456 0.765 0.036 0.904 0.060
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.3879 0.873 0.152 0.000 0.848
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.3116 0.907 0.892 0.108 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.3941 0.874 0.156 0.000 0.844
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.799 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.8749 0.701 0.152 0.572 0.276
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.8677 0.698 0.140 0.572 0.288
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.7379 0.540 0.376 0.040 0.584
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.4346 0.880 0.184 0.000 0.816
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.799 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.799 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.3889 0.854 0.884 0.032 0.084
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.3686 0.866 0.140 0.000 0.860
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.8722 0.704 0.152 0.576 0.272
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.8984 0.504 0.128 0.436 0.436
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.8749 0.701 0.152 0.572 0.276
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.799 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.7282 0.768 0.144 0.712 0.144
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.8752 0.671 0.132 0.548 0.320
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.4399 0.880 0.188 0.000 0.812
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.3933 0.916 0.008 0.200 0.792 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.3893 0.919 0.008 0.196 0.796 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1296 0.915 0.964 0.028 0.004 0.004
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.7241 0.327 0.536 0.276 0.188 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.2319 0.909 0.932 0.028 0.024 0.016
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.3972 0.917 0.008 0.204 0.788 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.1975 0.913 0.944 0.028 0.012 0.016
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3877 0.859 0.032 0.004 0.124 0.840
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.3870 0.684 0.004 0.788 0.208 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.1296 0.915 0.964 0.028 0.004 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.1004 0.920 0.024 0.000 0.004 0.972
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2096 0.910 0.940 0.016 0.028 0.016
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0657 0.893 0.004 0.984 0.012 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.2422 0.908 0.928 0.028 0.028 0.016
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.4262 0.629 0.008 0.756 0.236 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.2198 0.896 0.008 0.000 0.072 0.920
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1993 0.901 0.944 0.016 0.024 0.016
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1443 0.915 0.960 0.028 0.004 0.008
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.3870 0.915 0.004 0.208 0.788 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3895 0.914 0.012 0.184 0.804 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.1118 0.873 0.000 0.964 0.036 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0592 0.892 0.000 0.984 0.016 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1356 0.874 0.008 0.960 0.032 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0992 0.885 0.012 0.976 0.008 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1109 0.914 0.968 0.028 0.004 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.6148 0.440 0.084 0.636 0.280 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.3870 0.915 0.004 0.208 0.788 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0469 0.893 0.000 0.988 0.012 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.4082 0.821 0.820 0.008 0.152 0.020
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.4673 0.858 0.076 0.132 0.792 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0592 0.892 0.000 0.984 0.016 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3325 0.791 0.024 0.864 0.112 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.2124 0.909 0.040 0.000 0.028 0.932
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.7458 0.267 0.252 0.508 0.240 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0592 0.892 0.000 0.984 0.016 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0336 0.893 0.000 0.992 0.008 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0469 0.893 0.000 0.988 0.012 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.6908 0.407 0.336 0.036 0.052 0.576
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.7662 -0.226 0.112 0.028 0.492 0.368
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.4647 0.795 0.008 0.288 0.704 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0657 0.892 0.004 0.984 0.012 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2198 0.896 0.008 0.000 0.072 0.920
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.4422 0.589 0.008 0.736 0.256 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1174 0.920 0.020 0.000 0.012 0.968
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1975 0.857 0.016 0.936 0.048 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.3757 0.786 0.828 0.000 0.020 0.152
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.3831 0.916 0.004 0.204 0.792 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.4158 0.650 0.008 0.768 0.224 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.3992 0.911 0.008 0.188 0.800 0.004
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1109 0.914 0.968 0.028 0.004 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.4877 0.596 0.000 0.408 0.592 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.1975 0.857 0.016 0.936 0.048 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.2319 0.909 0.932 0.028 0.024 0.016
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0524 0.891 0.004 0.988 0.008 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.3564 0.843 0.860 0.012 0.112 0.016
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.4852 0.785 0.112 0.076 0.800 0.012
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.3791 0.919 0.004 0.200 0.796 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.3913 0.886 0.000 0.148 0.824 0.028
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1256 0.919 0.028 0.000 0.008 0.964
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.4446 0.807 0.816 0.024 0.024 0.136
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0469 0.893 0.000 0.988 0.012 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0592 0.892 0.000 0.984 0.016 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2124 0.910 0.040 0.000 0.028 0.932
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.1640 0.916 0.012 0.012 0.020 0.956
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.4019 0.919 0.012 0.196 0.792 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0469 0.893 0.000 0.988 0.012 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.4119 0.916 0.004 0.188 0.796 0.012
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3568 0.787 0.024 0.856 0.116 0.004
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.1938 0.915 0.012 0.000 0.052 0.936
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1296 0.915 0.964 0.028 0.004 0.004
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1042 0.920 0.020 0.000 0.008 0.972
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0469 0.893 0.000 0.988 0.012 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.3893 0.919 0.008 0.196 0.796 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.3710 0.915 0.004 0.192 0.804 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.6275 0.615 0.236 0.016 0.076 0.672
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1411 0.919 0.020 0.000 0.020 0.960
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0469 0.893 0.000 0.988 0.012 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0592 0.892 0.000 0.984 0.016 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2781 0.873 0.904 0.008 0.072 0.016
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.2198 0.896 0.008 0.000 0.072 0.920
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.3791 0.919 0.004 0.200 0.796 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.4057 0.890 0.000 0.152 0.816 0.032
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.3791 0.919 0.004 0.200 0.796 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0469 0.893 0.000 0.988 0.012 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.3933 0.916 0.008 0.200 0.792 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.3764 0.905 0.000 0.172 0.816 0.012
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.1042 0.920 0.020 0.000 0.008 0.972
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1943 0.8545 0.000 0.020 0.924 0.000 0.056
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0771 0.8651 0.000 0.020 0.976 0.000 0.004
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0324 0.8102 0.992 0.004 0.004 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.7685 0.1142 0.484 0.168 0.112 0.000 0.236
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.3980 0.7543 0.804 0.004 0.004 0.048 0.140
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.1012 0.8644 0.000 0.020 0.968 0.000 0.012
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0324 0.8102 0.992 0.004 0.004 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.4876 0.3184 0.008 0.012 0.000 0.544 0.436
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.5810 0.5592 0.000 0.604 0.152 0.000 0.244
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0324 0.8102 0.992 0.004 0.004 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0162 0.7493 0.000 0.000 0.000 0.996 0.004
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2445 0.7896 0.884 0.004 0.004 0.000 0.108
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.2390 0.8000 0.000 0.896 0.020 0.000 0.084
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1285 0.8004 0.956 0.004 0.004 0.000 0.036
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.6009 0.5215 0.000 0.580 0.180 0.000 0.240
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.4497 0.5375 0.000 0.000 0.016 0.632 0.352
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1124 0.7988 0.960 0.004 0.000 0.000 0.036
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0324 0.8102 0.992 0.004 0.004 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0898 0.8647 0.000 0.020 0.972 0.000 0.008
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1579 0.8595 0.000 0.024 0.944 0.000 0.032
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.2179 0.7853 0.000 0.896 0.004 0.000 0.100
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.2361 0.8037 0.000 0.892 0.012 0.000 0.096
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.4238 0.7109 0.000 0.756 0.052 0.000 0.192
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1278 0.8198 0.004 0.960 0.016 0.000 0.020
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1731 0.7878 0.932 0.004 0.004 0.000 0.060
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.7588 0.3364 0.068 0.460 0.228 0.000 0.244
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1818 0.8561 0.000 0.024 0.932 0.000 0.044
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0771 0.8216 0.000 0.976 0.020 0.000 0.004
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.4632 0.2981 0.540 0.012 0.000 0.000 0.448
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.4391 0.6401 0.016 0.024 0.744 0.000 0.216
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.2953 0.7762 0.000 0.844 0.012 0.000 0.144
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3858 0.6330 0.008 0.760 0.008 0.000 0.224
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.2516 0.6823 0.000 0.000 0.000 0.860 0.140
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.8342 0.1570 0.232 0.368 0.160 0.000 0.240
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.1670 0.8174 0.000 0.936 0.012 0.000 0.052
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0798 0.8209 0.000 0.976 0.016 0.000 0.008
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.1444 0.8190 0.000 0.948 0.012 0.000 0.040
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.7146 0.0813 0.172 0.020 0.008 0.432 0.368
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 5 0.7608 0.0000 0.076 0.012 0.312 0.124 0.476
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.6362 0.2438 0.000 0.224 0.520 0.000 0.256
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2189 0.7997 0.000 0.904 0.012 0.000 0.084
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.4524 0.5469 0.000 0.000 0.020 0.644 0.336
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.6195 0.4792 0.000 0.552 0.208 0.000 0.240
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0703 0.7472 0.000 0.000 0.000 0.976 0.024
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.2621 0.7505 0.004 0.876 0.008 0.000 0.112
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.5150 0.6335 0.692 0.000 0.000 0.172 0.136
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0898 0.8647 0.000 0.020 0.972 0.000 0.008
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5887 0.5441 0.000 0.596 0.164 0.000 0.240
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4197 0.6250 0.000 0.028 0.728 0.000 0.244
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1591 0.7879 0.940 0.004 0.004 0.000 0.052
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3353 0.5855 0.000 0.196 0.796 0.000 0.008
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.2865 0.7388 0.004 0.856 0.008 0.000 0.132
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3909 0.7567 0.808 0.004 0.004 0.044 0.140
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1059 0.8193 0.004 0.968 0.020 0.000 0.008
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.6003 0.6119 0.680 0.000 0.104 0.072 0.144
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.4805 0.5630 0.036 0.004 0.704 0.008 0.248
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0771 0.8651 0.000 0.020 0.976 0.000 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.1186 0.8447 0.000 0.008 0.964 0.008 0.020
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1041 0.7408 0.004 0.000 0.000 0.964 0.032
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.5595 0.6167 0.668 0.004 0.004 0.184 0.140
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0771 0.8216 0.000 0.976 0.020 0.000 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.1281 0.8203 0.000 0.956 0.012 0.000 0.032
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.3109 0.6286 0.000 0.000 0.000 0.800 0.200
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.1124 0.7458 0.000 0.000 0.004 0.960 0.036
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.1485 0.8605 0.000 0.020 0.948 0.000 0.032
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0671 0.8218 0.000 0.980 0.016 0.000 0.004
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0771 0.8651 0.000 0.020 0.976 0.000 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.4236 0.5227 0.004 0.664 0.004 0.000 0.328
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3551 0.6685 0.000 0.000 0.008 0.772 0.220
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1116 0.8098 0.964 0.004 0.004 0.000 0.028
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0865 0.7476 0.000 0.000 0.004 0.972 0.024
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0771 0.8216 0.000 0.976 0.020 0.000 0.004
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0771 0.8651 0.000 0.020 0.976 0.000 0.004
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.3370 0.7492 0.000 0.028 0.824 0.000 0.148
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.6469 0.4290 0.160 0.008 0.020 0.604 0.208
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0771 0.7475 0.000 0.000 0.004 0.976 0.020
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0771 0.8216 0.000 0.976 0.020 0.000 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0693 0.8219 0.000 0.980 0.012 0.000 0.008
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2773 0.7427 0.836 0.000 0.000 0.000 0.164
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.4524 0.5469 0.000 0.000 0.020 0.644 0.336
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.1117 0.8646 0.000 0.020 0.964 0.000 0.016
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0981 0.8527 0.000 0.012 0.972 0.008 0.008
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0898 0.8649 0.000 0.020 0.972 0.000 0.008
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0771 0.8216 0.000 0.976 0.020 0.000 0.004
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1943 0.8545 0.000 0.020 0.924 0.000 0.056
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0854 0.8549 0.000 0.012 0.976 0.004 0.008
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.7492 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.3140 0.8499 0.000 0.016 0.840 0.000 0.116 0.028
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0260 0.9093 0.000 0.000 0.992 0.000 0.008 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.7888 1.000 0.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.4492 0.3057 0.340 0.004 0.036 0.000 0.620 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.5714 0.6505 0.640 0.024 0.000 0.100 0.024 0.212
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.1552 0.9016 0.000 0.020 0.940 0.000 0.036 0.004
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0260 0.7877 0.992 0.000 0.000 0.000 0.000 0.008
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.6359 -0.1278 0.004 0.308 0.000 0.364 0.004 0.320
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.1982 0.5057 0.000 0.004 0.068 0.000 0.912 0.016
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.7888 1.000 0.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0146 0.6766 0.000 0.000 0.000 0.996 0.004 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3334 0.7489 0.820 0.040 0.000 0.000 0.008 0.132
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4393 0.7136 0.000 0.500 0.016 0.000 0.480 0.004
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1633 0.7669 0.932 0.024 0.000 0.000 0.000 0.044
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.1701 0.5074 0.000 0.008 0.072 0.000 0.920 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 6 0.4070 0.3268 0.000 0.000 0.004 0.424 0.004 0.568
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1633 0.7669 0.932 0.024 0.000 0.000 0.000 0.044
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0260 0.7877 0.992 0.000 0.000 0.000 0.000 0.008
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0260 0.9093 0.000 0.000 0.992 0.000 0.008 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.2311 0.8682 0.000 0.000 0.880 0.000 0.104 0.016
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.4266 0.7901 0.000 0.620 0.004 0.000 0.356 0.020
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.4828 -0.5781 0.000 0.384 0.016 0.000 0.568 0.032
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.3345 0.1650 0.000 0.184 0.028 0.000 0.788 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.4450 0.8505 0.000 0.592 0.016 0.000 0.380 0.012
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1075 0.7745 0.952 0.000 0.000 0.000 0.048 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.2712 0.5396 0.048 0.000 0.088 0.000 0.864 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.2340 0.8379 0.000 0.000 0.852 0.000 0.148 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.4275 0.8532 0.000 0.592 0.016 0.000 0.388 0.004
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 6 0.6634 0.0486 0.272 0.352 0.004 0.008 0.008 0.356
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.4315 -0.1161 0.012 0.000 0.488 0.000 0.496 0.004
#> 36EDD202-A845-4CE7-95D5-A515C471262E 5 0.4076 -0.1513 0.000 0.236 0.016 0.000 0.724 0.024
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3327 0.4480 0.000 0.820 0.000 0.000 0.092 0.088
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.4526 0.5353 0.008 0.040 0.000 0.720 0.020 0.212
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.3884 0.5233 0.184 0.004 0.052 0.000 0.760 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.4789 0.7937 0.000 0.512 0.016 0.000 0.448 0.024
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.4099 0.8523 0.000 0.612 0.016 0.000 0.372 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.4770 0.8150 0.000 0.532 0.016 0.000 0.428 0.024
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 5 0.7112 -0.0278 0.088 0.016 0.000 0.256 0.480 0.160
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 6 0.7685 0.2480 0.044 0.332 0.220 0.036 0.012 0.356
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.3971 0.4543 0.000 0.004 0.268 0.000 0.704 0.024
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 5 0.4378 -0.6830 0.000 0.452 0.016 0.000 0.528 0.004
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 6 0.3971 0.3127 0.000 0.000 0.000 0.448 0.004 0.548
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.1556 0.5139 0.000 0.000 0.080 0.000 0.920 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.2162 0.6604 0.000 0.004 0.000 0.896 0.012 0.088
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.3788 0.7734 0.000 0.704 0.004 0.000 0.280 0.012
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.6514 0.5706 0.556 0.036 0.000 0.172 0.024 0.212
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0260 0.9093 0.000 0.000 0.992 0.000 0.008 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.1719 0.4928 0.000 0.016 0.060 0.000 0.924 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.4253 -0.0403 0.000 0.000 0.460 0.000 0.524 0.016
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1075 0.7745 0.952 0.000 0.000 0.000 0.048 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3457 0.6920 0.000 0.136 0.808 0.000 0.052 0.004
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4204 0.7020 0.000 0.696 0.004 0.000 0.260 0.040
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5778 0.6507 0.640 0.040 0.000 0.088 0.020 0.212
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.4348 0.8515 0.000 0.600 0.016 0.000 0.376 0.008
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.7818 0.5177 0.508 0.040 0.064 0.108 0.068 0.212
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 5 0.5897 0.0213 0.040 0.000 0.400 0.000 0.476 0.084
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0260 0.9093 0.000 0.000 0.992 0.000 0.008 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0665 0.9017 0.000 0.004 0.980 0.000 0.008 0.008
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2494 0.6471 0.000 0.000 0.000 0.864 0.016 0.120
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.6673 0.5460 0.536 0.040 0.000 0.188 0.024 0.212
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.4265 0.8535 0.000 0.596 0.016 0.000 0.384 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.4699 0.8210 0.000 0.536 0.016 0.000 0.428 0.020
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.4406 0.5377 0.000 0.004 0.000 0.728 0.116 0.152
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.1429 0.6531 0.000 0.004 0.000 0.940 0.004 0.052
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.2389 0.8549 0.000 0.000 0.864 0.000 0.128 0.008
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.4275 0.8532 0.000 0.592 0.016 0.000 0.388 0.004
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0405 0.9070 0.000 0.000 0.988 0.000 0.008 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.4910 0.3167 0.000 0.668 0.004 0.000 0.136 0.192
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.5442 0.3028 0.000 0.152 0.000 0.612 0.012 0.224
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1594 0.7850 0.932 0.000 0.000 0.000 0.016 0.052
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1196 0.6596 0.000 0.000 0.000 0.952 0.008 0.040
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.4275 0.8532 0.000 0.592 0.016 0.000 0.388 0.004
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0291 0.9084 0.000 0.000 0.992 0.000 0.004 0.004
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4012 0.4787 0.000 0.000 0.640 0.000 0.344 0.016
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.7545 0.1846 0.044 0.084 0.020 0.504 0.108 0.240
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1219 0.6537 0.000 0.000 0.000 0.948 0.004 0.048
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.4275 0.8532 0.000 0.592 0.016 0.000 0.388 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.4387 0.8525 0.000 0.584 0.016 0.000 0.392 0.008
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.4689 0.5909 0.696 0.172 0.000 0.000 0.004 0.128
#> AF8AB83D-2917-4752-8C38-CF84C565B565 6 0.3966 0.3182 0.000 0.000 0.000 0.444 0.004 0.552
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.2024 0.8955 0.000 0.016 0.920 0.000 0.036 0.028
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0622 0.9059 0.000 0.000 0.980 0.000 0.012 0.008
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0993 0.9066 0.000 0.000 0.964 0.000 0.024 0.012
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.4275 0.8532 0.000 0.592 0.016 0.000 0.388 0.004
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.3140 0.8499 0.000 0.016 0.840 0.000 0.116 0.028
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0363 0.9056 0.000 0.000 0.988 0.000 0.012 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0260 0.6738 0.000 0.000 0.000 0.992 0.000 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17471 rows and 87 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.906 0.946 0.975 0.4958 0.509 0.509
#> 3 3 0.732 0.848 0.913 0.3523 0.775 0.577
#> 4 4 0.893 0.876 0.949 0.1219 0.878 0.652
#> 5 5 0.817 0.781 0.883 0.0651 0.936 0.753
#> 6 6 0.748 0.643 0.798 0.0365 0.968 0.849
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0000 0.963 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0376 0.961 0.004 0.996
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0376 0.987 0.996 0.004
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.8661 0.620 0.288 0.712
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.989 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0000 0.963 0.000 1.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0376 0.987 0.996 0.004
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0000 0.989 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.963 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0376 0.987 0.996 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.989 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.989 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.963 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.4815 0.879 0.896 0.104
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.963 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0000 0.989 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0376 0.987 0.996 0.004
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0376 0.987 0.996 0.004
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0376 0.961 0.004 0.996
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.7056 0.775 0.192 0.808
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.963 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.963 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0000 0.963 0.000 1.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.963 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0376 0.987 0.996 0.004
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0000 0.963 0.000 1.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0000 0.963 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.963 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.989 1.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.1843 0.963 0.972 0.028
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.963 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2948 0.923 0.052 0.948
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.989 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.7139 0.758 0.196 0.804
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.963 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.963 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.963 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.989 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.989 1.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0000 0.963 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.963 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.0000 0.989 1.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.963 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.989 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.963 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.989 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0376 0.961 0.004 0.996
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.963 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.6048 0.832 0.148 0.852
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0376 0.987 0.996 0.004
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.963 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.963 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.989 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.963 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.989 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0376 0.986 0.996 0.004
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0376 0.961 0.004 0.996
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.9661 0.398 0.392 0.608
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.989 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.989 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.963 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.963 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0000 0.989 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 1 0.7219 0.736 0.800 0.200
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.1843 0.943 0.028 0.972
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.963 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.5408 0.856 0.124 0.876
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0938 0.957 0.012 0.988
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0000 0.989 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0376 0.987 0.996 0.004
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.989 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.963 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.1414 0.951 0.020 0.980
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0376 0.961 0.004 0.996
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.989 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0000 0.989 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.963 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.963 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.989 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.989 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0376 0.961 0.004 0.996
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.9129 0.547 0.328 0.672
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0376 0.961 0.004 0.996
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.963 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.963 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0376 0.961 0.004 0.996
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0000 0.989 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.5637 0.777 0.172 0.040 0.788
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1129 0.856 0.020 0.004 0.976
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.871 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.6322 0.648 0.276 0.700 0.024
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.871 1.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.6719 0.758 0.160 0.096 0.744
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.871 1.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.4750 0.815 0.784 0.000 0.216
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0424 0.959 0.000 0.992 0.008
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.871 1.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.4605 0.822 0.796 0.000 0.204
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.871 1.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.964 0.000 1.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.871 1.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0747 0.953 0.000 0.984 0.016
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.5216 0.492 0.260 0.000 0.740
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.871 1.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.871 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.2200 0.848 0.004 0.056 0.940
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0747 0.855 0.016 0.000 0.984
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.964 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.964 0.000 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0000 0.964 0.000 1.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0237 0.961 0.000 0.996 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.871 1.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.5778 0.734 0.200 0.768 0.032
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.5254 0.668 0.000 0.264 0.736
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.964 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.4346 0.832 0.816 0.000 0.184
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.4702 0.757 0.212 0.000 0.788
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.964 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.964 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.4121 0.837 0.832 0.000 0.168
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.5774 0.710 0.232 0.748 0.020
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.964 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.964 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.964 0.000 1.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.1163 0.868 0.972 0.000 0.028
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.5988 0.626 0.632 0.000 0.368
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.5058 0.695 0.000 0.244 0.756
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.964 0.000 1.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.5497 0.412 0.292 0.000 0.708
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.1031 0.946 0.000 0.976 0.024
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.4887 0.808 0.772 0.000 0.228
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.964 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0424 0.871 0.992 0.000 0.008
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.1950 0.853 0.008 0.040 0.952
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0747 0.953 0.000 0.984 0.016
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.0000 0.853 0.000 0.000 1.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.871 1.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.5706 0.586 0.000 0.320 0.680
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.964 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.871 1.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.964 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.871 1.000 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.5178 0.716 0.256 0.000 0.744
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0592 0.855 0.000 0.012 0.988
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.853 0.000 0.000 1.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.2537 0.860 0.920 0.000 0.080
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.871 1.000 0.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.964 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.964 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.4452 0.827 0.808 0.000 0.192
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 1 0.9111 0.525 0.532 0.176 0.292
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.4504 0.768 0.196 0.000 0.804
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.964 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.853 0.000 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3412 0.833 0.000 0.876 0.124
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.5216 0.780 0.740 0.000 0.260
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.871 1.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.5216 0.780 0.740 0.000 0.260
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.964 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0892 0.855 0.020 0.000 0.980
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0000 0.853 0.000 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.4796 0.813 0.780 0.000 0.220
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.4931 0.805 0.768 0.000 0.232
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.964 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.964 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.871 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.5291 0.773 0.732 0.000 0.268
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.1753 0.851 0.000 0.048 0.952
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0000 0.853 0.000 0.000 1.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0592 0.855 0.000 0.012 0.988
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.964 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.6518 0.762 0.168 0.080 0.752
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.853 0.000 0.000 1.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.4887 0.808 0.772 0.000 0.228
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0707 0.904 0.980 0.000 0.000 0.020
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0469 0.938 0.012 0.000 0.988 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0188 0.958 0.000 0.996 0.004 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.4454 0.551 0.308 0.692 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.5467 0.391 0.364 0.612 0.024 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.5220 0.264 0.568 0.000 0.008 0.424
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.4643 0.492 0.344 0.000 0.656 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0188 0.957 0.004 0.996 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.2593 0.815 0.892 0.104 0.004 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.2216 0.855 0.092 0.000 0.000 0.908
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.3760 0.798 0.028 0.000 0.136 0.836
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.2831 0.831 0.000 0.120 0.876 0.004
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0336 0.954 0.000 0.992 0.008 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4977 0.218 0.540 0.000 0.000 0.460
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0188 0.958 0.000 0.996 0.004 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.4925 0.235 0.000 0.000 0.428 0.572
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3400 0.765 0.000 0.180 0.820 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0817 0.902 0.976 0.000 0.000 0.024
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.2053 0.865 0.924 0.000 0.004 0.072
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.5040 0.432 0.364 0.000 0.628 0.008
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0469 0.938 0.000 0.000 0.988 0.012
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.4643 0.501 0.656 0.000 0.000 0.344
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0188 0.942 0.000 0.000 0.996 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.4500 0.530 0.000 0.684 0.000 0.316
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0469 0.938 0.000 0.000 0.988 0.012
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.3444 0.738 0.184 0.000 0.000 0.816
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.914 1.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0707 0.933 0.000 0.000 0.980 0.020
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.960 0.000 1.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.944 0.000 0.000 1.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.942 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1408 0.8831 0.008 0.000 0.948 0.000 0.044
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0290 0.8961 0.000 0.000 0.992 0.000 0.008
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1270 0.9033 0.948 0.000 0.000 0.000 0.052
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.3662 0.5711 0.252 0.004 0.000 0.000 0.744
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.1403 0.8900 0.952 0.000 0.000 0.024 0.024
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.2529 0.8506 0.036 0.032 0.908 0.000 0.024
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.1270 0.9033 0.948 0.000 0.000 0.000 0.052
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.2407 0.8481 0.012 0.004 0.000 0.896 0.088
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.4422 0.5702 0.000 0.300 0.004 0.016 0.680
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.1270 0.9033 0.948 0.000 0.000 0.000 0.052
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0162 0.9076 0.000 0.000 0.000 0.996 0.004
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0404 0.8966 0.988 0.000 0.000 0.000 0.012
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.1430 0.8561 0.004 0.944 0.000 0.000 0.052
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1121 0.9036 0.956 0.000 0.000 0.000 0.044
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.3231 0.7465 0.000 0.196 0.004 0.000 0.800
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.0671 0.9065 0.000 0.000 0.004 0.980 0.016
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1197 0.9031 0.952 0.000 0.000 0.000 0.048
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1270 0.9033 0.948 0.000 0.000 0.000 0.052
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0290 0.8961 0.000 0.000 0.992 0.000 0.008
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0963 0.8872 0.000 0.000 0.964 0.000 0.036
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.2230 0.8306 0.000 0.884 0.000 0.000 0.116
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4045 0.4285 0.000 0.644 0.000 0.000 0.356
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5542 -0.0343 0.068 0.500 0.000 0.000 0.432
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0703 0.8659 0.000 0.976 0.000 0.000 0.024
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1792 0.8878 0.916 0.000 0.000 0.000 0.084
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.3161 0.7717 0.032 0.100 0.008 0.000 0.860
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.3757 0.6997 0.000 0.020 0.772 0.000 0.208
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0510 0.8678 0.000 0.984 0.000 0.000 0.016
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.5286 0.6702 0.724 0.004 0.020 0.152 0.100
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.6349 0.2034 0.168 0.000 0.360 0.000 0.472
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4297 0.0703 0.000 0.528 0.000 0.000 0.472
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2905 0.8007 0.036 0.868 0.000 0.000 0.096
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.1364 0.8868 0.036 0.000 0.000 0.952 0.012
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.3267 0.7366 0.112 0.044 0.000 0.000 0.844
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.2230 0.8174 0.000 0.884 0.000 0.000 0.116
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0404 0.8675 0.000 0.988 0.000 0.000 0.012
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2127 0.8417 0.000 0.892 0.000 0.000 0.108
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.4801 0.5240 0.048 0.000 0.000 0.668 0.284
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.8119 0.2929 0.204 0.008 0.260 0.428 0.100
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.3704 0.7578 0.000 0.088 0.092 0.000 0.820
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2813 0.7408 0.000 0.832 0.000 0.000 0.168
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0671 0.9065 0.000 0.000 0.004 0.980 0.016
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.3209 0.7399 0.000 0.180 0.008 0.000 0.812
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0162 0.9076 0.000 0.000 0.000 0.996 0.004
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1732 0.8347 0.000 0.920 0.000 0.000 0.080
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4086 0.6085 0.704 0.000 0.000 0.284 0.012
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.8956 0.000 0.000 1.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.3300 0.7411 0.004 0.204 0.000 0.000 0.792
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.5572 0.4940 0.000 0.000 0.124 0.248 0.628
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1908 0.8810 0.908 0.000 0.000 0.000 0.092
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3727 0.6494 0.000 0.216 0.768 0.000 0.016
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.2020 0.8319 0.000 0.900 0.000 0.000 0.100
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0912 0.8904 0.972 0.000 0.000 0.012 0.016
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0290 0.8673 0.000 0.992 0.000 0.000 0.008
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.2196 0.8632 0.916 0.000 0.004 0.056 0.024
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.7300 -0.0998 0.264 0.000 0.380 0.024 0.332
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0162 0.8959 0.000 0.000 0.996 0.000 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0451 0.8930 0.000 0.000 0.988 0.008 0.004
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0566 0.9042 0.004 0.000 0.000 0.984 0.012
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.4040 0.6493 0.724 0.000 0.000 0.260 0.016
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0510 0.8678 0.000 0.984 0.000 0.000 0.016
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.1478 0.8530 0.000 0.936 0.000 0.000 0.064
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0566 0.9039 0.004 0.000 0.000 0.984 0.012
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.0290 0.9082 0.000 0.000 0.000 0.992 0.008
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.1331 0.8852 0.008 0.000 0.952 0.000 0.040
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0510 0.8678 0.000 0.984 0.000 0.000 0.016
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0162 0.8951 0.000 0.000 0.996 0.004 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.4403 0.7271 0.000 0.772 0.004 0.092 0.132
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0566 0.9075 0.000 0.000 0.004 0.984 0.012
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1270 0.9037 0.948 0.000 0.000 0.000 0.052
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0290 0.9082 0.000 0.000 0.000 0.992 0.008
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0510 0.8678 0.000 0.984 0.000 0.000 0.016
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.8956 0.000 0.000 1.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4276 0.3717 0.000 0.000 0.616 0.004 0.380
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.4558 0.6198 0.252 0.000 0.004 0.708 0.036
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0290 0.9082 0.000 0.000 0.000 0.992 0.008
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0510 0.8678 0.000 0.984 0.000 0.000 0.016
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0963 0.8637 0.000 0.964 0.000 0.000 0.036
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0290 0.8968 0.992 0.000 0.000 0.000 0.008
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0671 0.9065 0.000 0.000 0.004 0.980 0.016
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0290 0.8961 0.000 0.000 0.992 0.000 0.008
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.1195 0.8793 0.000 0.000 0.960 0.028 0.012
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0404 0.8960 0.000 0.000 0.988 0.000 0.012
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0510 0.8678 0.000 0.984 0.000 0.000 0.016
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1956 0.8645 0.008 0.000 0.916 0.000 0.076
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.8956 0.000 0.000 1.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.9081 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.2635 0.8603 0.004 0.004 0.880 0.000 0.036 0.076
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0260 0.8789 0.000 0.000 0.992 0.000 0.000 0.008
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0146 0.7695 0.996 0.000 0.000 0.000 0.004 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.3984 0.4338 0.320 0.008 0.000 0.000 0.664 0.008
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.5697 0.6010 0.608 0.000 0.000 0.080 0.060 0.252
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.4224 0.7242 0.008 0.032 0.744 0.000 0.016 0.200
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0909 0.7631 0.968 0.000 0.000 0.000 0.012 0.020
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.4663 0.2860 0.036 0.000 0.000 0.552 0.004 0.408
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.5667 0.5224 0.000 0.188 0.016 0.012 0.628 0.156
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0291 0.7699 0.992 0.000 0.000 0.000 0.004 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0632 0.8038 0.000 0.000 0.000 0.976 0.000 0.024
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3440 0.7072 0.776 0.000 0.000 0.000 0.028 0.196
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.3412 0.6828 0.000 0.808 0.000 0.000 0.064 0.128
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1367 0.7522 0.944 0.000 0.000 0.000 0.012 0.044
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.2558 0.6368 0.000 0.156 0.000 0.000 0.840 0.004
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.2278 0.7791 0.000 0.000 0.000 0.868 0.004 0.128
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1265 0.7537 0.948 0.000 0.000 0.000 0.008 0.044
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0622 0.7660 0.980 0.000 0.000 0.000 0.012 0.008
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0458 0.8786 0.000 0.000 0.984 0.000 0.000 0.016
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.2448 0.8527 0.000 0.000 0.884 0.000 0.064 0.052
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.4630 0.4531 0.000 0.580 0.000 0.000 0.048 0.372
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.5289 0.3643 0.000 0.560 0.000 0.000 0.316 0.124
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.6043 -0.1190 0.132 0.452 0.000 0.000 0.392 0.024
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2266 0.7516 0.000 0.880 0.000 0.000 0.012 0.108
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0865 0.7608 0.964 0.000 0.000 0.000 0.036 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.2434 0.6466 0.036 0.064 0.000 0.000 0.892 0.008
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.3804 0.6868 0.000 0.020 0.748 0.000 0.220 0.012
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0146 0.7749 0.000 0.996 0.000 0.000 0.000 0.004
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 6 0.5340 0.0672 0.352 0.000 0.012 0.072 0.004 0.560
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.7007 0.2409 0.196 0.000 0.308 0.000 0.412 0.084
#> 36EDD202-A845-4CE7-95D5-A515C471262E 5 0.5421 -0.0672 0.000 0.432 0.000 0.000 0.452 0.116
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.4127 0.3701 0.012 0.620 0.000 0.000 0.004 0.364
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.4719 0.5435 0.016 0.000 0.000 0.676 0.060 0.248
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.3593 0.5927 0.164 0.044 0.000 0.000 0.788 0.004
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.4513 0.6233 0.000 0.704 0.000 0.000 0.172 0.124
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0806 0.7749 0.000 0.972 0.000 0.000 0.008 0.020
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.4566 0.6466 0.000 0.700 0.000 0.000 0.140 0.160
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.6349 0.3459 0.040 0.000 0.000 0.516 0.236 0.208
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 6 0.6584 0.2809 0.092 0.000 0.160 0.192 0.004 0.552
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.3513 0.6379 0.000 0.052 0.048 0.004 0.840 0.056
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2613 0.7003 0.000 0.848 0.000 0.000 0.140 0.012
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2146 0.7837 0.000 0.000 0.000 0.880 0.004 0.116
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.3426 0.6368 0.004 0.116 0.000 0.000 0.816 0.064
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1074 0.8015 0.000 0.000 0.000 0.960 0.012 0.028
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.3189 0.6175 0.000 0.760 0.000 0.000 0.004 0.236
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.6377 0.4705 0.532 0.000 0.000 0.212 0.052 0.204
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0458 0.8786 0.000 0.000 0.984 0.000 0.000 0.016
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.2624 0.6413 0.004 0.148 0.000 0.000 0.844 0.004
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.6950 0.3419 0.000 0.004 0.108 0.192 0.500 0.196
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1349 0.7459 0.940 0.000 0.000 0.000 0.056 0.004
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3934 0.5251 0.000 0.304 0.676 0.000 0.000 0.020
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4167 0.4896 0.000 0.632 0.000 0.000 0.024 0.344
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5651 0.5888 0.592 0.000 0.000 0.064 0.060 0.284
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1471 0.7663 0.000 0.932 0.000 0.000 0.004 0.064
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.6209 0.5494 0.548 0.000 0.004 0.092 0.068 0.288
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 5 0.8359 0.0394 0.188 0.000 0.152 0.064 0.308 0.288
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.8788 0.000 0.000 1.000 0.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.2085 0.8530 0.000 0.000 0.912 0.024 0.008 0.056
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2215 0.7735 0.012 0.000 0.000 0.900 0.012 0.076
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.6595 0.4617 0.488 0.000 0.000 0.180 0.060 0.272
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0260 0.7741 0.000 0.992 0.000 0.000 0.000 0.008
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3745 0.6953 0.000 0.784 0.000 0.000 0.100 0.116
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2527 0.7586 0.004 0.000 0.000 0.880 0.032 0.084
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.1411 0.8020 0.000 0.004 0.000 0.936 0.000 0.060
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.3063 0.8472 0.024 0.000 0.860 0.000 0.052 0.064
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0291 0.7748 0.000 0.992 0.000 0.000 0.004 0.004
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0622 0.8787 0.000 0.000 0.980 0.000 0.012 0.008
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 6 0.5443 -0.2767 0.000 0.416 0.000 0.052 0.032 0.500
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.2558 0.7681 0.000 0.000 0.000 0.840 0.004 0.156
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.2412 0.7501 0.880 0.000 0.000 0.000 0.028 0.092
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0458 0.8095 0.000 0.000 0.000 0.984 0.000 0.016
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0146 0.7749 0.000 0.996 0.000 0.000 0.000 0.004
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0405 0.8796 0.000 0.000 0.988 0.000 0.004 0.008
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.5608 0.2914 0.000 0.000 0.536 0.020 0.348 0.096
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.5663 0.4965 0.196 0.000 0.004 0.616 0.020 0.164
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1007 0.8052 0.000 0.000 0.000 0.956 0.000 0.044
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0146 0.7749 0.000 0.996 0.000 0.000 0.000 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.1572 0.7689 0.000 0.936 0.000 0.000 0.028 0.036
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2669 0.7205 0.836 0.000 0.000 0.000 0.008 0.156
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.2234 0.7813 0.000 0.000 0.000 0.872 0.004 0.124
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.1787 0.8761 0.000 0.004 0.920 0.000 0.008 0.068
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.2585 0.8407 0.000 0.000 0.888 0.048 0.016 0.048
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0790 0.8781 0.000 0.000 0.968 0.000 0.000 0.032
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0146 0.7749 0.000 0.996 0.000 0.000 0.000 0.004
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.3303 0.8340 0.004 0.004 0.836 0.000 0.080 0.076
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0922 0.8773 0.000 0.000 0.968 0.004 0.004 0.024
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0146 0.8073 0.000 0.000 0.000 0.996 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["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 17471 rows and 87 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 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 0.506 0.874 0.921 0.4811 0.495 0.495
#> 3 3 0.419 0.533 0.777 0.3652 0.752 0.537
#> 4 4 0.524 0.504 0.753 0.1120 0.782 0.453
#> 5 5 0.644 0.635 0.781 0.0582 0.872 0.558
#> 6 6 0.741 0.761 0.866 0.0380 0.957 0.804
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 1 0.9580 0.321 0.620 0.380
#> F569915C-8F77-4D67-9730-30824DB57EE5 1 0.4690 0.888 0.900 0.100
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.943 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.9044 0.674 0.320 0.680
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.943 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.7376 0.753 0.792 0.208
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0938 0.942 0.988 0.012
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0672 0.942 0.992 0.008
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.6438 0.868 0.164 0.836
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.943 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.3879 0.903 0.924 0.076
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.943 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0672 0.872 0.008 0.992
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.4431 0.895 0.908 0.092
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.5408 0.878 0.124 0.876
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0938 0.942 0.988 0.012
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1633 0.939 0.976 0.024
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0376 0.943 0.996 0.004
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.6148 0.873 0.152 0.848
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.3274 0.923 0.940 0.060
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.6247 0.872 0.156 0.844
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.6343 0.870 0.160 0.840
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1184 0.878 0.016 0.984
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.875 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1184 0.941 0.984 0.016
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.6712 0.861 0.176 0.824
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.6343 0.870 0.160 0.840
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.875 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.943 1.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.1843 0.937 0.972 0.028
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.6343 0.870 0.160 0.840
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2423 0.857 0.040 0.960
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.943 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.7453 0.749 0.788 0.212
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0672 0.877 0.008 0.992
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.875 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2603 0.880 0.044 0.956
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0938 0.941 0.988 0.012
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3733 0.911 0.928 0.072
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.6531 0.866 0.168 0.832
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.6343 0.870 0.160 0.840
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.0000 0.943 1.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.6343 0.870 0.160 0.840
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.943 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.875 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.943 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.9850 0.267 0.428 0.572
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.4815 0.879 0.104 0.896
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.6801 0.800 0.820 0.180
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.3274 0.922 0.940 0.060
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.875 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.875 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.943 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.875 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.943 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.943 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.7815 0.801 0.232 0.768
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 1 0.5629 0.863 0.868 0.132
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0376 0.942 0.996 0.004
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.943 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.875 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.875 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.4022 0.899 0.920 0.080
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.8267 0.786 0.260 0.740
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.4690 0.888 0.900 0.100
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.875 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.9000 0.693 0.316 0.684
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.6343 0.870 0.160 0.840
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0000 0.943 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.943 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.2423 0.928 0.960 0.040
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.875 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 1 0.5059 0.880 0.888 0.112
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.6712 0.861 0.176 0.824
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0938 0.942 0.988 0.012
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.2423 0.928 0.960 0.040
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.875 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.875 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.943 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.943 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.7299 0.833 0.204 0.796
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 1 0.5946 0.843 0.856 0.144
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 1 0.2043 0.935 0.968 0.032
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.875 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.6247 0.872 0.156 0.844
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.6343 0.870 0.160 0.840
#> 0D907A63-D23F-4684-80D9-0BA552435B86 2 0.8267 0.786 0.260 0.740
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.6920 0.4233 0.164 0.104 0.732
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.3695 0.5256 0.108 0.012 0.880
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.3192 0.6564 0.888 0.000 0.112
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.8659 -0.0836 0.104 0.408 0.488
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.2796 0.6385 0.908 0.000 0.092
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.6567 0.5613 0.752 0.160 0.088
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.6111 0.4406 0.604 0.000 0.396
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.5178 0.5380 0.744 0.000 0.256
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.5733 0.5471 0.000 0.676 0.324
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.3752 0.6417 0.856 0.000 0.144
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.6244 0.1955 0.440 0.000 0.560
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0592 0.6672 0.988 0.000 0.012
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4316 0.6866 0.044 0.868 0.088
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.6813 0.3190 0.520 0.012 0.468
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.5650 0.5932 0.000 0.688 0.312
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.5905 0.4288 0.648 0.000 0.352
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.6669 0.3265 0.524 0.008 0.468
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.4452 0.6218 0.808 0.000 0.192
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.5465 0.4734 0.000 0.288 0.712
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0983 0.5507 0.004 0.016 0.980
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.5560 0.5826 0.000 0.700 0.300
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.5706 0.5525 0.000 0.680 0.320
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.3941 0.7083 0.000 0.844 0.156
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.8111 0.000 1.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.6516 0.3210 0.516 0.004 0.480
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.6286 0.3429 0.000 0.536 0.464
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.6026 0.4777 0.000 0.624 0.376
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8111 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0592 0.6672 0.988 0.000 0.012
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.6813 0.3190 0.520 0.012 0.468
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.5591 0.5774 0.000 0.696 0.304
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0237 0.8083 0.004 0.996 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.2796 0.6385 0.908 0.000 0.092
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.7841 -0.2774 0.472 0.052 0.476
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0424 0.8090 0.000 0.992 0.008
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.8111 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2066 0.7888 0.000 0.940 0.060
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.3551 0.6326 0.868 0.000 0.132
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.6967 0.4564 0.668 0.044 0.288
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.5291 0.3885 0.000 0.268 0.732
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.4654 0.6858 0.000 0.792 0.208
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.5785 0.3194 0.332 0.000 0.668
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.6291 0.3339 0.000 0.532 0.468
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.6280 0.1700 0.460 0.000 0.540
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.8111 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.2796 0.6385 0.908 0.000 0.092
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.8228 -0.0408 0.076 0.512 0.412
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5138 0.6547 0.000 0.748 0.252
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4136 0.5163 0.116 0.020 0.864
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.6680 0.3090 0.508 0.008 0.484
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.8111 0.000 1.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.8111 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.2796 0.6385 0.908 0.000 0.092
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.8111 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0592 0.6672 0.988 0.000 0.012
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.5327 0.6214 0.728 0.000 0.272
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.5058 0.5156 0.000 0.244 0.756
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.6634 0.5710 0.104 0.144 0.752
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.2959 0.6366 0.900 0.000 0.100
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.2796 0.6385 0.908 0.000 0.092
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.8111 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.8111 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.5706 0.3798 0.680 0.000 0.320
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.9520 0.3542 0.352 0.196 0.452
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.6819 0.3061 0.512 0.012 0.476
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.8111 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.7252 0.5664 0.100 0.196 0.704
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.4452 0.6986 0.000 0.808 0.192
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.5327 0.4486 0.728 0.000 0.272
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.2066 0.6681 0.940 0.000 0.060
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.6274 0.1772 0.456 0.000 0.544
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8111 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.3690 0.5310 0.100 0.016 0.884
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.3267 0.5823 0.000 0.116 0.884
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.3784 0.6392 0.864 0.004 0.132
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.6267 0.1832 0.452 0.000 0.548
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8111 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.8111 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0592 0.6672 0.988 0.000 0.012
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.6204 0.3269 0.576 0.000 0.424
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.5178 0.5010 0.000 0.256 0.744
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.6486 0.5659 0.144 0.096 0.760
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.6813 -0.2516 0.468 0.012 0.520
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8111 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.3482 0.5809 0.000 0.128 0.872
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.5621 0.4423 0.000 0.308 0.692
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.6244 0.1955 0.440 0.000 0.560
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 1 0.5296 -0.15749 0.500 0.008 0.492 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.4888 0.57084 0.412 0.000 0.588 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.4888 -0.10298 0.588 0.000 0.000 0.412
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.7377 0.31696 0.608 0.188 0.176 0.028
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.0000 0.74846 0.000 0.000 0.000 1.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.8729 0.02872 0.468 0.152 0.084 0.296
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.2647 0.44387 0.880 0.000 0.000 0.120
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.4866 -0.03311 0.596 0.000 0.000 0.404
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.6970 0.36645 0.256 0.576 0.168 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.4543 0.12336 0.676 0.000 0.000 0.324
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2861 0.72717 0.096 0.000 0.016 0.888
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 4 0.4790 0.46861 0.380 0.000 0.000 0.620
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4643 0.38277 0.000 0.656 0.000 0.344
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1792 0.45277 0.932 0.000 0.000 0.068
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 1 0.6727 0.12638 0.520 0.384 0.096 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.6421 -0.01988 0.368 0.000 0.556 0.076
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1792 0.45277 0.932 0.000 0.000 0.068
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.4328 0.22106 0.748 0.000 0.008 0.244
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.5661 0.65171 0.220 0.080 0.700 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.4134 0.66151 0.260 0.000 0.740 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.6784 0.41266 0.244 0.600 0.156 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.5935 0.50133 0.256 0.664 0.080 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5085 0.34151 0.376 0.616 0.008 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.3539 0.44012 0.820 0.000 0.176 0.004
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.6714 0.30849 0.616 0.208 0.176 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.7302 0.19866 0.332 0.500 0.168 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 4 0.4804 0.46430 0.384 0.000 0.000 0.616
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.2892 0.45350 0.896 0.000 0.036 0.068
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.6731 0.41935 0.248 0.604 0.148 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0000 0.74846 0.000 0.000 0.000 1.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.3356 0.43889 0.824 0.000 0.176 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0336 0.81273 0.008 0.992 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3667 0.72977 0.056 0.856 0.088 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.2345 0.72859 0.100 0.000 0.000 0.900
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.6610 -0.00157 0.604 0.004 0.292 0.100
#> FF7021BB-414B-4466-BC43-372D856E6A1D 1 0.5527 0.18318 0.616 0.028 0.356 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.4214 0.64866 0.204 0.780 0.016 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.3356 0.38217 0.000 0.000 0.824 0.176
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 1 0.6745 0.30644 0.612 0.212 0.176 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.3071 0.72387 0.044 0.000 0.068 0.888
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0000 0.74846 0.000 0.000 0.000 1.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.7159 0.38576 0.200 0.244 0.556 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5292 0.14181 0.480 0.512 0.008 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.4941 0.02802 0.564 0.000 0.436 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.3356 0.43889 0.824 0.000 0.176 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.0000 0.74846 0.000 0.000 0.000 1.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.4643 0.50576 0.344 0.000 0.000 0.656
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.5244 0.27563 0.436 0.000 0.008 0.556
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.4422 0.66288 0.256 0.008 0.736 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.4830 0.59374 0.392 0.000 0.608 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1716 0.73364 0.064 0.000 0.000 0.936
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.74846 0.000 0.000 0.000 1.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2530 0.72320 0.112 0.000 0.000 0.888
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.8537 0.19270 0.044 0.204 0.448 0.304
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.5825 0.33498 0.664 0.000 0.268 0.068
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.5374 0.66124 0.224 0.008 0.724 0.044
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3649 0.66305 0.204 0.796 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.1724 0.74540 0.032 0.000 0.020 0.948
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 4 0.4746 0.46523 0.368 0.000 0.000 0.632
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.5736 0.39374 0.044 0.000 0.328 0.628
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.4382 0.66288 0.296 0.000 0.704 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4605 0.55239 0.336 0.000 0.664 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.5165 0.28041 0.484 0.000 0.004 0.512
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.4800 0.60979 0.044 0.000 0.196 0.760
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 4 0.4804 0.46430 0.384 0.000 0.000 0.616
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.4699 0.54737 0.004 0.000 0.320 0.676
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.4422 0.66288 0.256 0.008 0.736 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.4164 0.66215 0.264 0.000 0.736 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 1 0.6262 -0.25134 0.540 0.000 0.400 0.060
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.81648 0.000 1.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 1 0.4978 0.14771 0.612 0.004 0.384 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.6050 0.63497 0.232 0.100 0.668 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3071 0.72387 0.044 0.000 0.068 0.888
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.4270 -0.09009 0.004 0.004 0.656 0.000 0.336
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1965 0.66814 0.096 0.000 0.904 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.3866 0.68237 0.808 0.000 0.000 0.096 0.096
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.5191 0.71308 0.044 0.000 0.324 0.008 0.624
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.0794 0.88202 0.028 0.000 0.000 0.972 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.7223 0.61793 0.608 0.160 0.048 0.132 0.052
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.5109 0.20206 0.580 0.000 0.028 0.008 0.384
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.5463 0.64714 0.688 0.000 0.144 0.156 0.012
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.4470 0.45936 0.000 0.616 0.372 0.000 0.012
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.3370 0.64355 0.824 0.000 0.000 0.028 0.148
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0324 0.88143 0.000 0.000 0.004 0.992 0.004
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3895 0.59012 0.680 0.000 0.000 0.320 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4161 0.27787 0.000 0.608 0.000 0.392 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.5200 0.54715 0.688 0.000 0.152 0.000 0.160
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.5732 0.62693 0.000 0.184 0.192 0.000 0.624
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.4238 0.45720 0.628 0.000 0.004 0.000 0.368
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.3495 0.61806 0.812 0.000 0.028 0.000 0.160
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 5 0.4547 0.27529 0.400 0.000 0.000 0.012 0.588
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.1544 0.67057 0.000 0.068 0.932 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0324 0.68404 0.004 0.000 0.992 0.000 0.004
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.4929 0.51612 0.024 0.640 0.324 0.000 0.012
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4356 0.51048 0.000 0.648 0.340 0.000 0.012
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.4688 0.43228 0.004 0.364 0.016 0.000 0.616
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 5 0.4790 0.72063 0.024 0.000 0.344 0.004 0.628
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.4101 0.71701 0.000 0.000 0.372 0.000 0.628
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.5682 0.30340 0.000 0.540 0.372 0.000 0.088
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.3838 0.62586 0.716 0.000 0.004 0.280 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.6103 0.34491 0.548 0.000 0.292 0.000 0.160
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4402 0.49672 0.000 0.636 0.352 0.000 0.012
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0794 0.88202 0.028 0.000 0.000 0.972 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.4101 0.71701 0.000 0.000 0.372 0.000 0.628
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0290 0.84919 0.000 0.992 0.008 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.1952 0.80091 0.000 0.912 0.084 0.000 0.004
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1059 0.87432 0.008 0.000 0.020 0.968 0.004
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3949 0.49393 0.696 0.004 0.300 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.4101 0.71701 0.000 0.000 0.372 0.000 0.628
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.3563 0.67986 0.000 0.780 0.208 0.000 0.012
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.6315 0.30544 0.160 0.000 0.468 0.000 0.372
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.4251 0.71575 0.000 0.004 0.372 0.000 0.624
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0162 0.88090 0.000 0.000 0.000 0.996 0.004
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0794 0.88202 0.028 0.000 0.000 0.972 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.5555 0.42967 0.132 0.232 0.636 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.5579 0.55707 0.000 0.264 0.116 0.000 0.620
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.4305 0.53802 0.000 0.000 0.488 0.000 0.512
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 5 0.5632 0.66192 0.140 0.000 0.232 0.000 0.628
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.0794 0.88202 0.028 0.000 0.000 0.972 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.4242 0.43634 0.572 0.000 0.000 0.428 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 5 0.4984 0.34179 0.008 0.000 0.028 0.344 0.620
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.68239 0.000 0.000 1.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.2017 0.67789 0.080 0.000 0.912 0.000 0.008
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0510 0.88291 0.016 0.000 0.000 0.984 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0794 0.88202 0.028 0.000 0.000 0.972 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0566 0.87913 0.000 0.000 0.012 0.984 0.004
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.6185 0.08753 0.000 0.124 0.500 0.372 0.004
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.5509 -0.18217 0.468 0.000 0.468 0.000 0.064
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1197 0.67727 0.000 0.000 0.952 0.048 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3427 0.69509 0.000 0.796 0.192 0.000 0.012
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.1026 0.87023 0.004 0.000 0.024 0.968 0.004
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.5405 0.33578 0.484 0.000 0.000 0.460 0.056
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.4276 0.40182 0.000 0.000 0.380 0.616 0.004
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0880 0.69009 0.032 0.000 0.968 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.2230 0.56597 0.000 0.000 0.884 0.000 0.116
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.5946 0.60787 0.580 0.000 0.108 0.304 0.008
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.3300 0.67740 0.000 0.000 0.204 0.792 0.004
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2732 0.67322 0.840 0.000 0.000 0.160 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.6458 0.31518 0.160 0.000 0.004 0.464 0.372
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0404 0.67805 0.000 0.000 0.988 0.000 0.012
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0404 0.67805 0.000 0.000 0.988 0.000 0.012
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.4278 0.00865 0.452 0.000 0.548 0.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.85305 0.000 1.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 5 0.4238 0.71908 0.000 0.004 0.368 0.000 0.628
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.1851 0.65636 0.000 0.088 0.912 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0162 0.88090 0.000 0.000 0.000 0.996 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 5 0.4182 0.361 0.024 0.004 0.312 0.000 0.660 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1204 0.813 0.000 0.000 0.944 0.000 0.056 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.2609 0.679 0.868 0.000 0.000 0.036 0.096 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.0891 0.836 0.024 0.000 0.000 0.008 0.968 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.0260 0.951 0.008 0.000 0.000 0.992 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.5619 0.647 0.692 0.108 0.092 0.088 0.020 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.3833 -0.108 0.556 0.000 0.000 0.000 0.444 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.2814 0.685 0.820 0.000 0.000 0.008 0.172 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.3189 0.730 0.000 0.760 0.000 0.004 0.236 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.1908 0.682 0.900 0.000 0.000 0.004 0.096 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0972 0.943 0.000 0.000 0.028 0.964 0.008 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3101 0.650 0.756 0.000 0.000 0.244 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.3737 0.307 0.000 0.608 0.000 0.392 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.3371 0.607 0.708 0.000 0.000 0.000 0.292 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.1663 0.804 0.000 0.088 0.000 0.000 0.912 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 6 0.0508 0.981 0.012 0.000 0.000 0.000 0.004 0.984
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0363 0.671 0.988 0.000 0.000 0.000 0.012 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 5 0.3634 0.539 0.356 0.000 0.000 0.000 0.644 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.1367 0.804 0.000 0.012 0.944 0.000 0.044 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1814 0.816 0.000 0.000 0.900 0.000 0.100 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3290 0.747 0.016 0.776 0.000 0.000 0.208 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.3050 0.732 0.000 0.764 0.000 0.000 0.236 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.3109 0.631 0.004 0.224 0.000 0.000 0.772 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 5 0.0692 0.839 0.020 0.000 0.000 0.004 0.976 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.0000 0.840 0.000 0.000 0.000 0.000 1.000 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.3765 0.452 0.000 0.596 0.000 0.000 0.404 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2907 0.681 0.828 0.000 0.020 0.152 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.3531 0.606 0.672 0.000 0.000 0.000 0.328 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.3023 0.736 0.000 0.768 0.000 0.000 0.232 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0260 0.951 0.008 0.000 0.000 0.992 0.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.0000 0.840 0.000 0.000 0.000 0.000 1.000 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0260 0.883 0.000 0.992 0.000 0.000 0.008 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.1141 0.860 0.000 0.948 0.000 0.000 0.052 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0777 0.939 0.004 0.000 0.000 0.972 0.024 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3908 0.654 0.764 0.004 0.188 0.008 0.036 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.0000 0.840 0.000 0.000 0.000 0.000 1.000 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2854 0.757 0.000 0.792 0.000 0.000 0.208 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 6 0.0000 0.991 0.000 0.000 0.000 0.000 0.000 1.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.0146 0.840 0.000 0.004 0.000 0.000 0.996 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0865 0.941 0.000 0.000 0.036 0.964 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0260 0.951 0.008 0.000 0.000 0.992 0.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.4018 0.643 0.160 0.044 0.772 0.000 0.024 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.2003 0.778 0.000 0.116 0.000 0.000 0.884 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.1663 0.787 0.000 0.000 0.088 0.000 0.912 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 5 0.2597 0.717 0.176 0.000 0.000 0.000 0.824 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.0260 0.951 0.008 0.000 0.000 0.992 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.3592 0.558 0.656 0.000 0.000 0.344 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 5 0.2823 0.643 0.000 0.000 0.000 0.204 0.796 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.1204 0.813 0.000 0.000 0.944 0.000 0.056 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.2664 0.783 0.000 0.000 0.816 0.000 0.184 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0405 0.950 0.004 0.000 0.008 0.988 0.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0260 0.951 0.008 0.000 0.000 0.992 0.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0909 0.946 0.000 0.000 0.020 0.968 0.012 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.4255 0.551 0.000 0.068 0.708 0.224 0.000 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.4613 0.623 0.660 0.000 0.080 0.000 0.260 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1285 0.811 0.000 0.000 0.944 0.004 0.052 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3136 0.765 0.000 0.796 0.016 0.000 0.188 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0713 0.935 0.000 0.000 0.000 0.972 0.028 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.5303 0.492 0.528 0.000 0.000 0.360 0.112 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.4141 0.180 0.000 0.000 0.556 0.432 0.012 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.2003 0.813 0.000 0.000 0.884 0.000 0.116 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.3578 0.619 0.000 0.000 0.660 0.000 0.340 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.4585 0.678 0.692 0.000 0.000 0.192 0.116 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.3309 0.618 0.000 0.000 0.280 0.720 0.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0146 0.668 0.996 0.000 0.004 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 6 0.0000 0.991 0.000 0.000 0.000 0.000 0.000 1.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.2883 0.765 0.000 0.000 0.788 0.000 0.212 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.2912 0.762 0.000 0.000 0.784 0.000 0.216 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 1 0.4994 0.386 0.544 0.000 0.380 0.000 0.076 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.886 0.000 1.000 0.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 5 0.0146 0.841 0.000 0.004 0.000 0.000 0.996 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.1204 0.813 0.000 0.000 0.944 0.000 0.056 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0865 0.941 0.000 0.000 0.036 0.964 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 17471 rows and 87 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 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 0.351 0.604 0.840 0.4364 0.518 0.518
#> 3 3 0.496 0.729 0.834 0.4181 0.628 0.404
#> 4 4 0.743 0.816 0.902 0.1504 0.818 0.552
#> 5 5 0.716 0.592 0.788 0.0838 0.885 0.608
#> 6 6 0.766 0.698 0.846 0.0560 0.926 0.670
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.9944 0.162 0.456 0.544
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.9993 0.054 0.484 0.516
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.786 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.8499 0.553 0.724 0.276
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.786 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.9970 0.110 0.532 0.468
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.786 1.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0000 0.786 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.6887 0.697 0.184 0.816
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.786 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.786 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.786 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.7056 0.688 0.192 0.808
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.786 1.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.8555 0.591 0.280 0.720
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0000 0.786 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.786 1.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.786 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.9922 0.188 0.448 0.552
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.8909 0.507 0.692 0.308
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.7056 0.685 0.192 0.808
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0376 0.751 0.004 0.996
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.9661 0.374 0.392 0.608
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.4298 0.740 0.088 0.912
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.786 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.9963 0.126 0.536 0.464
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.9881 0.223 0.436 0.564
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.750 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.786 1.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.8909 0.507 0.692 0.308
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.1633 0.752 0.024 0.976
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.9323 0.484 0.348 0.652
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.786 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.9491 0.396 0.632 0.368
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.750 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.750 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0938 0.752 0.012 0.988
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.1633 0.774 0.976 0.024
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.786 1.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 1 0.9933 0.169 0.548 0.452
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0938 0.752 0.012 0.988
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.0000 0.786 1.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.6247 0.712 0.156 0.844
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.786 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.9000 0.534 0.316 0.684
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.786 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.9866 0.233 0.432 0.568
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5737 0.724 0.136 0.864
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.9552 0.382 0.624 0.376
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0376 0.784 0.996 0.004
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.9491 0.396 0.368 0.632
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.9170 0.508 0.332 0.668
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.786 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.3114 0.747 0.056 0.944
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.786 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.8713 0.531 0.708 0.292
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 1 0.9954 0.142 0.540 0.460
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 1 0.9129 0.477 0.672 0.328
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.786 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.786 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.750 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.750 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.5294 0.712 0.880 0.120
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 1 0.6623 0.669 0.828 0.172
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.9944 0.156 0.544 0.456
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.750 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 1 0.9710 0.323 0.600 0.400
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.9815 0.325 0.420 0.580
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0000 0.786 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.786 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.786 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.750 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 1 0.9944 0.156 0.544 0.456
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 1 0.9580 0.373 0.620 0.380
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.4690 0.727 0.900 0.100
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0000 0.786 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.750 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.750 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.786 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.786 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 1 0.9866 0.235 0.568 0.432
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 1 0.8909 0.509 0.692 0.308
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 1 0.9896 0.210 0.560 0.440
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.750 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.9944 0.162 0.456 0.544
#> A247D92D-253A-4BEC-B450-184AF90D17D0 1 0.9170 0.470 0.668 0.332
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0000 0.786 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0237 0.8541 0.000 0.004 0.996
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0237 0.8541 0.000 0.004 0.996
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.5305 0.8005 0.788 0.020 0.192
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.4963 0.6823 0.200 0.008 0.792
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.5305 0.8005 0.788 0.020 0.192
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.2681 0.8331 0.028 0.040 0.932
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.5305 0.8005 0.788 0.020 0.192
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.2804 0.7876 0.924 0.060 0.016
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.6737 0.4574 0.040 0.272 0.688
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.5305 0.8005 0.788 0.020 0.192
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.2804 0.7876 0.924 0.060 0.016
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.5503 0.7957 0.772 0.020 0.208
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.9786 0.1897 0.364 0.400 0.236
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.5305 0.8005 0.788 0.020 0.192
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.6142 0.5969 0.040 0.212 0.748
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.2492 0.7878 0.936 0.048 0.016
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.5305 0.8005 0.788 0.020 0.192
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.5305 0.8005 0.788 0.020 0.192
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0237 0.8541 0.000 0.004 0.996
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.8538 0.000 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 1 0.8128 0.1360 0.492 0.440 0.068
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.5397 0.7138 0.000 0.720 0.280
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.8448 0.4765 0.164 0.220 0.616
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.6000 0.7840 0.040 0.760 0.200
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.5305 0.8005 0.788 0.020 0.192
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.3155 0.8242 0.044 0.040 0.916
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0892 0.8497 0.000 0.020 0.980
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.2261 0.8464 0.000 0.932 0.068
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.5092 0.8067 0.804 0.020 0.176
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.0892 0.8460 0.020 0.000 0.980
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.6518 0.2815 0.004 0.512 0.484
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.7953 0.3416 0.564 0.368 0.068
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.2703 0.7879 0.928 0.056 0.016
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.5581 0.6934 0.176 0.036 0.788
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.3752 0.8331 0.000 0.856 0.144
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.2711 0.8482 0.000 0.912 0.088
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.5804 0.7918 0.088 0.800 0.112
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.3120 0.7810 0.908 0.012 0.080
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.6255 0.7961 0.748 0.048 0.204
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.2383 0.8370 0.044 0.016 0.940
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.6215 0.4440 0.000 0.572 0.428
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.2492 0.7878 0.936 0.048 0.016
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.6188 0.5905 0.040 0.216 0.744
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.2703 0.7879 0.928 0.056 0.016
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 1 0.8140 0.0797 0.476 0.456 0.068
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.5253 0.8013 0.792 0.020 0.188
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0424 0.8536 0.000 0.008 0.992
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.6482 0.5372 0.040 0.244 0.716
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.2774 0.8233 0.072 0.008 0.920
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.5305 0.8005 0.788 0.020 0.192
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.7708 0.0492 0.048 0.424 0.528
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 1 0.8046 0.2699 0.536 0.396 0.068
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5305 0.8005 0.788 0.020 0.192
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.4555 0.7962 0.000 0.800 0.200
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.4883 0.7974 0.788 0.004 0.208
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.5397 0.4750 0.280 0.000 0.720
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.8538 0.000 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.5363 0.4647 0.276 0.000 0.724
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.2703 0.7879 0.928 0.056 0.016
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.5792 0.8017 0.772 0.036 0.192
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.3715 0.8397 0.004 0.868 0.128
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2261 0.8464 0.000 0.932 0.068
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.3207 0.7770 0.904 0.012 0.084
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 1 0.4915 0.6883 0.804 0.012 0.184
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0237 0.8541 0.000 0.004 0.996
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.2261 0.8464 0.000 0.932 0.068
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0424 0.8526 0.008 0.000 0.992
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 1 0.8068 0.4324 0.596 0.316 0.088
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.2599 0.7881 0.932 0.052 0.016
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.5305 0.8005 0.788 0.020 0.192
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.2492 0.7878 0.936 0.048 0.016
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.2261 0.8464 0.000 0.932 0.068
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.8538 0.000 0.000 1.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0424 0.8526 0.008 0.000 0.992
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.4575 0.7877 0.828 0.012 0.160
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.2804 0.7876 0.924 0.060 0.016
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.2261 0.8464 0.000 0.932 0.068
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2711 0.8482 0.000 0.912 0.088
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.5305 0.8005 0.788 0.020 0.192
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.2492 0.7878 0.936 0.048 0.016
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0424 0.8526 0.008 0.000 0.992
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0592 0.8530 0.012 0.000 0.988
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.8538 0.000 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.2261 0.8464 0.000 0.932 0.068
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1411 0.8350 0.000 0.036 0.964
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0237 0.8537 0.004 0.000 0.996
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.2804 0.7876 0.924 0.060 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0376 0.9134 0.004 0.004 0.992 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.9140 0.000 0.000 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0188 0.9087 0.996 0.000 0.000 0.004
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.2976 0.8165 0.120 0.000 0.872 0.008
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0336 0.9079 0.992 0.000 0.000 0.008
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0524 0.9130 0.008 0.004 0.988 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0188 0.9087 0.996 0.000 0.000 0.004
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.1824 0.8683 0.004 0.060 0.000 0.936
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.3975 0.6175 0.000 0.240 0.760 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0188 0.9087 0.996 0.000 0.000 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0895 0.8901 0.020 0.000 0.004 0.976
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3306 0.7755 0.840 0.000 0.156 0.004
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4936 0.5425 0.000 0.624 0.372 0.004
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1816 0.8948 0.948 0.024 0.004 0.024
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.1209 0.9014 0.004 0.032 0.964 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.0188 0.8990 0.000 0.000 0.004 0.996
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1733 0.8936 0.948 0.028 0.000 0.024
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0188 0.9087 0.996 0.000 0.000 0.004
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.9140 0.000 0.000 1.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.9140 0.000 0.000 1.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3978 0.8734 0.000 0.836 0.108 0.056
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4040 0.7683 0.000 0.752 0.248 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.1970 0.8780 0.008 0.060 0.932 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.6286 0.7007 0.000 0.660 0.140 0.200
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0336 0.9076 0.992 0.000 0.000 0.008
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.1256 0.9030 0.008 0.028 0.964 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0376 0.9134 0.004 0.004 0.992 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.2081 0.8904 0.000 0.916 0.084 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.8365 0.3567 0.496 0.084 0.108 0.312
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.0188 0.9136 0.000 0.000 0.996 0.004
#> 36EDD202-A845-4CE7-95D5-A515C471262E 3 0.4999 -0.1740 0.000 0.492 0.508 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3292 0.8221 0.004 0.880 0.036 0.080
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0188 0.8991 0.004 0.000 0.000 0.996
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.1362 0.9045 0.012 0.020 0.964 0.004
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.2530 0.8879 0.000 0.888 0.112 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.2281 0.8915 0.000 0.904 0.096 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2868 0.8774 0.000 0.864 0.136 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.5780 -0.0237 0.028 0.000 0.496 0.476
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.5710 0.6208 0.008 0.060 0.228 0.704
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0336 0.9131 0.000 0.008 0.992 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.4843 0.4838 0.000 0.604 0.396 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0188 0.8991 0.004 0.000 0.000 0.996
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.1305 0.8990 0.004 0.036 0.960 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0188 0.8991 0.004 0.000 0.000 0.996
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.3286 0.8319 0.000 0.876 0.044 0.080
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4072 0.6688 0.748 0.000 0.000 0.252
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.1389 0.8845 0.000 0.048 0.952 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.1398 0.8962 0.004 0.040 0.956 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.0336 0.9128 0.000 0.000 0.992 0.008
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0336 0.9076 0.992 0.000 0.000 0.008
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.4697 0.3339 0.000 0.356 0.644 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.3107 0.8242 0.000 0.884 0.036 0.080
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0188 0.9087 0.996 0.000 0.000 0.004
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.2868 0.8774 0.000 0.864 0.136 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.5212 0.6998 0.740 0.000 0.192 0.068
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.1004 0.9018 0.024 0.000 0.972 0.004
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.9140 0.000 0.000 1.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0336 0.9129 0.000 0.000 0.992 0.008
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0817 0.8892 0.024 0.000 0.000 0.976
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.2011 0.8715 0.920 0.000 0.000 0.080
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.2345 0.8910 0.000 0.900 0.100 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2081 0.8904 0.000 0.916 0.084 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.5498 0.2745 0.020 0.000 0.404 0.576
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.3942 0.6511 0.000 0.000 0.236 0.764
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0336 0.9135 0.008 0.000 0.992 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.2081 0.8904 0.000 0.916 0.084 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.9140 0.000 0.000 1.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.5393 0.5808 0.000 0.688 0.044 0.268
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0188 0.8991 0.004 0.000 0.000 0.996
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0188 0.9087 0.996 0.000 0.000 0.004
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0895 0.8901 0.020 0.000 0.004 0.976
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.2081 0.8904 0.000 0.916 0.084 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.9140 0.000 0.000 1.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0188 0.9135 0.000 0.000 0.996 0.004
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.5291 0.4515 0.024 0.000 0.652 0.324
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0188 0.8990 0.000 0.000 0.004 0.996
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.2081 0.8904 0.000 0.916 0.084 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2281 0.8915 0.000 0.904 0.096 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2871 0.8686 0.896 0.032 0.000 0.072
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0188 0.8991 0.004 0.000 0.000 0.996
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0000 0.9140 0.000 0.000 1.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0188 0.9135 0.000 0.000 0.996 0.004
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.9140 0.000 0.000 1.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.2081 0.8904 0.000 0.916 0.084 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0524 0.9130 0.008 0.004 0.988 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0469 0.9113 0.000 0.000 0.988 0.012
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0188 0.8990 0.000 0.000 0.004 0.996
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 5 0.3010 0.523354 0.000 0.004 0.172 0.000 0.824
#> F569915C-8F77-4D67-9730-30824DB57EE5 5 0.4410 -0.189597 0.000 0.004 0.440 0.000 0.556
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.930455 1.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.2463 0.511812 0.100 0.004 0.008 0.000 0.888
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.930455 1.000 0.000 0.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 5 0.2361 0.556688 0.000 0.012 0.096 0.000 0.892
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.930455 1.000 0.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.4251 0.606379 0.004 0.000 0.372 0.624 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.5441 0.239925 0.000 0.324 0.080 0.000 0.596
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.930455 1.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0510 0.828340 0.016 0.000 0.000 0.984 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2890 0.826338 0.836 0.000 0.004 0.000 0.160
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4742 0.399152 0.000 0.648 0.008 0.020 0.324
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.3561 0.864940 0.844 0.000 0.060 0.012 0.084
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.0671 0.550468 0.000 0.016 0.004 0.000 0.980
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.0324 0.830469 0.004 0.000 0.004 0.992 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1809 0.902001 0.928 0.000 0.060 0.012 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0162 0.929225 0.996 0.000 0.000 0.000 0.004
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 5 0.4310 0.000683 0.000 0.004 0.392 0.000 0.604
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.4305 0.428757 0.000 0.000 0.512 0.000 0.488
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0798 0.865590 0.000 0.976 0.008 0.016 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.1544 0.835575 0.000 0.932 0.000 0.000 0.068
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.3419 0.398186 0.000 0.180 0.016 0.000 0.804
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.3048 0.700364 0.000 0.820 0.004 0.176 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.930455 1.000 0.000 0.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.0579 0.551527 0.000 0.008 0.008 0.000 0.984
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.4193 0.427737 0.000 0.024 0.256 0.000 0.720
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 3 0.7320 -0.449732 0.180 0.000 0.420 0.356 0.044
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.3333 0.498367 0.004 0.000 0.208 0.000 0.788
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4150 0.287369 0.000 0.612 0.000 0.000 0.388
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.4829 0.632666 0.000 0.660 0.300 0.036 0.004
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0404 0.828694 0.012 0.000 0.000 0.988 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.0451 0.549914 0.000 0.008 0.004 0.000 0.988
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.5910 -0.068440 0.040 0.000 0.032 0.476 0.452
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.4590 -0.431694 0.000 0.000 0.568 0.420 0.012
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.4414 0.080129 0.000 0.004 0.376 0.004 0.616
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.3661 0.555102 0.000 0.724 0.000 0.000 0.276
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0290 0.829062 0.000 0.000 0.008 0.992 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.1648 0.546445 0.000 0.040 0.020 0.000 0.940
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0162 0.830687 0.004 0.000 0.000 0.996 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1493 0.853940 0.000 0.948 0.028 0.024 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.3876 0.505710 0.684 0.000 0.000 0.316 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 5 0.6468 0.139394 0.000 0.188 0.360 0.000 0.452
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.2130 0.503947 0.000 0.080 0.012 0.000 0.908
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.6012 -0.222829 0.000 0.000 0.400 0.116 0.484
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.930455 1.000 0.000 0.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.5477 0.493513 0.000 0.648 0.220 0.000 0.132
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4677 0.635624 0.000 0.664 0.300 0.036 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.930455 1.000 0.000 0.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.3977 0.796711 0.792 0.000 0.024 0.016 0.168
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 5 0.3737 0.472208 0.004 0.000 0.224 0.008 0.764
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.4300 0.460041 0.000 0.000 0.524 0.000 0.476
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.4552 0.456918 0.000 0.000 0.524 0.008 0.468
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0510 0.828340 0.016 0.000 0.000 0.984 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0290 0.927028 0.992 0.000 0.000 0.008 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0290 0.871253 0.000 0.992 0.000 0.000 0.008
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.5426 0.105434 0.016 0.000 0.032 0.544 0.408
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.4452 0.460818 0.000 0.000 0.032 0.696 0.272
#> 1CC36859-357A-49E0-A367-4F57D47288BA 5 0.3266 0.503054 0.000 0.004 0.200 0.000 0.796
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.4300 0.463431 0.000 0.000 0.524 0.000 0.476
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 4 0.6905 0.210755 0.000 0.296 0.304 0.396 0.004
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0162 0.829746 0.000 0.000 0.004 0.996 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0451 0.927326 0.988 0.000 0.004 0.000 0.008
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0486 0.828533 0.004 0.000 0.004 0.988 0.004
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 5 0.4268 -0.231388 0.000 0.000 0.444 0.000 0.556
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4443 0.454555 0.000 0.000 0.524 0.004 0.472
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.5640 0.070095 0.028 0.000 0.028 0.448 0.496
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0162 0.830687 0.004 0.000 0.000 0.996 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.3051 0.858143 0.852 0.000 0.120 0.028 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0290 0.829062 0.000 0.000 0.008 0.992 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.4302 0.449265 0.000 0.000 0.520 0.000 0.480
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.4300 0.463431 0.000 0.000 0.524 0.000 0.476
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.4300 0.460041 0.000 0.000 0.524 0.000 0.476
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.874316 0.000 1.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 5 0.3081 0.538040 0.000 0.012 0.156 0.000 0.832
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.4294 0.461731 0.000 0.000 0.532 0.000 0.468
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0162 0.830687 0.004 0.000 0.000 0.996 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 5 0.2165 0.8722 0.000 0.008 0.108 0.000 0.884 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.3081 0.6497 0.000 0.004 0.776 0.000 0.220 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.9409 1.000 0.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.2237 0.8499 0.064 0.004 0.024 0.000 0.904 0.004
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0260 0.9413 0.992 0.000 0.000 0.000 0.008 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 5 0.2571 0.8675 0.000 0.060 0.064 0.000 0.876 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.9409 1.000 0.000 0.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3797 0.2507 0.000 0.000 0.000 0.580 0.000 0.420
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.6450 0.1785 0.000 0.332 0.420 0.016 0.228 0.004
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0146 0.9412 0.996 0.000 0.000 0.000 0.004 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0551 0.7123 0.004 0.000 0.000 0.984 0.004 0.008
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2092 0.8409 0.876 0.000 0.000 0.000 0.124 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.5051 0.5009 0.000 0.676 0.020 0.000 0.192 0.112
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.2237 0.8903 0.896 0.000 0.000 0.000 0.036 0.068
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.1410 0.8864 0.000 0.008 0.044 0.000 0.944 0.004
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.3390 0.5461 0.000 0.000 0.000 0.704 0.000 0.296
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.2145 0.8900 0.900 0.000 0.000 0.000 0.028 0.072
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.9409 1.000 0.000 0.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.4067 0.1080 0.000 0.008 0.548 0.000 0.444 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0865 0.8102 0.000 0.000 0.964 0.000 0.036 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.2416 0.7340 0.000 0.844 0.000 0.000 0.000 0.156
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.2432 0.7360 0.000 0.876 0.024 0.000 0.100 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.1672 0.8879 0.000 0.016 0.048 0.000 0.932 0.004
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2482 0.7396 0.000 0.848 0.000 0.000 0.004 0.148
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0260 0.9413 0.992 0.000 0.000 0.000 0.008 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.1268 0.8837 0.000 0.008 0.036 0.000 0.952 0.004
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.3422 0.7952 0.000 0.036 0.176 0.000 0.788 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8448 0.000 1.000 0.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 6 0.7102 0.0959 0.252 0.000 0.008 0.216 0.076 0.448
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.2823 0.7845 0.000 0.000 0.204 0.000 0.796 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4089 0.4947 0.000 0.696 0.040 0.000 0.264 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 6 0.4095 0.1473 0.000 0.480 0.000 0.000 0.008 0.512
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0260 0.7136 0.000 0.000 0.000 0.992 0.000 0.008
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.1364 0.8873 0.000 0.004 0.048 0.000 0.944 0.004
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.8448 0.000 1.000 0.000 0.000 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.8448 0.000 1.000 0.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0937 0.8278 0.000 0.960 0.000 0.000 0.000 0.040
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.6765 0.2953 0.072 0.000 0.312 0.476 0.132 0.008
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 6 0.6048 -0.1565 0.000 0.000 0.212 0.368 0.004 0.416
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.3852 0.4748 0.000 0.012 0.664 0.000 0.324 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.3642 0.5894 0.000 0.760 0.036 0.000 0.204 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3769 0.4954 0.000 0.000 0.000 0.640 0.004 0.356
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.2814 0.8462 0.000 0.080 0.052 0.000 0.864 0.004
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.7143 0.000 0.000 0.000 1.000 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.3126 0.5987 0.000 0.752 0.000 0.000 0.000 0.248
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.2068 0.8654 0.904 0.000 0.000 0.080 0.008 0.008
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.6100 0.0365 0.000 0.304 0.384 0.000 0.312 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.1738 0.8884 0.000 0.016 0.052 0.000 0.928 0.004
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.1625 0.7970 0.000 0.000 0.928 0.012 0.060 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0260 0.9413 0.992 0.000 0.000 0.000 0.008 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.5065 0.3631 0.000 0.636 0.192 0.000 0.172 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 6 0.3866 0.1352 0.000 0.484 0.000 0.000 0.000 0.516
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.9409 1.000 0.000 0.000 0.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1141 0.8204 0.000 0.948 0.000 0.000 0.000 0.052
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.2730 0.7599 0.808 0.000 0.000 0.000 0.192 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 5 0.3615 0.6227 0.008 0.000 0.292 0.000 0.700 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0632 0.8117 0.000 0.000 0.976 0.000 0.024 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0993 0.8090 0.000 0.000 0.964 0.012 0.024 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0767 0.7093 0.012 0.000 0.000 0.976 0.004 0.008
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0260 0.9413 0.992 0.000 0.000 0.000 0.008 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.8448 0.000 1.000 0.000 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.8448 0.000 1.000 0.000 0.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.4537 0.3555 0.004 0.000 0.400 0.572 0.016 0.008
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.3578 0.4582 0.000 0.000 0.340 0.660 0.000 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 5 0.3240 0.7419 0.000 0.004 0.244 0.000 0.752 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.8448 0.000 1.000 0.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0547 0.8111 0.000 0.000 0.980 0.000 0.020 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 6 0.5731 0.4234 0.000 0.304 0.004 0.172 0.000 0.520
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0291 0.7142 0.000 0.000 0.004 0.992 0.000 0.004
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0260 0.9413 0.992 0.000 0.000 0.000 0.008 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1053 0.7094 0.004 0.000 0.012 0.964 0.000 0.020
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8448 0.000 1.000 0.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.2902 0.6827 0.000 0.004 0.800 0.000 0.196 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0000 0.7978 0.000 0.000 1.000 0.000 0.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.6682 0.2689 0.032 0.000 0.228 0.460 0.272 0.008
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0000 0.7143 0.000 0.000 0.000 1.000 0.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8448 0.000 1.000 0.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.8448 0.000 1.000 0.000 0.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2968 0.8408 0.840 0.000 0.000 0.004 0.028 0.128
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.3769 0.4954 0.000 0.000 0.000 0.640 0.004 0.356
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0713 0.8113 0.000 0.000 0.972 0.000 0.028 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0547 0.8111 0.000 0.000 0.980 0.000 0.020 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0632 0.8117 0.000 0.000 0.976 0.000 0.024 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8448 0.000 1.000 0.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 5 0.1757 0.8848 0.000 0.008 0.076 0.000 0.916 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.7978 0.000 0.000 1.000 0.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.7143 0.000 0.000 0.000 1.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17471 rows and 87 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 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 0.973 0.944 0.976 0.4964 0.505 0.505
#> 3 3 0.824 0.878 0.936 0.3284 0.733 0.521
#> 4 4 0.939 0.911 0.943 0.1371 0.806 0.506
#> 5 5 0.854 0.828 0.915 0.0561 0.925 0.720
#> 6 6 0.781 0.641 0.783 0.0413 0.892 0.560
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0000 0.971 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.971 0.000 1.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.979 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.979 1.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.979 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0376 0.969 0.004 0.996
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.979 1.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0000 0.979 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0376 0.969 0.004 0.996
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.979 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.979 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.979 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.971 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.979 1.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.971 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0376 0.976 0.996 0.004
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.979 1.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.979 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.971 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.6973 0.779 0.188 0.812
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.971 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.971 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1843 0.952 0.028 0.972
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.971 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.979 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.1414 0.959 0.020 0.980
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0000 0.971 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.971 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.979 1.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.3733 0.909 0.928 0.072
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.971 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2236 0.945 0.036 0.964
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.979 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.9944 0.153 0.456 0.544
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.971 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.971 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.971 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.979 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.6973 0.763 0.812 0.188
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0376 0.969 0.004 0.996
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.971 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.1414 0.963 0.980 0.020
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.971 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.979 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.971 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.979 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0000 0.971 0.000 1.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.971 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.3114 0.929 0.056 0.944
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.979 1.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.971 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.971 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.979 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.971 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.979 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.979 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.971 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.7299 0.756 0.204 0.796
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.979 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.979 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.971 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.971 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0000 0.979 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 1 0.9754 0.273 0.592 0.408
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.3584 0.918 0.068 0.932
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.971 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.3733 0.914 0.072 0.928
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.971 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.1414 0.963 0.980 0.020
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.979 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.979 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.971 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.1414 0.959 0.020 0.980
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.971 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.979 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0000 0.979 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.971 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.971 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.979 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.979 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.971 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.7219 0.762 0.200 0.800
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0000 0.971 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.971 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.971 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.971 0.000 1.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0000 0.979 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.4228 0.833 0.008 0.844 0.148
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.4452 0.786 0.000 0.808 0.192
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.961 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0892 0.948 0.980 0.000 0.020
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.961 1.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.7580 0.464 0.340 0.604 0.056
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.961 1.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.4452 0.752 0.808 0.000 0.192
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.1964 0.891 0.000 0.056 0.944
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.961 1.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.1964 0.915 0.056 0.000 0.944
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.961 1.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.929 0.000 1.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.961 1.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.1643 0.913 0.000 0.956 0.044
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.1529 0.915 0.040 0.000 0.960
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.961 1.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.961 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.2165 0.904 0.000 0.936 0.064
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0237 0.903 0.000 0.004 0.996
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.929 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.929 0.000 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5327 0.636 0.272 0.728 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0424 0.926 0.000 0.992 0.008
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.961 1.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.4628 0.851 0.088 0.856 0.056
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.1964 0.907 0.000 0.944 0.056
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.929 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.961 1.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.4353 0.837 0.836 0.008 0.156
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.929 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.6095 0.359 0.392 0.608 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 3 0.2165 0.910 0.064 0.000 0.936
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.5307 0.798 0.820 0.124 0.056
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.929 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.929 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.929 0.000 1.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.1964 0.915 0.056 0.000 0.944
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3589 0.888 0.900 0.048 0.052
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0424 0.902 0.000 0.008 0.992
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.929 0.000 1.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.1031 0.911 0.024 0.000 0.976
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.1753 0.911 0.000 0.952 0.048
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.1964 0.915 0.056 0.000 0.944
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.929 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.961 1.000 0.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.1964 0.907 0.000 0.944 0.056
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0424 0.927 0.000 0.992 0.008
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.1529 0.883 0.000 0.040 0.960
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.961 1.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0237 0.928 0.000 0.996 0.004
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.929 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.961 1.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.929 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.1411 0.936 0.964 0.000 0.036
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.5926 0.417 0.356 0.000 0.644
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.4178 0.810 0.000 0.828 0.172
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.903 0.000 0.000 1.000
#> C41F3064-4483-4796-B860-82155BAA5157 3 0.1964 0.915 0.056 0.000 0.944
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.2448 0.896 0.924 0.000 0.076
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.929 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.929 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.1964 0.915 0.056 0.000 0.944
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.1964 0.915 0.056 0.000 0.944
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.5667 0.799 0.140 0.800 0.060
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.929 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.903 0.000 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.929 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.1964 0.915 0.056 0.000 0.944
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.961 1.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.1753 0.915 0.048 0.000 0.952
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.929 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.5465 0.561 0.000 0.288 0.712
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.5431 0.570 0.000 0.284 0.716
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.1964 0.915 0.056 0.000 0.944
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.1964 0.915 0.056 0.000 0.944
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.929 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.929 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.961 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.1964 0.915 0.056 0.000 0.944
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.5363 0.585 0.000 0.276 0.724
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0000 0.903 0.000 0.000 1.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.5948 0.488 0.000 0.640 0.360
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.929 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.3237 0.894 0.032 0.912 0.056
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0747 0.898 0.000 0.016 0.984
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.1964 0.915 0.056 0.000 0.944
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.2032 0.928 0.028 0.036 0.936 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1902 0.930 0.000 0.064 0.932 0.004
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0188 0.926 0.996 0.000 0.004 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0188 0.926 0.996 0.000 0.004 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0376 0.926 0.992 0.000 0.004 0.004
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.1690 0.891 0.032 0.008 0.952 0.008
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0188 0.926 0.996 0.000 0.004 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3547 0.847 0.072 0.000 0.064 0.864
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.1059 0.973 0.000 0.016 0.012 0.972
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0188 0.926 0.996 0.000 0.004 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0469 0.987 0.000 0.000 0.012 0.988
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1743 0.912 0.940 0.000 0.056 0.004
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1118 0.919 0.964 0.000 0.036 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.5310 0.350 0.012 0.412 0.576 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.0592 0.983 0.000 0.000 0.016 0.984
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0469 0.924 0.988 0.000 0.012 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0188 0.926 0.996 0.000 0.004 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.1792 0.928 0.000 0.068 0.932 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1902 0.916 0.004 0.000 0.932 0.064
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.4730 0.425 0.364 0.636 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0188 0.926 0.996 0.000 0.004 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.2271 0.905 0.076 0.008 0.916 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.2011 0.921 0.000 0.080 0.920 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2179 0.906 0.924 0.000 0.064 0.012
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.1940 0.904 0.076 0.000 0.924 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2234 0.905 0.004 0.924 0.064 0.008
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0707 0.962 0.000 0.000 0.020 0.980
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.2216 0.868 0.908 0.000 0.092 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0469 0.987 0.000 0.000 0.012 0.988
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3577 0.853 0.832 0.000 0.156 0.012
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.2111 0.932 0.000 0.044 0.932 0.024
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0469 0.987 0.000 0.000 0.012 0.988
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.1867 0.899 0.000 0.928 0.072 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0469 0.987 0.000 0.000 0.012 0.988
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0592 0.956 0.000 0.984 0.016 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4360 0.669 0.744 0.000 0.008 0.248
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.1940 0.922 0.000 0.076 0.924 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.3311 0.766 0.000 0.828 0.172 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.3404 0.885 0.000 0.032 0.864 0.104
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0188 0.926 0.996 0.000 0.004 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.4040 0.733 0.000 0.248 0.752 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.2021 0.910 0.932 0.000 0.056 0.012
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.3913 0.825 0.824 0.000 0.148 0.028
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.1824 0.913 0.060 0.000 0.936 0.004
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.1902 0.930 0.000 0.064 0.932 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.1792 0.914 0.000 0.000 0.932 0.068
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0336 0.984 0.000 0.000 0.008 0.992
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.5497 0.183 0.524 0.000 0.016 0.460
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0469 0.987 0.000 0.000 0.012 0.988
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.0469 0.987 0.000 0.000 0.012 0.988
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.2124 0.910 0.068 0.008 0.924 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1792 0.914 0.000 0.000 0.932 0.068
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1118 0.941 0.000 0.964 0.036 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0000 0.978 0.000 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0188 0.926 0.996 0.000 0.004 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0469 0.987 0.000 0.000 0.012 0.988
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.2131 0.931 0.000 0.036 0.932 0.032
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.2021 0.931 0.000 0.056 0.932 0.012
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.0469 0.987 0.000 0.000 0.012 0.988
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0469 0.987 0.000 0.000 0.012 0.988
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2179 0.906 0.924 0.000 0.064 0.012
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0469 0.987 0.000 0.000 0.012 0.988
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.2131 0.931 0.000 0.036 0.932 0.032
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.1792 0.914 0.000 0.000 0.932 0.068
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.1902 0.930 0.000 0.064 0.932 0.004
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.2101 0.929 0.012 0.060 0.928 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.1902 0.917 0.000 0.004 0.932 0.064
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0469 0.987 0.000 0.000 0.012 0.988
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.8571 1.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.2605 0.8045 0.852 0.000 0.000 0.000 0.148
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.1121 0.8334 0.956 0.000 0.000 0.000 0.044
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 5 0.4305 0.1137 0.000 0.000 0.488 0.000 0.512
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.8571 1.000 0.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 5 0.3196 0.6518 0.004 0.000 0.000 0.192 0.804
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.4711 0.6564 0.000 0.116 0.000 0.736 0.148
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0290 0.8548 0.992 0.000 0.000 0.000 0.008
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 5 0.3039 0.7590 0.192 0.000 0.000 0.000 0.808
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.2280 0.7601 0.880 0.000 0.000 0.000 0.120
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.6886 0.0707 0.380 0.440 0.024 0.000 0.156
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.0162 0.9159 0.000 0.000 0.004 0.996 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1043 0.8361 0.960 0.000 0.000 0.000 0.040
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.8571 1.000 0.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0162 0.9321 0.000 0.000 0.996 0.000 0.004
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0162 0.9276 0.000 0.996 0.000 0.000 0.004
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.2605 0.8335 0.000 0.852 0.000 0.000 0.148
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1121 0.9033 0.044 0.956 0.000 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2020 0.8316 0.900 0.000 0.000 0.000 0.100
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.2690 0.7979 0.844 0.000 0.000 0.000 0.156
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 5 0.2690 0.7722 0.156 0.000 0.000 0.000 0.844
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.5689 0.5042 0.248 0.000 0.616 0.000 0.136
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.2848 0.8236 0.004 0.840 0.000 0.000 0.156
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 5 0.2929 0.6628 0.000 0.180 0.000 0.000 0.820
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.3210 0.6876 0.000 0.000 0.000 0.788 0.212
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.2690 0.7979 0.844 0.000 0.000 0.000 0.156
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.1478 0.8974 0.000 0.936 0.000 0.000 0.064
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.1270 0.9047 0.000 0.948 0.000 0.000 0.052
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.3039 0.7703 0.012 0.000 0.000 0.836 0.152
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 5 0.3459 0.7607 0.080 0.004 0.072 0.000 0.844
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.3943 0.7778 0.016 0.000 0.800 0.028 0.156
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0162 0.9276 0.000 0.996 0.000 0.000 0.004
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.3771 0.7954 0.036 0.804 0.004 0.000 0.156
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.5284 0.2514 0.568 0.000 0.000 0.376 0.056
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5267 0.7115 0.088 0.724 0.032 0.000 0.156
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.3991 0.7703 0.004 0.000 0.792 0.048 0.156
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2329 0.8201 0.876 0.000 0.000 0.000 0.124
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.1410 0.8821 0.000 0.060 0.940 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 5 0.3752 0.6680 0.292 0.000 0.000 0.000 0.708
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 5 0.5503 0.6127 0.300 0.000 0.072 0.008 0.620
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.4049 0.7206 0.164 0.000 0.780 0.000 0.056
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.6433 0.1360 0.312 0.000 0.000 0.488 0.200
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0290 0.9263 0.000 0.992 0.000 0.000 0.008
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.2471 0.8171 0.000 0.864 0.000 0.000 0.136
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0510 0.9080 0.000 0.000 0.000 0.984 0.016
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0290 0.8548 0.992 0.000 0.000 0.000 0.008
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.2605 0.8188 0.000 0.000 0.852 0.000 0.148
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 5 0.2690 0.7722 0.156 0.000 0.000 0.000 0.844
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.9287 0.000 1.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.9345 0.000 0.000 1.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.9191 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.9154 0.000 0.000 1.000 0.000 0.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0146 0.9158 0.000 0.000 0.996 0.000 0.000 0.004
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.4356 0.6300 0.608 0.000 0.000 0.000 0.360 0.032
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.4114 0.5499 0.532 0.000 0.004 0.000 0.460 0.004
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.2701 0.4134 0.864 0.000 0.000 0.028 0.004 0.104
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.3479 0.6701 0.012 0.008 0.768 0.000 0.000 0.212
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.4470 0.6284 0.604 0.000 0.000 0.000 0.356 0.040
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 6 0.3518 0.4873 0.012 0.000 0.000 0.256 0.000 0.732
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.4531 0.4261 0.004 0.352 0.000 0.036 0.608 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.4312 0.6278 0.604 0.000 0.000 0.000 0.368 0.028
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0632 0.8268 0.024 0.000 0.000 0.976 0.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 6 0.3371 0.4504 0.292 0.000 0.000 0.000 0.000 0.708
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.1049 0.8718 0.008 0.960 0.000 0.000 0.000 0.032
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.5682 0.5187 0.528 0.000 0.000 0.000 0.248 0.224
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.6010 0.4223 0.108 0.360 0.028 0.000 0.500 0.004
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.3370 0.6710 0.000 0.000 0.012 0.772 0.212 0.004
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.4904 0.6212 0.600 0.000 0.000 0.000 0.316 0.084
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.4470 0.6284 0.604 0.000 0.000 0.000 0.356 0.040
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.9154 0.000 0.000 1.000 0.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1285 0.8801 0.000 0.000 0.944 0.000 0.052 0.004
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3288 0.5316 0.000 0.724 0.000 0.000 0.276 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.3945 0.3926 0.000 0.380 0.000 0.008 0.612 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0146 0.9091 0.004 0.996 0.000 0.000 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0260 0.9068 0.000 0.992 0.000 0.000 0.008 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.4199 0.6210 0.600 0.000 0.000 0.000 0.380 0.020
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.2442 0.1618 0.144 0.000 0.000 0.000 0.852 0.004
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0405 0.9106 0.000 0.000 0.988 0.000 0.008 0.004
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 6 0.0260 0.6967 0.008 0.000 0.000 0.000 0.000 0.992
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.4900 0.4415 0.080 0.000 0.624 0.000 0.292 0.004
#> 36EDD202-A845-4CE7-95D5-A515C471262E 5 0.3747 0.3729 0.000 0.396 0.000 0.000 0.604 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 6 0.3464 0.5282 0.000 0.312 0.000 0.000 0.000 0.688
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.5598 0.2825 0.396 0.000 0.000 0.460 0.000 0.144
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.3872 -0.3989 0.392 0.000 0.000 0.000 0.604 0.004
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 5 0.4067 0.2407 0.000 0.444 0.000 0.008 0.548 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3547 0.4258 0.000 0.668 0.000 0.000 0.332 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 5 0.4880 0.3051 0.288 0.000 0.000 0.092 0.620 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 6 0.0937 0.6913 0.000 0.000 0.040 0.000 0.000 0.960
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.4954 0.3512 0.000 0.000 0.128 0.232 0.640 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0551 0.8277 0.000 0.000 0.004 0.984 0.008 0.004
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.3207 0.3935 0.044 0.124 0.000 0.000 0.828 0.004
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1610 0.8021 0.084 0.000 0.000 0.916 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4771 0.3498 0.728 0.000 0.000 0.124 0.036 0.112
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0146 0.9158 0.000 0.000 0.996 0.000 0.000 0.004
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.5597 0.3328 0.048 0.384 0.040 0.000 0.524 0.004
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.4131 0.2334 0.000 0.000 0.356 0.020 0.624 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.4209 0.6201 0.596 0.000 0.000 0.000 0.384 0.020
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3198 0.6091 0.000 0.260 0.740 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3769 0.0560 0.640 0.000 0.000 0.004 0.000 0.356
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.4856 0.1948 0.672 0.000 0.044 0.036 0.000 0.248
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.5333 -0.0593 0.480 0.000 0.432 0.000 0.080 0.008
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0146 0.9158 0.000 0.000 0.996 0.000 0.000 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0508 0.9102 0.000 0.000 0.984 0.000 0.012 0.004
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.3499 0.5912 0.320 0.000 0.000 0.680 0.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.5170 0.1432 0.620 0.000 0.000 0.204 0.000 0.176
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3126 0.6146 0.000 0.752 0.000 0.000 0.248 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2378 0.7583 0.152 0.000 0.000 0.848 0.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.0363 0.8266 0.000 0.000 0.000 0.988 0.012 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0146 0.9142 0.000 0.000 0.996 0.000 0.004 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0146 0.9158 0.000 0.000 0.996 0.000 0.000 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 6 0.5757 0.2024 0.000 0.320 0.000 0.000 0.192 0.488
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3888 0.5981 0.000 0.000 0.000 0.716 0.032 0.252
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.2489 0.5622 0.860 0.000 0.000 0.000 0.128 0.012
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0146 0.8295 0.004 0.000 0.000 0.996 0.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0146 0.9158 0.000 0.000 0.996 0.000 0.000 0.004
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.3810 0.2496 0.000 0.000 0.572 0.000 0.428 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 4 0.4358 0.6146 0.000 0.000 0.000 0.712 0.196 0.092
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0000 0.8292 0.000 0.000 0.000 1.000 0.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2664 0.7135 0.000 0.816 0.000 0.000 0.184 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 6 0.0713 0.6932 0.028 0.000 0.000 0.000 0.000 0.972
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0777 0.8227 0.000 0.000 0.000 0.972 0.024 0.004
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0000 0.9154 0.000 0.000 1.000 0.000 0.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0508 0.9102 0.000 0.000 0.984 0.000 0.012 0.004
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0146 0.9158 0.000 0.000 0.996 0.000 0.000 0.004
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.9123 0.000 1.000 0.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0146 0.9142 0.000 0.000 0.996 0.000 0.004 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0146 0.9158 0.000 0.000 0.996 0.000 0.000 0.004
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0260 0.8294 0.008 0.000 0.000 0.992 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17471 rows and 87 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.444 0.778 0.878 0.4396 0.513 0.513
#> 3 3 0.304 0.665 0.790 0.2093 0.926 0.856
#> 4 4 0.446 0.525 0.733 0.1665 0.924 0.835
#> 5 5 0.482 0.234 0.591 0.1159 0.822 0.598
#> 6 6 0.461 0.591 0.749 0.0723 0.778 0.424
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.3879 0.8956 0.076 0.924
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.4022 0.8944 0.080 0.920
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0672 0.7919 0.992 0.008
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.6712 0.8246 0.176 0.824
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0672 0.7919 0.992 0.008
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.7139 0.7896 0.196 0.804
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0672 0.7919 0.992 0.008
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.8909 0.6498 0.692 0.308
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 1 0.9977 0.2756 0.528 0.472
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0672 0.7919 0.992 0.008
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.3879 0.7891 0.924 0.076
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1633 0.7949 0.976 0.024
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.2043 0.8968 0.032 0.968
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0938 0.7931 0.988 0.012
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.5408 0.8691 0.124 0.876
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.9661 0.4896 0.608 0.392
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1184 0.7934 0.984 0.016
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0672 0.7919 0.992 0.008
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.3733 0.8963 0.072 0.928
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.7376 0.7762 0.208 0.792
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0376 0.8925 0.004 0.996
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0672 0.8856 0.008 0.992
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.7056 0.8025 0.192 0.808
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.8909 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1633 0.7929 0.976 0.024
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.5629 0.8621 0.132 0.868
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.3733 0.8981 0.072 0.928
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8909 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0672 0.7919 0.992 0.008
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.9933 0.3132 0.548 0.452
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0376 0.8922 0.004 0.996
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.6887 0.7585 0.184 0.816
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.4022 0.7875 0.920 0.080
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.7056 0.8025 0.192 0.808
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0672 0.8856 0.008 0.992
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.8909 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0376 0.8925 0.004 0.996
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.9754 0.4673 0.592 0.408
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.8207 0.6829 0.256 0.744
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.4815 0.8734 0.104 0.896
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.4161 0.8926 0.084 0.916
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.9209 0.6122 0.664 0.336
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.6973 0.8080 0.188 0.812
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.8909 0.6498 0.692 0.308
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0376 0.8925 0.004 0.996
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.8499 0.6780 0.724 0.276
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.3733 0.8963 0.072 0.928
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5519 0.8651 0.128 0.872
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.6973 0.8080 0.188 0.812
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1414 0.7934 0.980 0.020
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.2603 0.8974 0.044 0.956
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0376 0.8925 0.004 0.996
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0672 0.7919 0.992 0.008
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.8909 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.1633 0.7949 0.976 0.024
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.9866 0.3956 0.568 0.432
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.4022 0.8944 0.080 0.920
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.4298 0.8921 0.088 0.912
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.3733 0.7899 0.928 0.072
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.4022 0.7875 0.920 0.080
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.8909 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0672 0.8856 0.008 0.992
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.8861 0.6539 0.696 0.304
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.9977 -0.0934 0.472 0.528
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.7453 0.7731 0.212 0.788
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.8909 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.3431 0.8943 0.064 0.936
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0376 0.8925 0.004 0.996
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.9998 0.2512 0.508 0.492
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.7866 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.9129 0.6234 0.672 0.328
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8909 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.4022 0.8944 0.080 0.920
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.7376 0.7762 0.208 0.792
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.3274 0.7877 0.940 0.060
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.8909 0.6506 0.692 0.308
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8909 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.8909 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0672 0.7919 0.992 0.008
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.9635 0.4989 0.612 0.388
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.3431 0.8979 0.064 0.936
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.4022 0.8944 0.080 0.920
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.7139 0.7896 0.196 0.804
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8909 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.3879 0.8956 0.076 0.924
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.3733 0.8963 0.072 0.928
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.8909 0.6498 0.692 0.308
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.4399 0.815 0.188 0.812 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.4504 0.811 0.196 0.804 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 3 0.5178 0.901 0.256 0.000 0.744
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.6295 0.758 0.236 0.728 0.036
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.5706 -0.210 0.680 0.000 0.320
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.6224 0.701 0.296 0.688 0.016
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 3 0.5178 0.901 0.256 0.000 0.744
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.5115 0.657 0.796 0.188 0.016
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 1 0.5859 0.437 0.656 0.344 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 3 0.5178 0.901 0.256 0.000 0.744
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.3918 0.257 0.856 0.004 0.140
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.6735 -0.429 0.564 0.012 0.424
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.1860 0.816 0.052 0.948 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 3 0.5404 0.899 0.256 0.004 0.740
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.4974 0.789 0.236 0.764 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.6632 0.593 0.692 0.272 0.036
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 3 0.5656 0.891 0.264 0.008 0.728
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 3 0.5178 0.901 0.256 0.000 0.744
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.4399 0.814 0.188 0.812 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.5785 0.665 0.332 0.668 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0237 0.809 0.004 0.996 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.1877 0.785 0.012 0.956 0.032
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.6940 0.740 0.224 0.708 0.068
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.808 0.000 1.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 3 0.5992 0.877 0.268 0.016 0.716
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.4931 0.787 0.232 0.768 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.4645 0.819 0.176 0.816 0.008
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.808 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 3 0.4796 0.866 0.220 0.000 0.780
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.6955 0.452 0.636 0.332 0.032
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0424 0.810 0.008 0.992 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.5053 0.645 0.164 0.812 0.024
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.7124 0.100 0.672 0.056 0.272
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.6940 0.740 0.224 0.708 0.068
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.1877 0.785 0.012 0.956 0.032
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0424 0.804 0.008 0.992 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.1337 0.798 0.016 0.972 0.012
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.6193 0.574 0.692 0.292 0.016
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.6867 0.598 0.336 0.636 0.028
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.5891 0.782 0.200 0.764 0.036
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.3340 0.817 0.120 0.880 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.5109 0.655 0.780 0.212 0.008
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.6416 0.735 0.260 0.708 0.032
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.5115 0.657 0.796 0.188 0.016
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0237 0.809 0.004 0.996 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.5891 0.630 0.780 0.168 0.052
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.4399 0.814 0.188 0.812 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.4887 0.789 0.228 0.772 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.6416 0.735 0.260 0.708 0.032
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 3 0.5848 0.883 0.268 0.012 0.720
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.3816 0.822 0.148 0.852 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0237 0.809 0.004 0.996 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5882 -0.275 0.652 0.000 0.348
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.808 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.6357 -0.224 0.652 0.012 0.336
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.6143 0.529 0.684 0.304 0.012
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.4504 0.811 0.196 0.804 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.4784 0.808 0.200 0.796 0.004
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.3784 0.256 0.864 0.004 0.132
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.7124 0.100 0.672 0.056 0.272
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0747 0.813 0.016 0.984 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.1482 0.792 0.012 0.968 0.020
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.5384 0.655 0.788 0.188 0.024
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.9544 -0.205 0.364 0.440 0.196
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.6621 0.702 0.284 0.684 0.032
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0237 0.806 0.004 0.996 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.4521 0.813 0.180 0.816 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0237 0.809 0.004 0.996 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.7279 0.376 0.588 0.376 0.036
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.6204 0.537 0.424 0.000 0.576
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.5318 0.656 0.780 0.204 0.016
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.808 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.4504 0.811 0.196 0.804 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.5785 0.665 0.332 0.668 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.5431 0.451 0.284 0.000 0.716
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.6585 0.642 0.736 0.200 0.064
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0237 0.806 0.004 0.996 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0475 0.805 0.004 0.992 0.004
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 3 0.5178 0.901 0.256 0.000 0.744
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.6597 0.599 0.696 0.268 0.036
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.4235 0.818 0.176 0.824 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.4504 0.811 0.196 0.804 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.6224 0.701 0.296 0.688 0.016
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.808 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.4399 0.815 0.188 0.812 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.4399 0.814 0.188 0.812 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.5115 0.657 0.796 0.188 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.5429 0.6239 0.004 0.592 0.012 0.392
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.5244 0.6277 0.000 0.600 0.012 0.388
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.7901 1.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.6592 0.5300 0.048 0.532 0.016 0.404
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.7640 -0.1290 0.356 0.000 0.212 0.432
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.6186 0.4754 0.028 0.492 0.012 0.468
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.7901 1.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3050 0.6235 0.044 0.012 0.044 0.900
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.4358 0.5605 0.016 0.124 0.036 0.824
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.7901 1.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.6392 0.0449 0.068 0.000 0.404 0.528
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.6503 0.3666 0.640 0.000 0.164 0.196
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.2593 0.6780 0.004 0.892 0.000 0.104
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0188 0.7894 0.996 0.000 0.000 0.004
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.5689 0.5738 0.004 0.564 0.020 0.412
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.5689 0.6070 0.048 0.120 0.068 0.764
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0657 0.7851 0.984 0.000 0.004 0.012
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.7901 1.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.5217 0.6326 0.000 0.608 0.012 0.380
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 4 0.5628 -0.3704 0.000 0.420 0.024 0.556
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0336 0.6759 0.000 0.992 0.000 0.008
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.1211 0.6501 0.000 0.960 0.040 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.6993 0.5149 0.076 0.520 0.016 0.388
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0188 0.6748 0.000 0.996 0.000 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1229 0.7707 0.968 0.008 0.004 0.020
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.5589 0.5672 0.004 0.568 0.016 0.412
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.5498 0.6474 0.004 0.624 0.020 0.352
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0188 0.6748 0.000 0.996 0.000 0.004
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2647 0.6409 0.880 0.000 0.120 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 4 0.7190 0.4878 0.108 0.180 0.060 0.652
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.2021 0.6750 0.000 0.932 0.012 0.056
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3808 0.4895 0.184 0.808 0.004 0.004
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.7164 0.1775 0.296 0.004 0.148 0.552
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.7001 0.5126 0.076 0.516 0.016 0.392
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.1211 0.6501 0.000 0.960 0.040 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0188 0.6712 0.000 0.996 0.004 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0895 0.6644 0.000 0.976 0.020 0.004
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.5122 0.6220 0.044 0.112 0.048 0.796
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.6835 0.3986 0.048 0.476 0.024 0.452
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.6147 0.5711 0.000 0.564 0.056 0.380
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.4368 0.6600 0.004 0.748 0.004 0.244
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3210 0.6272 0.036 0.024 0.044 0.896
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.6655 0.4633 0.048 0.472 0.016 0.464
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.3050 0.6235 0.044 0.012 0.044 0.900
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0336 0.6759 0.000 0.992 0.000 0.008
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.3909 0.6010 0.080 0.012 0.052 0.856
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.5217 0.6326 0.000 0.608 0.012 0.380
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5580 0.5721 0.004 0.572 0.016 0.408
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.6655 0.4633 0.048 0.472 0.016 0.464
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1082 0.7756 0.972 0.004 0.004 0.020
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.4836 0.6578 0.000 0.672 0.008 0.320
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0336 0.6759 0.000 0.992 0.000 0.008
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.7629 0.0425 0.400 0.000 0.204 0.396
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0188 0.6748 0.000 0.996 0.000 0.004
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.7586 -0.1809 0.388 0.000 0.196 0.416
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.5634 0.5672 0.044 0.152 0.048 0.756
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.5244 0.6277 0.000 0.600 0.012 0.388
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.5429 0.6224 0.004 0.592 0.012 0.392
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.6804 0.0594 0.104 0.000 0.376 0.520
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.7164 0.1775 0.296 0.004 0.148 0.552
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.2760 0.6824 0.000 0.872 0.000 0.128
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0921 0.6583 0.000 0.972 0.028 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.3221 0.6209 0.048 0.012 0.048 0.892
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.8641 -0.0868 0.048 0.384 0.376 0.192
#> 1CC36859-357A-49E0-A367-4F57D47288BA 4 0.6780 -0.4507 0.056 0.452 0.016 0.476
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.6735 0.000 1.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.5298 0.6351 0.000 0.612 0.016 0.372
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0336 0.6759 0.000 0.992 0.000 0.008
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.6817 0.4756 0.044 0.224 0.076 0.656
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.6970 0.2281 0.576 0.000 0.256 0.168
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.3272 0.6257 0.036 0.020 0.052 0.892
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0188 0.6748 0.000 0.996 0.000 0.004
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.5244 0.6277 0.000 0.600 0.012 0.388
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 4 0.5628 -0.3704 0.000 0.420 0.024 0.556
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.4054 0.0000 0.188 0.000 0.796 0.016
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.4041 0.6060 0.044 0.020 0.084 0.852
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.6735 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0188 0.6720 0.000 0.996 0.004 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.7901 1.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.5635 0.6107 0.048 0.116 0.068 0.768
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.5339 0.6304 0.000 0.600 0.016 0.384
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.5244 0.6277 0.000 0.600 0.012 0.388
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.6186 0.4754 0.028 0.492 0.012 0.468
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0188 0.6748 0.000 0.996 0.000 0.004
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.5429 0.6239 0.004 0.592 0.012 0.392
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.5217 0.6326 0.000 0.608 0.012 0.380
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3050 0.6235 0.044 0.012 0.044 0.900
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.6868 -0.472 0.004 0.400 0.000 0.260 0.336
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.6658 -0.322 0.000 0.444 0.000 0.264 0.292
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0609 0.778 0.980 0.000 0.000 0.020 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.7668 0.856 0.048 0.292 0.000 0.300 0.360
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.7443 -0.352 0.336 0.000 0.036 0.384 0.244
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 4 0.7068 -0.503 0.012 0.360 0.000 0.372 0.256
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0609 0.778 0.980 0.000 0.000 0.020 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0324 0.479 0.000 0.000 0.004 0.992 0.004
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.3565 0.326 0.000 0.024 0.000 0.800 0.176
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0609 0.778 0.980 0.000 0.000 0.020 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.6916 -0.170 0.024 0.000 0.228 0.508 0.240
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.6255 0.526 0.616 0.004 0.016 0.176 0.188
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.3169 0.406 0.000 0.856 0.000 0.084 0.060
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0771 0.777 0.976 0.000 0.000 0.020 0.004
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.6898 0.831 0.004 0.300 0.000 0.296 0.400
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.4244 0.381 0.012 0.000 0.012 0.728 0.248
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1082 0.774 0.964 0.000 0.000 0.028 0.008
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0609 0.778 0.980 0.000 0.000 0.020 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.6631 -0.307 0.000 0.452 0.000 0.256 0.292
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 4 0.6642 -0.549 0.000 0.228 0.000 0.420 0.352
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0162 0.500 0.000 0.996 0.000 0.004 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.3731 0.423 0.000 0.800 0.040 0.000 0.160
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.7947 0.832 0.076 0.276 0.000 0.296 0.352
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0609 0.497 0.000 0.980 0.000 0.000 0.020
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1525 0.764 0.948 0.004 0.000 0.036 0.012
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.6941 0.847 0.004 0.324 0.000 0.304 0.368
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.6935 -0.326 0.004 0.460 0.008 0.228 0.300
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.501 0.000 1.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.5258 0.401 0.664 0.000 0.104 0.000 0.232
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 4 0.6661 0.235 0.076 0.060 0.016 0.612 0.236
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.2905 0.425 0.000 0.868 0.000 0.036 0.096
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3280 0.373 0.176 0.812 0.000 0.000 0.012
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.6701 -0.120 0.268 0.000 0.016 0.520 0.196
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.7929 0.836 0.076 0.268 0.000 0.292 0.364
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.3731 0.423 0.000 0.800 0.040 0.000 0.160
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.2848 0.457 0.000 0.840 0.004 0.000 0.156
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3264 0.455 0.000 0.836 0.020 0.004 0.140
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.2756 0.416 0.000 0.024 0.004 0.880 0.092
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.7070 -0.464 0.012 0.348 0.004 0.420 0.216
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.7415 0.580 0.000 0.304 0.032 0.272 0.392
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.6459 -0.371 0.004 0.508 0.000 0.184 0.304
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.1121 0.479 0.000 0.000 0.000 0.956 0.044
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 4 0.7682 -0.662 0.048 0.292 0.000 0.344 0.316
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0324 0.479 0.000 0.000 0.004 0.992 0.004
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0162 0.500 0.000 0.996 0.000 0.004 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.1682 0.451 0.032 0.000 0.012 0.944 0.012
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.6631 -0.307 0.000 0.452 0.000 0.256 0.292
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.6936 0.846 0.004 0.324 0.000 0.300 0.372
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.7682 -0.662 0.048 0.292 0.000 0.344 0.316
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1364 0.768 0.952 0.000 0.000 0.036 0.012
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.6301 -0.229 0.000 0.532 0.000 0.216 0.252
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0324 0.500 0.000 0.992 0.000 0.004 0.004
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.7155 0.281 0.376 0.000 0.016 0.348 0.260
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0609 0.497 0.000 0.980 0.000 0.000 0.020
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.7302 0.266 0.364 0.004 0.016 0.352 0.264
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.4674 0.326 0.012 0.024 0.004 0.712 0.248
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.6658 -0.322 0.000 0.444 0.000 0.264 0.292
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.6810 -0.336 0.004 0.436 0.000 0.264 0.296
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.7392 -0.138 0.076 0.000 0.172 0.504 0.248
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.6453 -0.055 0.268 0.000 0.016 0.556 0.160
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.4016 0.305 0.000 0.796 0.000 0.092 0.112
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3412 0.444 0.000 0.820 0.028 0.000 0.152
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0671 0.475 0.000 0.000 0.004 0.980 0.016
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.8355 0.381 0.000 0.232 0.372 0.224 0.172
#> 1CC36859-357A-49E0-A367-4F57D47288BA 4 0.7655 -0.637 0.048 0.280 0.000 0.368 0.304
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.2732 0.457 0.000 0.840 0.000 0.000 0.160
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.6815 -0.335 0.000 0.432 0.004 0.260 0.304
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0162 0.500 0.000 0.996 0.000 0.004 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.5849 0.304 0.008 0.080 0.016 0.640 0.256
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.7565 0.431 0.520 0.000 0.176 0.164 0.140
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1251 0.481 0.000 0.000 0.008 0.956 0.036
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.501 0.000 1.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.6658 -0.322 0.000 0.444 0.000 0.264 0.292
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 4 0.6642 -0.549 0.000 0.228 0.000 0.420 0.352
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.0609 0.358 0.020 0.000 0.980 0.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.2359 0.453 0.000 0.000 0.060 0.904 0.036
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.2732 0.457 0.000 0.840 0.000 0.000 0.160
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2890 0.456 0.000 0.836 0.004 0.000 0.160
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0609 0.778 0.980 0.000 0.000 0.020 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.4217 0.386 0.012 0.000 0.012 0.732 0.244
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.6718 -0.471 0.000 0.412 0.000 0.260 0.328
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.6658 -0.322 0.000 0.444 0.000 0.264 0.292
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 4 0.7068 -0.503 0.012 0.360 0.000 0.372 0.256
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.501 0.000 1.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.6868 -0.472 0.004 0.400 0.000 0.260 0.336
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.6641 -0.313 0.000 0.448 0.000 0.256 0.296
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0324 0.481 0.000 0.000 0.004 0.992 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.2660 0.76725 0.004 0.100 0.872 0.016 0.008 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1866 0.75630 0.000 0.084 0.908 0.008 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.84554 1.000 0.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.6060 0.62503 0.048 0.164 0.648 0.096 0.044 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.6302 -0.03917 0.348 0.000 0.000 0.452 0.172 0.028
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.3665 0.70559 0.008 0.036 0.812 0.128 0.016 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.84554 1.000 0.000 0.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.2700 0.59945 0.000 0.000 0.156 0.836 0.004 0.004
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.4046 0.42940 0.000 0.008 0.368 0.620 0.004 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.84554 1.000 0.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.5503 0.05703 0.020 0.000 0.000 0.604 0.120 0.256
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.5547 0.36235 0.624 0.000 0.012 0.232 0.120 0.012
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.4488 0.65444 0.000 0.652 0.304 0.032 0.012 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0291 0.84382 0.992 0.000 0.004 0.000 0.004 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.5099 0.65493 0.004 0.160 0.704 0.088 0.044 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.5605 0.52434 0.012 0.016 0.280 0.612 0.072 0.008
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0665 0.83839 0.980 0.000 0.008 0.008 0.004 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.84554 1.000 0.000 0.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.1958 0.74589 0.000 0.100 0.896 0.004 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3460 0.66325 0.000 0.036 0.796 0.164 0.004 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3398 0.76276 0.000 0.740 0.252 0.000 0.008 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.2789 0.58849 0.000 0.872 0.020 0.004 0.016 0.088
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.6379 0.60730 0.072 0.176 0.620 0.088 0.044 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.3404 0.75927 0.000 0.760 0.224 0.000 0.016 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0909 0.82102 0.968 0.000 0.020 0.012 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.5316 0.64834 0.004 0.176 0.680 0.096 0.044 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.2939 0.73085 0.004 0.140 0.840 0.004 0.008 0.004
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.3445 0.76218 0.000 0.744 0.244 0.000 0.012 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 5 0.3819 0.00000 0.316 0.000 0.000 0.000 0.672 0.012
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 4 0.6495 0.34089 0.076 0.020 0.348 0.500 0.052 0.004
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4397 0.59960 0.000 0.632 0.336 0.012 0.020 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.5878 0.63726 0.100 0.628 0.176 0.000 0.096 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.5812 0.27764 0.280 0.000 0.028 0.588 0.092 0.012
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.6379 0.61036 0.072 0.168 0.624 0.088 0.048 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.2789 0.58849 0.000 0.872 0.020 0.004 0.016 0.088
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.2527 0.66823 0.000 0.892 0.040 0.004 0.008 0.056
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3183 0.65429 0.000 0.852 0.068 0.004 0.012 0.064
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.3512 0.55980 0.000 0.008 0.272 0.720 0.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.4423 0.62093 0.008 0.024 0.740 0.196 0.028 0.004
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.7291 0.40240 0.000 0.236 0.512 0.092 0.072 0.088
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.5733 0.32296 0.004 0.420 0.480 0.060 0.036 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3669 0.59427 0.000 0.000 0.208 0.760 0.004 0.028
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.4337 0.71497 0.048 0.076 0.780 0.092 0.004 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.2700 0.59945 0.000 0.000 0.156 0.836 0.004 0.004
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.3398 0.76065 0.000 0.740 0.252 0.000 0.008 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.3763 0.59315 0.028 0.000 0.152 0.796 0.012 0.012
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.1958 0.74589 0.000 0.100 0.896 0.004 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.5272 0.65110 0.004 0.176 0.684 0.092 0.044 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4337 0.71497 0.048 0.076 0.780 0.092 0.004 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0820 0.82642 0.972 0.000 0.016 0.012 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.2772 0.66237 0.000 0.180 0.816 0.004 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.3421 0.76105 0.000 0.736 0.256 0.000 0.008 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.6119 -0.12546 0.384 0.000 0.000 0.420 0.184 0.012
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.3404 0.75927 0.000 0.760 0.224 0.000 0.016 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.6435 -0.09760 0.372 0.000 0.012 0.416 0.188 0.012
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.5115 0.41553 0.012 0.012 0.352 0.584 0.040 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.1866 0.75630 0.000 0.084 0.908 0.008 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.2162 0.75299 0.004 0.088 0.896 0.012 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.6374 0.07356 0.092 0.000 0.000 0.568 0.156 0.184
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.6045 0.32307 0.280 0.000 0.052 0.576 0.080 0.012
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.3991 0.32617 0.000 0.524 0.472 0.000 0.004 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2917 0.63091 0.000 0.868 0.040 0.004 0.012 0.076
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.3118 0.59862 0.000 0.000 0.156 0.820 0.012 0.012
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 6 0.7092 0.26188 0.000 0.308 0.048 0.160 0.032 0.452
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.4499 0.70421 0.048 0.068 0.764 0.116 0.004 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.2384 0.66057 0.000 0.900 0.032 0.004 0.008 0.056
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.4481 0.63027 0.000 0.176 0.736 0.032 0.000 0.056
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3398 0.76276 0.000 0.740 0.252 0.000 0.008 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.7210 0.40212 0.008 0.108 0.196 0.552 0.072 0.064
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.6781 0.25873 0.536 0.004 0.000 0.176 0.156 0.128
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.3752 0.59415 0.000 0.000 0.200 0.760 0.004 0.036
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.3470 0.76108 0.000 0.740 0.248 0.000 0.012 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.1866 0.75630 0.000 0.084 0.908 0.008 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.3460 0.66325 0.000 0.036 0.796 0.164 0.004 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.2454 -0.00149 0.020 0.008 0.000 0.000 0.088 0.884
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.4437 0.57186 0.000 0.000 0.188 0.716 0.004 0.092
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.2384 0.66162 0.000 0.900 0.032 0.004 0.008 0.056
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2445 0.65842 0.000 0.896 0.032 0.004 0.008 0.060
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0146 0.84421 0.996 0.000 0.000 0.000 0.004 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.5587 0.53054 0.012 0.016 0.276 0.616 0.072 0.008
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.2501 0.76425 0.000 0.108 0.872 0.016 0.004 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.1866 0.75630 0.000 0.084 0.908 0.008 0.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.3665 0.70559 0.008 0.036 0.812 0.128 0.016 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.3470 0.76108 0.000 0.740 0.248 0.000 0.012 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.2660 0.76725 0.004 0.100 0.872 0.016 0.008 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.1908 0.74770 0.000 0.096 0.900 0.004 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.2848 0.59872 0.000 0.000 0.160 0.828 0.004 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
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 17471 rows and 87 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 0.974 0.989 0.4947 0.505 0.505
#> 3 3 0.525 0.665 0.764 0.2851 0.636 0.406
#> 4 4 0.829 0.838 0.893 0.1505 0.805 0.523
#> 5 5 0.761 0.649 0.799 0.0761 0.921 0.721
#> 6 6 0.745 0.670 0.810 0.0461 0.909 0.631
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0376 0.988 0.004 0.996
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0376 0.988 0.004 0.996
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.988 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.988 1.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.988 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0376 0.988 0.004 0.996
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.988 1.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0000 0.988 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0376 0.988 0.004 0.996
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.988 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.988 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.988 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.988 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.988 1.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0376 0.988 0.004 0.996
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0000 0.988 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.988 1.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.988 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0376 0.988 0.004 0.996
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.0376 0.988 0.004 0.996
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.988 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.988 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0376 0.988 0.004 0.996
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.988 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.988 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0376 0.988 0.004 0.996
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0000 0.988 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.988 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.988 1.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.0000 0.988 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.988 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.988 0.000 1.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.988 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.9686 0.332 0.604 0.396
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.988 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.988 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.988 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.988 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.988 1.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0376 0.988 0.004 0.996
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.988 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.1843 0.960 0.972 0.028
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0376 0.988 0.004 0.996
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.988 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.988 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.988 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0376 0.988 0.004 0.996
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0376 0.988 0.004 0.996
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.6048 0.831 0.148 0.852
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.988 1.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.988 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.988 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.988 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.988 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.988 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.988 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0376 0.988 0.004 0.996
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0376 0.988 0.004 0.996
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.988 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.988 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.988 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.988 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0000 0.988 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.7219 0.750 0.200 0.800
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.5519 0.856 0.128 0.872
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.988 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0376 0.988 0.004 0.996
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.988 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0000 0.988 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.988 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.988 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.988 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0376 0.988 0.004 0.996
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0376 0.988 0.004 0.996
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.988 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0000 0.988 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.988 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.988 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.988 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.988 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0376 0.988 0.004 0.996
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0376 0.988 0.004 0.996
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0376 0.988 0.004 0.996
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.988 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0376 0.988 0.004 0.996
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0376 0.988 0.004 0.996
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0000 0.988 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.5365 0.6548 0.004 0.252 0.744
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.5365 0.6877 0.004 0.252 0.744
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0237 0.8636 0.996 0.000 0.004
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.6654 0.2558 0.456 0.008 0.536
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.3619 0.8119 0.864 0.000 0.136
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.5365 0.6877 0.004 0.252 0.744
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0237 0.8636 0.996 0.000 0.004
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.5497 0.3526 0.292 0.000 0.708
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.5158 0.6952 0.004 0.232 0.764
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0237 0.8636 0.996 0.000 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.6204 0.5060 0.576 0.000 0.424
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0237 0.8636 0.996 0.000 0.004
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.5926 0.2756 0.000 0.644 0.356
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0237 0.8636 0.996 0.000 0.004
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.5517 0.6726 0.004 0.268 0.728
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.3038 0.5742 0.104 0.000 0.896
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0237 0.8636 0.996 0.000 0.004
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0237 0.8636 0.996 0.000 0.004
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.5690 0.6493 0.004 0.288 0.708
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1860 0.6683 0.000 0.052 0.948
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.8933 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.1643 0.8691 0.000 0.956 0.044
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.6520 -0.1720 0.004 0.508 0.488
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.8933 0.000 1.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0592 0.8619 0.988 0.000 0.012
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.5201 0.6943 0.004 0.236 0.760
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.6483 0.3091 0.004 0.452 0.544
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8933 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0237 0.8636 0.996 0.000 0.004
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.6026 0.3546 0.376 0.000 0.624
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.8933 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.8933 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.6204 0.5060 0.576 0.000 0.424
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.7699 0.3390 0.420 0.048 0.532
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.1643 0.8691 0.000 0.956 0.044
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.1529 0.8718 0.000 0.960 0.040
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.1529 0.8718 0.000 0.960 0.040
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.5497 0.3526 0.292 0.000 0.708
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.6026 0.3134 0.376 0.000 0.624
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.4452 0.6897 0.000 0.192 0.808
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.5810 0.3484 0.000 0.664 0.336
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.1753 0.6311 0.048 0.000 0.952
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.6460 0.3429 0.004 0.440 0.556
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.5497 0.3526 0.292 0.000 0.708
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.8933 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.5859 0.6412 0.656 0.000 0.344
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.5902 0.6091 0.004 0.316 0.680
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.6489 -0.0505 0.004 0.540 0.456
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4293 0.6968 0.004 0.164 0.832
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0237 0.8636 0.996 0.000 0.004
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0747 0.8810 0.000 0.984 0.016
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.8933 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3482 0.8159 0.872 0.000 0.128
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.8933 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.5098 0.7393 0.752 0.000 0.248
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.2796 0.6013 0.092 0.000 0.908
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.5365 0.6877 0.004 0.252 0.744
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.5365 0.6877 0.004 0.252 0.744
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.5968 0.6101 0.636 0.000 0.364
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.6008 0.6018 0.628 0.000 0.372
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.8933 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.1643 0.8691 0.000 0.956 0.044
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.5397 0.3764 0.280 0.000 0.720
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.7741 0.4075 0.068 0.324 0.608
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.5365 0.6877 0.004 0.252 0.744
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.8933 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.4346 0.6975 0.000 0.184 0.816
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.8933 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.4842 0.4215 0.224 0.000 0.776
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0424 0.8625 0.992 0.000 0.008
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.5431 0.3690 0.284 0.000 0.716
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8933 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.5365 0.6877 0.004 0.252 0.744
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4974 0.6945 0.000 0.236 0.764
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.1860 0.8319 0.948 0.000 0.052
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.5497 0.3526 0.292 0.000 0.708
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8933 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.1643 0.8691 0.000 0.956 0.044
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0237 0.8636 0.996 0.000 0.004
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.5397 0.3764 0.280 0.000 0.720
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.5404 0.6845 0.004 0.256 0.740
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.5158 0.6955 0.004 0.232 0.764
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.5201 0.6943 0.004 0.236 0.760
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8933 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.5690 0.6493 0.004 0.288 0.708
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.5178 0.6857 0.000 0.256 0.744
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.5397 0.3764 0.280 0.000 0.720
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1888 0.867 0.000 0.044 0.940 0.016
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0592 0.889 0.000 0.000 0.984 0.016
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1557 0.949 0.944 0.000 0.000 0.056
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.5593 0.637 0.260 0.004 0.688 0.048
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.5203 0.233 0.416 0.008 0.000 0.576
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0592 0.889 0.000 0.000 0.984 0.016
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.1743 0.949 0.940 0.004 0.000 0.056
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.1398 0.896 0.004 0.000 0.040 0.956
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.2636 0.878 0.020 0.012 0.916 0.052
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.1557 0.949 0.944 0.000 0.000 0.056
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2125 0.870 0.076 0.004 0.000 0.920
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1637 0.948 0.940 0.000 0.000 0.060
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.5920 0.335 0.016 0.368 0.596 0.020
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1847 0.947 0.940 0.004 0.004 0.052
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.2392 0.875 0.024 0.012 0.928 0.036
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.3325 0.855 0.008 0.044 0.064 0.884
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1847 0.947 0.940 0.004 0.004 0.052
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1743 0.949 0.940 0.004 0.000 0.056
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0336 0.888 0.000 0.000 0.992 0.008
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1970 0.879 0.008 0.000 0.932 0.060
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.2748 0.940 0.020 0.904 0.072 0.004
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.2929 0.891 0.028 0.908 0.024 0.040
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.2605 0.866 0.024 0.040 0.920 0.016
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2238 0.944 0.004 0.920 0.072 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1930 0.946 0.936 0.004 0.004 0.056
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.2484 0.875 0.024 0.012 0.924 0.040
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0376 0.885 0.004 0.000 0.992 0.004
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.2053 0.944 0.004 0.924 0.072 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1890 0.946 0.936 0.008 0.000 0.056
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.7330 0.400 0.336 0.048 0.552 0.064
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.3731 0.911 0.032 0.856 0.104 0.008
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.1902 0.941 0.004 0.932 0.064 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.2197 0.868 0.080 0.004 0.000 0.916
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.6178 0.356 0.408 0.004 0.544 0.044
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.2929 0.891 0.028 0.908 0.024 0.040
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.2352 0.927 0.016 0.928 0.044 0.012
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2673 0.923 0.016 0.916 0.048 0.020
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1585 0.896 0.004 0.004 0.040 0.952
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.7369 0.434 0.212 0.008 0.564 0.216
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.4081 0.841 0.032 0.052 0.856 0.060
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.5328 0.476 0.020 0.316 0.660 0.004
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2654 0.844 0.000 0.004 0.108 0.888
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.2418 0.875 0.024 0.016 0.928 0.032
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1585 0.896 0.004 0.004 0.040 0.952
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1867 0.944 0.000 0.928 0.072 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.2197 0.868 0.080 0.004 0.000 0.916
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0469 0.888 0.000 0.000 0.988 0.012
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.2432 0.871 0.024 0.028 0.928 0.020
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.2335 0.878 0.020 0.000 0.920 0.060
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1994 0.944 0.936 0.004 0.008 0.052
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.5330 0.203 0.004 0.516 0.476 0.004
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.1867 0.944 0.000 0.928 0.072 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5137 0.153 0.544 0.004 0.000 0.452
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.2238 0.944 0.004 0.920 0.072 0.004
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.2714 0.843 0.112 0.004 0.000 0.884
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.5125 0.451 0.004 0.004 0.616 0.376
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0592 0.889 0.000 0.000 0.984 0.016
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0592 0.889 0.000 0.000 0.984 0.016
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2412 0.864 0.084 0.008 0.000 0.908
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.2197 0.868 0.080 0.004 0.000 0.916
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.2238 0.944 0.004 0.920 0.072 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2437 0.900 0.024 0.928 0.024 0.024
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.1398 0.896 0.004 0.000 0.040 0.956
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.5618 0.535 0.028 0.288 0.012 0.672
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.1124 0.888 0.012 0.004 0.972 0.012
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.2053 0.944 0.004 0.924 0.072 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1406 0.881 0.000 0.024 0.960 0.016
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.2515 0.942 0.012 0.912 0.072 0.004
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3005 0.866 0.008 0.044 0.048 0.900
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1557 0.949 0.944 0.000 0.000 0.056
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1489 0.895 0.000 0.004 0.044 0.952
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.2053 0.944 0.004 0.924 0.072 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0592 0.889 0.000 0.000 0.984 0.016
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.1854 0.883 0.012 0.000 0.940 0.048
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.3577 0.863 0.868 0.056 0.004 0.072
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1639 0.896 0.008 0.004 0.036 0.952
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.2053 0.944 0.004 0.924 0.072 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.2432 0.901 0.024 0.928 0.020 0.028
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1557 0.949 0.944 0.000 0.000 0.056
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1474 0.893 0.000 0.000 0.052 0.948
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0592 0.889 0.000 0.000 0.984 0.016
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0592 0.889 0.000 0.000 0.984 0.016
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0592 0.889 0.000 0.000 0.984 0.016
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.2053 0.944 0.004 0.924 0.072 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0188 0.886 0.004 0.000 0.996 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0592 0.889 0.000 0.000 0.984 0.016
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.1489 0.895 0.000 0.004 0.044 0.952
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0609 0.6988 0.000 0.000 0.980 0.000 0.020
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0290 0.7034 0.000 0.000 0.992 0.000 0.008
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0671 0.8687 0.980 0.000 0.000 0.004 0.016
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.6659 0.5825 0.268 0.000 0.204 0.012 0.516
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.5714 0.3671 0.312 0.000 0.000 0.580 0.108
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0510 0.7017 0.000 0.000 0.984 0.000 0.016
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0865 0.8678 0.972 0.000 0.000 0.004 0.024
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.1041 0.8653 0.000 0.000 0.004 0.964 0.032
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.6029 -0.1763 0.000 0.020 0.512 0.068 0.400
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0671 0.8687 0.980 0.000 0.000 0.004 0.016
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2189 0.8495 0.012 0.000 0.000 0.904 0.084
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2376 0.8389 0.904 0.000 0.000 0.044 0.052
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.6635 0.0322 0.004 0.508 0.168 0.008 0.312
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1124 0.8661 0.960 0.000 0.000 0.004 0.036
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.5084 -0.2545 0.000 0.020 0.488 0.008 0.484
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.3876 0.7675 0.000 0.000 0.032 0.776 0.192
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.2286 0.8326 0.888 0.000 0.000 0.004 0.108
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1205 0.8654 0.956 0.000 0.000 0.004 0.040
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0290 0.7024 0.000 0.000 0.992 0.000 0.008
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3628 0.4762 0.000 0.000 0.772 0.012 0.216
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.1990 0.8747 0.004 0.920 0.008 0.000 0.068
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4385 0.7123 0.004 0.672 0.012 0.000 0.312
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.5708 0.2185 0.000 0.060 0.452 0.008 0.480
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0324 0.8883 0.000 0.992 0.004 0.000 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.3391 0.7561 0.800 0.000 0.000 0.012 0.188
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.5180 -0.2622 0.000 0.020 0.488 0.012 0.480
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0162 0.7041 0.000 0.000 0.996 0.000 0.004
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0162 0.8887 0.000 0.996 0.004 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2407 0.8366 0.896 0.004 0.000 0.012 0.088
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.7519 0.4933 0.220 0.000 0.248 0.064 0.468
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4937 0.4544 0.004 0.604 0.028 0.000 0.364
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.1282 0.8731 0.000 0.952 0.000 0.004 0.044
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.1251 0.8572 0.008 0.000 0.000 0.956 0.036
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.6584 0.5815 0.280 0.000 0.200 0.008 0.512
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.4162 0.7203 0.004 0.680 0.004 0.000 0.312
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.1365 0.8852 0.004 0.952 0.004 0.000 0.040
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2877 0.8438 0.004 0.848 0.004 0.000 0.144
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1430 0.8599 0.000 0.000 0.004 0.944 0.052
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.6682 0.2133 0.092 0.004 0.628 0.164 0.112
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.4313 0.4052 0.000 0.000 0.356 0.008 0.636
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 5 0.7044 0.3483 0.004 0.276 0.336 0.004 0.380
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3303 0.8176 0.000 0.000 0.076 0.848 0.076
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.5159 -0.2468 0.000 0.024 0.496 0.008 0.472
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0865 0.8632 0.000 0.000 0.004 0.972 0.024
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0671 0.8882 0.000 0.980 0.004 0.000 0.016
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.1082 0.8585 0.008 0.000 0.000 0.964 0.028
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.7036 0.000 0.000 1.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.5232 -0.2584 0.000 0.028 0.492 0.008 0.472
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.5096 -0.1550 0.000 0.000 0.520 0.036 0.444
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2930 0.7841 0.832 0.000 0.000 0.004 0.164
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3628 0.3852 0.000 0.216 0.772 0.000 0.012
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.1116 0.8860 0.004 0.964 0.004 0.000 0.028
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5626 0.1625 0.504 0.000 0.000 0.420 0.076
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0324 0.8883 0.000 0.992 0.004 0.000 0.004
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.2853 0.8161 0.052 0.000 0.000 0.876 0.072
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.6022 0.1173 0.000 0.000 0.324 0.540 0.136
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0290 0.7024 0.000 0.000 0.992 0.000 0.008
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0162 0.7025 0.000 0.000 0.996 0.000 0.004
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2654 0.8380 0.032 0.000 0.000 0.884 0.084
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.1082 0.8585 0.008 0.000 0.000 0.964 0.028
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0324 0.8883 0.000 0.992 0.004 0.000 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3647 0.7915 0.004 0.764 0.004 0.000 0.228
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.1357 0.8613 0.000 0.000 0.004 0.948 0.048
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.6467 0.5070 0.000 0.116 0.024 0.532 0.328
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.3876 0.2985 0.000 0.000 0.684 0.000 0.316
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0324 0.8890 0.000 0.992 0.004 0.000 0.004
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0404 0.7005 0.000 0.000 0.988 0.000 0.012
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1443 0.8834 0.004 0.948 0.004 0.000 0.044
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3536 0.7828 0.000 0.000 0.032 0.812 0.156
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0865 0.8644 0.972 0.000 0.000 0.004 0.024
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.2068 0.8564 0.000 0.000 0.004 0.904 0.092
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0162 0.8887 0.000 0.996 0.004 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0162 0.7025 0.000 0.000 0.996 0.000 0.004
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4108 0.2985 0.000 0.000 0.684 0.008 0.308
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.4240 0.6283 0.684 0.000 0.008 0.004 0.304
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.2286 0.8542 0.000 0.000 0.004 0.888 0.108
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0162 0.8887 0.000 0.996 0.004 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.3317 0.8080 0.004 0.804 0.004 0.000 0.188
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1205 0.8594 0.956 0.000 0.000 0.004 0.040
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1704 0.8574 0.000 0.000 0.004 0.928 0.068
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0703 0.6979 0.000 0.000 0.976 0.000 0.024
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0404 0.7035 0.000 0.000 0.988 0.000 0.012
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0510 0.7030 0.000 0.000 0.984 0.000 0.016
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0162 0.8887 0.000 0.996 0.004 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0794 0.6956 0.000 0.000 0.972 0.000 0.028
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0290 0.7024 0.000 0.000 0.992 0.000 0.008
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.1282 0.8642 0.000 0.000 0.004 0.952 0.044
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0972 0.8783 0.000 0.000 0.964 0.000 0.008 0.028
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0405 0.8832 0.000 0.000 0.988 0.000 0.008 0.004
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0260 0.8864 0.992 0.000 0.000 0.000 0.000 0.008
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.3744 0.6905 0.116 0.000 0.072 0.000 0.800 0.012
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.5641 0.4348 0.212 0.000 0.000 0.608 0.024 0.156
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0603 0.8792 0.000 0.000 0.980 0.000 0.004 0.016
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0717 0.8874 0.976 0.000 0.000 0.000 0.016 0.008
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3248 0.7628 0.000 0.000 0.004 0.828 0.052 0.116
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.5579 0.6762 0.000 0.000 0.188 0.060 0.648 0.104
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0146 0.8870 0.996 0.000 0.000 0.000 0.000 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.3320 0.6970 0.000 0.000 0.000 0.772 0.016 0.212
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3590 0.7969 0.828 0.000 0.000 0.072 0.040 0.060
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 5 0.6051 0.1602 0.000 0.388 0.056 0.000 0.476 0.080
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1245 0.8871 0.952 0.000 0.000 0.000 0.032 0.016
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.3261 0.7368 0.000 0.000 0.204 0.000 0.780 0.016
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.5097 0.5775 0.000 0.000 0.016 0.600 0.064 0.320
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1829 0.8716 0.920 0.000 0.000 0.000 0.056 0.024
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0820 0.8870 0.972 0.000 0.000 0.000 0.016 0.012
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0508 0.8828 0.000 0.000 0.984 0.000 0.004 0.012
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.4346 0.2503 0.000 0.000 0.632 0.004 0.336 0.028
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3563 0.7845 0.000 0.796 0.000 0.000 0.072 0.132
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 6 0.4584 -0.0315 0.000 0.444 0.004 0.000 0.028 0.524
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.3564 0.7396 0.000 0.040 0.136 0.000 0.808 0.016
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1616 0.8293 0.000 0.932 0.000 0.000 0.020 0.048
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.3790 0.6627 0.716 0.000 0.000 0.004 0.264 0.016
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.2664 0.7439 0.000 0.000 0.184 0.000 0.816 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0692 0.8817 0.000 0.000 0.976 0.000 0.004 0.020
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8408 0.000 1.000 0.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2846 0.8387 0.856 0.000 0.000 0.000 0.060 0.084
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.8069 0.3398 0.080 0.000 0.156 0.108 0.404 0.252
#> 36EDD202-A845-4CE7-95D5-A515C471262E 5 0.5500 0.2513 0.000 0.360 0.008 0.000 0.524 0.108
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2389 0.8128 0.000 0.888 0.000 0.000 0.052 0.060
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0767 0.7510 0.004 0.000 0.000 0.976 0.008 0.012
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.3646 0.6914 0.116 0.000 0.072 0.000 0.804 0.008
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 6 0.4399 -0.0782 0.000 0.460 0.000 0.000 0.024 0.516
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.2999 0.7938 0.000 0.836 0.000 0.000 0.040 0.124
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.4215 0.6366 0.000 0.700 0.000 0.000 0.056 0.244
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.3104 0.7609 0.000 0.000 0.004 0.844 0.084 0.068
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.7276 0.3597 0.092 0.000 0.548 0.140 0.084 0.136
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.5164 0.5221 0.000 0.000 0.096 0.004 0.584 0.316
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 5 0.4936 0.6748 0.000 0.148 0.120 0.000 0.704 0.028
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.5746 0.6879 0.000 0.000 0.104 0.648 0.100 0.148
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.3231 0.7363 0.000 0.000 0.200 0.000 0.784 0.016
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0405 0.7562 0.000 0.000 0.000 0.988 0.004 0.008
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1863 0.8317 0.000 0.920 0.000 0.000 0.036 0.044
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0551 0.7515 0.004 0.000 0.000 0.984 0.004 0.008
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0405 0.8828 0.000 0.000 0.988 0.000 0.004 0.008
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.2933 0.7386 0.000 0.004 0.200 0.000 0.796 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.4369 0.7283 0.000 0.000 0.176 0.020 0.740 0.064
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.3136 0.7557 0.796 0.000 0.000 0.000 0.188 0.016
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3348 0.6945 0.000 0.152 0.812 0.000 0.016 0.020
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.2629 0.8233 0.000 0.872 0.000 0.000 0.060 0.068
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.5985 -0.1163 0.420 0.000 0.000 0.448 0.040 0.092
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1003 0.8398 0.000 0.964 0.000 0.000 0.016 0.020
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.3020 0.6976 0.032 0.000 0.000 0.864 0.040 0.064
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.5784 0.5027 0.000 0.000 0.164 0.628 0.152 0.056
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0146 0.8832 0.000 0.000 0.996 0.000 0.000 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0260 0.8830 0.000 0.000 0.992 0.000 0.000 0.008
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.3104 0.6777 0.000 0.000 0.000 0.800 0.016 0.184
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0551 0.7515 0.004 0.000 0.000 0.984 0.004 0.008
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0914 0.8363 0.000 0.968 0.000 0.000 0.016 0.016
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.4747 0.3167 0.000 0.568 0.000 0.000 0.056 0.376
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.3466 0.7600 0.000 0.000 0.004 0.816 0.084 0.096
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 6 0.4342 -0.1157 0.000 0.016 0.004 0.272 0.020 0.688
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.4702 -0.2130 0.000 0.000 0.496 0.000 0.460 0.044
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0508 0.8408 0.000 0.984 0.000 0.000 0.004 0.012
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0405 0.8824 0.000 0.000 0.988 0.000 0.004 0.008
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3227 0.7986 0.000 0.824 0.000 0.000 0.060 0.116
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.4719 0.5896 0.000 0.000 0.016 0.636 0.040 0.308
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.2068 0.8484 0.904 0.000 0.000 0.008 0.008 0.080
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.4238 0.7408 0.000 0.000 0.000 0.728 0.092 0.180
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0146 0.8403 0.000 0.996 0.000 0.000 0.000 0.004
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0405 0.8828 0.000 0.000 0.988 0.000 0.004 0.008
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 5 0.4596 0.2971 0.000 0.000 0.460 0.004 0.508 0.028
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.4504 -0.2146 0.432 0.000 0.004 0.000 0.024 0.540
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.4486 0.7334 0.000 0.000 0.000 0.696 0.096 0.208
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0146 0.8403 0.000 0.996 0.000 0.000 0.000 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.4224 0.0951 0.000 0.552 0.000 0.000 0.016 0.432
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1995 0.8629 0.912 0.000 0.000 0.000 0.036 0.052
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.4032 0.7435 0.000 0.000 0.004 0.764 0.092 0.140
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0717 0.8809 0.000 0.000 0.976 0.000 0.008 0.016
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0603 0.8792 0.000 0.000 0.980 0.000 0.004 0.016
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0692 0.8787 0.000 0.000 0.976 0.000 0.004 0.020
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8408 0.000 1.000 0.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1092 0.8706 0.000 0.000 0.960 0.000 0.020 0.020
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0458 0.8820 0.000 0.000 0.984 0.000 0.000 0.016
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3487 0.7544 0.000 0.000 0.000 0.788 0.044 0.168
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 17471 rows and 87 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 1.000 0.977 0.989 0.5011 0.500 0.500
#> 3 3 0.954 0.949 0.967 0.3213 0.809 0.628
#> 4 4 0.923 0.902 0.955 0.1375 0.870 0.638
#> 5 5 0.891 0.820 0.919 0.0629 0.920 0.698
#> 6 6 0.812 0.684 0.838 0.0349 0.976 0.885
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0000 0.989 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.989 0.000 1.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.989 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.989 1.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.989 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0000 0.989 0.000 1.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.989 1.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0000 0.989 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.989 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.989 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.989 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.989 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.989 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.989 1.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.989 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0000 0.989 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.989 1.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.989 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.989 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.7219 0.757 0.200 0.800
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.989 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.989 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0000 0.989 0.000 1.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.989 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.989 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0376 0.985 0.004 0.996
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0000 0.989 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.989 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.989 1.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.0000 0.989 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.989 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.989 0.000 1.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.989 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.7219 0.756 0.800 0.200
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.989 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.989 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.989 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.989 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.989 1.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0000 0.989 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.989 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.0000 0.989 1.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.989 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.989 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.989 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.989 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0000 0.989 0.000 1.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.989 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.7299 0.750 0.796 0.204
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.989 1.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.989 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.989 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.989 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.989 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.989 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.989 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.989 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0000 0.989 0.000 1.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.989 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.989 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.989 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.989 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0000 0.989 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.7299 0.751 0.204 0.796
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.0672 0.982 0.992 0.008
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.989 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0000 0.989 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.989 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.0000 0.989 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.989 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.989 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.989 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.4562 0.891 0.096 0.904
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.989 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.989 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0000 0.989 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.989 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.989 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.989 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.989 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.989 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0000 0.989 0.000 1.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0000 0.989 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.989 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.989 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.989 0.000 1.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0000 0.989 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1411 0.964 0.000 0.036 0.964
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1529 0.964 0.000 0.040 0.960
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.965 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.965 1.000 0.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.965 1.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.1411 0.964 0.000 0.036 0.964
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.965 1.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.1529 0.961 0.960 0.000 0.040
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.2165 0.929 0.000 0.936 0.064
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.965 1.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.1529 0.961 0.960 0.000 0.040
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.965 1.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.977 0.000 1.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.965 1.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.1753 0.942 0.000 0.952 0.048
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.4399 0.820 0.812 0.000 0.188
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.965 1.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.965 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.1529 0.964 0.000 0.040 0.960
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.951 0.000 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.977 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.977 0.000 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1163 0.953 0.028 0.972 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.977 0.000 1.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.965 1.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.4351 0.815 0.004 0.828 0.168
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1643 0.961 0.000 0.044 0.956
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.977 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.965 1.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.2066 0.925 0.940 0.000 0.060
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.977 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.977 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.1529 0.961 0.960 0.000 0.040
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.5115 0.692 0.768 0.228 0.004
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.977 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.977 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.977 0.000 1.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.1529 0.961 0.960 0.000 0.040
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0237 0.963 0.996 0.000 0.004
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.3116 0.876 0.000 0.108 0.892
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.977 0.000 1.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.2796 0.885 0.092 0.000 0.908
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.1289 0.954 0.000 0.968 0.032
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.1529 0.961 0.960 0.000 0.040
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.977 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.1411 0.962 0.964 0.000 0.036
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.1529 0.964 0.000 0.040 0.960
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0424 0.972 0.000 0.992 0.008
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.3425 0.862 0.112 0.004 0.884
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.965 1.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.2261 0.944 0.000 0.068 0.932
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.977 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.965 1.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.977 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.965 1.000 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.1643 0.960 0.956 0.000 0.044
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.1529 0.964 0.000 0.040 0.960
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.1411 0.964 0.000 0.036 0.964
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.1411 0.962 0.964 0.000 0.036
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.1529 0.961 0.960 0.000 0.040
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.977 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.977 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.1529 0.961 0.960 0.000 0.040
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.6841 0.663 0.200 0.724 0.076
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.2878 0.908 0.096 0.000 0.904
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.977 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0237 0.953 0.000 0.004 0.996
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.977 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.4178 0.841 0.828 0.000 0.172
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.965 1.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.1529 0.961 0.960 0.000 0.040
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.977 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.1411 0.964 0.000 0.036 0.964
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0000 0.951 0.000 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.2066 0.925 0.940 0.000 0.060
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.1529 0.961 0.960 0.000 0.040
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.977 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.977 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.965 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.1529 0.961 0.960 0.000 0.040
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.1529 0.964 0.000 0.040 0.960
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0000 0.951 0.000 0.000 1.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.951 0.000 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.977 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1529 0.964 0.000 0.040 0.960
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.1529 0.964 0.000 0.040 0.960
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.1529 0.961 0.960 0.000 0.040
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0469 0.953 0.988 0.000 0.000 0.012
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.4605 0.546 0.336 0.000 0.000 0.664
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.5912 0.155 0.000 0.524 0.036 0.440
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0188 0.955 0.996 0.000 0.000 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.1118 0.907 0.036 0.000 0.000 0.964
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1389 0.933 0.952 0.000 0.000 0.048
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.3751 0.749 0.004 0.800 0.196 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.4564 0.517 0.328 0.672 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.7817 0.567 0.084 0.608 0.176 0.132
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0592 0.952 0.984 0.000 0.000 0.016
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.1118 0.934 0.964 0.000 0.000 0.036
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3444 0.780 0.816 0.000 0.000 0.184
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.2142 0.926 0.000 0.016 0.928 0.056
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2081 0.866 0.000 0.000 0.084 0.916
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.1888 0.903 0.016 0.940 0.044 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.2611 0.861 0.008 0.896 0.096 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.4379 0.745 0.036 0.000 0.172 0.792
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.955 1.000 0.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.1557 0.932 0.000 0.056 0.944 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.4164 0.654 0.264 0.000 0.000 0.736
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.3801 0.711 0.220 0.000 0.000 0.780
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.0336 0.919 0.008 0.000 0.000 0.992
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1118 0.907 0.036 0.000 0.000 0.964
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.5282 0.581 0.036 0.276 0.000 0.688
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.4730 0.433 0.636 0.000 0.364 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0707 0.949 0.980 0.000 0.000 0.020
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0921 0.944 0.972 0.000 0.000 0.028
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1118 0.907 0.036 0.000 0.000 0.964
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0592 0.952 0.984 0.000 0.000 0.016
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0000 0.922 0.000 0.000 0.000 1.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.944 0.000 1.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.992 0.000 0.000 1.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.922 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1197 0.8900 0.000 0.000 0.952 0.000 0.048
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.9159 0.000 0.000 1.000 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.9125 1.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.2329 0.7717 0.124 0.000 0.000 0.000 0.876
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.4305 0.0762 0.488 0.000 0.000 0.512 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0162 0.9145 0.000 0.000 0.996 0.000 0.004
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0162 0.9122 0.996 0.000 0.000 0.000 0.004
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0000 0.8883 0.000 0.000 0.000 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.5707 0.2830 0.000 0.092 0.000 0.364 0.544
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.9125 1.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.1197 0.8695 0.048 0.000 0.000 0.952 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0955 0.8990 0.968 0.000 0.000 0.028 0.004
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.9475 0.000 1.000 0.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0510 0.9087 0.984 0.000 0.000 0.000 0.016
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.1281 0.8183 0.000 0.032 0.012 0.000 0.956
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.1544 0.8514 0.000 0.000 0.000 0.932 0.068
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0609 0.9073 0.980 0.000 0.000 0.000 0.020
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0290 0.9113 0.992 0.000 0.000 0.000 0.008
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.9159 0.000 0.000 1.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3913 0.5371 0.000 0.000 0.676 0.000 0.324
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.9475 0.000 1.000 0.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.2074 0.8846 0.000 0.896 0.000 0.000 0.104
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.5906 0.5252 0.140 0.284 0.000 0.000 0.576
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0162 0.9478 0.000 0.996 0.000 0.000 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2280 0.8242 0.880 0.000 0.000 0.000 0.120
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.1616 0.8194 0.004 0.008 0.008 0.032 0.948
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0703 0.9061 0.000 0.000 0.976 0.000 0.024
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0162 0.9478 0.000 0.996 0.000 0.000 0.004
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0162 0.9120 0.996 0.000 0.000 0.000 0.004
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.2962 0.8374 0.868 0.000 0.000 0.048 0.084
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4171 0.3128 0.000 0.604 0.000 0.000 0.396
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.9475 0.000 1.000 0.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0290 0.8876 0.008 0.000 0.000 0.992 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.3534 0.6218 0.256 0.000 0.000 0.000 0.744
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.2020 0.8872 0.000 0.900 0.000 0.000 0.100
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.9475 0.000 1.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0794 0.9350 0.000 0.972 0.000 0.000 0.028
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0000 0.8883 0.000 0.000 0.000 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.2284 0.8659 0.912 0.000 0.028 0.056 0.004
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.1331 0.8026 0.000 0.000 0.040 0.008 0.952
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.3210 0.7020 0.000 0.788 0.000 0.000 0.212
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.1571 0.8498 0.000 0.000 0.060 0.936 0.004
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.1851 0.8084 0.000 0.088 0.000 0.000 0.912
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.8883 0.000 0.000 0.000 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.9475 0.000 1.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0290 0.8876 0.008 0.000 0.000 0.992 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.9159 0.000 0.000 1.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.1591 0.8207 0.004 0.052 0.004 0.000 0.940
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.2517 0.7898 0.000 0.004 0.008 0.104 0.884
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2929 0.7444 0.820 0.000 0.000 0.000 0.180
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.0703 0.9003 0.000 0.024 0.976 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9475 0.000 1.000 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.4359 0.2231 0.584 0.000 0.000 0.412 0.004
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0162 0.9478 0.000 0.996 0.000 0.000 0.004
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.4437 0.0802 0.464 0.000 0.000 0.532 0.004
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.3092 0.8186 0.048 0.000 0.036 0.880 0.036
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.9159 0.000 0.000 1.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.9159 0.000 0.000 1.000 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1341 0.8672 0.056 0.000 0.000 0.944 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0290 0.8876 0.008 0.000 0.000 0.992 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0162 0.9478 0.000 0.996 0.000 0.000 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.1608 0.9063 0.000 0.928 0.000 0.000 0.072
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.8883 0.000 0.000 0.000 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.6114 0.4898 0.048 0.220 0.000 0.640 0.092
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.6612 0.0494 0.240 0.000 0.452 0.000 0.308
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0162 0.9478 0.000 0.996 0.000 0.000 0.004
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0162 0.9146 0.000 0.000 0.996 0.000 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.9475 0.000 1.000 0.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.1341 0.8588 0.000 0.000 0.000 0.944 0.056
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.9125 1.000 0.000 0.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0000 0.8883 0.000 0.000 0.000 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0162 0.9478 0.000 0.996 0.000 0.000 0.004
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.9159 0.000 0.000 1.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4150 0.4089 0.000 0.000 0.612 0.000 0.388
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.1792 0.8592 0.916 0.000 0.000 0.000 0.084
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1270 0.8681 0.052 0.000 0.000 0.948 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0162 0.9478 0.000 0.996 0.000 0.000 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.1732 0.9034 0.000 0.920 0.000 0.000 0.080
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0162 0.9120 0.996 0.000 0.000 0.000 0.004
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0162 0.8873 0.000 0.000 0.000 0.996 0.004
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0693 0.9094 0.000 0.008 0.980 0.000 0.012
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0000 0.9159 0.000 0.000 1.000 0.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0290 0.9138 0.000 0.000 0.992 0.000 0.008
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0162 0.9478 0.000 0.996 0.000 0.000 0.004
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1197 0.8903 0.000 0.000 0.952 0.000 0.048
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.9159 0.000 0.000 1.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.8883 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.2841 0.7990 0.000 0.000 0.824 0.000 0.012 0.164
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0458 0.9083 0.000 0.000 0.984 0.000 0.000 0.016
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0146 0.8520 0.996 0.000 0.000 0.000 0.000 0.004
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.2147 0.6560 0.084 0.000 0.000 0.000 0.896 0.020
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.4705 0.3984 0.292 0.000 0.000 0.640 0.004 0.064
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.1141 0.9001 0.000 0.000 0.948 0.000 0.000 0.052
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0806 0.8503 0.972 0.000 0.000 0.000 0.008 0.020
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.1531 0.6787 0.000 0.000 0.000 0.928 0.004 0.068
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 6 0.7066 0.0695 0.000 0.088 0.000 0.192 0.352 0.368
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0146 0.8520 0.996 0.000 0.000 0.000 0.000 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.3023 0.6305 0.008 0.000 0.000 0.808 0.004 0.180
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2506 0.8024 0.880 0.000 0.000 0.052 0.000 0.068
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.1462 0.8615 0.000 0.936 0.000 0.000 0.008 0.056
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1176 0.8464 0.956 0.000 0.000 0.000 0.020 0.024
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.2890 0.6178 0.000 0.024 0.004 0.000 0.844 0.128
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.3979 0.1391 0.000 0.000 0.000 0.540 0.004 0.456
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1970 0.8232 0.912 0.000 0.000 0.000 0.060 0.028
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1257 0.8464 0.952 0.000 0.000 0.000 0.020 0.028
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0146 0.9079 0.000 0.000 0.996 0.000 0.000 0.004
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.5220 0.4797 0.000 0.000 0.596 0.000 0.264 0.140
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.1204 0.8675 0.000 0.944 0.000 0.000 0.000 0.056
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4550 0.4192 0.000 0.544 0.000 0.000 0.036 0.420
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.5136 0.4427 0.048 0.244 0.000 0.000 0.656 0.052
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0713 0.8712 0.000 0.972 0.000 0.000 0.000 0.028
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.4023 0.6365 0.720 0.000 0.000 0.004 0.240 0.036
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.0603 0.6591 0.004 0.000 0.000 0.000 0.980 0.016
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.2432 0.8516 0.000 0.008 0.888 0.000 0.024 0.080
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8729 0.000 1.000 0.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1007 0.8461 0.956 0.000 0.000 0.000 0.000 0.044
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.5229 0.5325 0.596 0.000 0.000 0.052 0.032 0.320
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.5232 0.3547 0.000 0.564 0.000 0.000 0.320 0.116
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0363 0.8720 0.000 0.988 0.000 0.000 0.000 0.012
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.1268 0.6748 0.008 0.000 0.000 0.952 0.004 0.036
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.3578 0.6009 0.164 0.000 0.000 0.000 0.784 0.052
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.4353 0.4988 0.000 0.588 0.000 0.000 0.028 0.384
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.1501 0.8567 0.000 0.924 0.000 0.000 0.000 0.076
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3202 0.7769 0.000 0.800 0.000 0.000 0.024 0.176
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1938 0.6714 0.004 0.000 0.000 0.920 0.036 0.040
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4025 0.7421 0.788 0.000 0.032 0.060 0.000 0.120
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.4325 0.0115 0.000 0.000 0.008 0.008 0.504 0.480
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2988 0.7280 0.000 0.824 0.000 0.000 0.152 0.024
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.4435 0.5126 0.000 0.000 0.032 0.664 0.012 0.292
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.1633 0.6620 0.000 0.024 0.000 0.000 0.932 0.044
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0146 0.6822 0.000 0.000 0.000 0.996 0.000 0.004
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0458 0.8727 0.000 0.984 0.000 0.000 0.000 0.016
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0951 0.6790 0.008 0.000 0.000 0.968 0.004 0.020
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0146 0.9079 0.000 0.000 0.996 0.000 0.000 0.004
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.2325 0.6509 0.000 0.048 0.000 0.000 0.892 0.060
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.4275 0.4737 0.000 0.000 0.004 0.076 0.728 0.192
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.3934 0.6050 0.708 0.000 0.000 0.000 0.260 0.032
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.1204 0.8790 0.000 0.056 0.944 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0865 0.8709 0.000 0.964 0.000 0.000 0.000 0.036
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.5032 0.0425 0.464 0.000 0.000 0.472 0.004 0.060
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0547 0.8730 0.000 0.980 0.000 0.000 0.000 0.020
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.5064 0.3248 0.300 0.000 0.000 0.604 0.004 0.092
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.5222 0.4272 0.060 0.000 0.000 0.676 0.068 0.196
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0547 0.9084 0.000 0.000 0.980 0.000 0.000 0.020
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.9078 0.000 0.000 1.000 0.000 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2776 0.6551 0.032 0.000 0.000 0.860 0.004 0.104
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.1036 0.6783 0.008 0.000 0.000 0.964 0.004 0.024
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0146 0.8724 0.000 0.996 0.000 0.000 0.000 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3558 0.7086 0.000 0.736 0.000 0.000 0.016 0.248
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.1390 0.6841 0.004 0.000 0.000 0.948 0.016 0.032
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 6 0.4915 0.1655 0.004 0.048 0.004 0.332 0.004 0.608
#> 1CC36859-357A-49E0-A367-4F57D47288BA 5 0.7434 0.2434 0.184 0.000 0.252 0.000 0.388 0.176
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0260 0.8733 0.000 0.992 0.000 0.000 0.000 0.008
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0713 0.9067 0.000 0.000 0.972 0.000 0.000 0.028
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1327 0.8645 0.000 0.936 0.000 0.000 0.000 0.064
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3782 0.2500 0.000 0.000 0.000 0.636 0.004 0.360
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0777 0.8493 0.972 0.000 0.000 0.004 0.000 0.024
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.3323 0.5902 0.000 0.000 0.000 0.752 0.008 0.240
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8729 0.000 1.000 0.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.9078 0.000 0.000 1.000 0.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.5400 0.3409 0.000 0.000 0.536 0.000 0.332 0.132
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.4150 0.4742 0.616 0.000 0.000 0.008 0.008 0.368
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.3838 0.5757 0.020 0.000 0.000 0.732 0.008 0.240
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8729 0.000 1.000 0.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.3555 0.6690 0.000 0.712 0.000 0.000 0.008 0.280
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0713 0.8488 0.972 0.000 0.000 0.000 0.000 0.028
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.3398 0.5976 0.000 0.000 0.000 0.740 0.008 0.252
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.1605 0.8986 0.000 0.016 0.936 0.000 0.004 0.044
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.1285 0.8999 0.000 0.000 0.944 0.000 0.004 0.052
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.1297 0.9025 0.000 0.000 0.948 0.000 0.012 0.040
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8729 0.000 1.000 0.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.2201 0.8780 0.000 0.000 0.900 0.000 0.052 0.048
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0260 0.9081 0.000 0.000 0.992 0.000 0.000 0.008
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.2948 0.6223 0.000 0.000 0.000 0.804 0.008 0.188
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 17471 rows and 87 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.567 0.771 0.900 0.4757 0.530 0.530
#> 3 3 0.655 0.711 0.867 0.3760 0.596 0.365
#> 4 4 0.892 0.884 0.942 0.1538 0.837 0.563
#> 5 5 0.833 0.778 0.885 0.0647 0.893 0.605
#> 6 6 0.932 0.905 0.951 0.0368 0.967 0.832
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.8955 0.567 0.312 0.688
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.8267 0.648 0.260 0.740
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.856 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.8861 0.608 0.696 0.304
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.856 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.9358 0.504 0.352 0.648
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 2 0.9909 0.339 0.444 0.556
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 2 0.9286 0.516 0.344 0.656
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.890 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.856 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.8861 0.610 0.696 0.304
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.856 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.1184 0.880 0.016 0.984
#> 694B0504-095E-431A-84F2-B4387072138E 2 0.9909 0.339 0.444 0.556
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.3274 0.838 0.060 0.940
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0672 0.853 0.992 0.008
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 2 0.9909 0.339 0.444 0.556
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.856 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.890 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.9286 0.557 0.656 0.344
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.890 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.890 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1414 0.881 0.020 0.980
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.890 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.856 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0000 0.890 0.000 1.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0938 0.884 0.012 0.988
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.890 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 2 0.9909 0.339 0.444 0.556
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.9686 0.165 0.604 0.396
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.890 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.890 0.000 1.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.856 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.3879 0.837 0.076 0.924
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.890 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.890 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.890 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.4431 0.801 0.908 0.092
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.9909 0.339 0.444 0.556
#> FF7021BB-414B-4466-BC43-372D856E6A1D 1 0.9909 0.384 0.556 0.444
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.890 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.9460 0.523 0.636 0.364
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.890 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.1184 0.850 0.984 0.016
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.890 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.856 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.6887 0.734 0.184 0.816
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.890 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.0938 0.885 0.012 0.988
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.4562 0.780 0.904 0.096
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.890 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.890 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.856 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.890 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.856 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.856 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.890 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0938 0.885 0.012 0.988
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.856 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.856 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.890 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.890 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.7056 0.732 0.808 0.192
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.0000 0.890 0.000 1.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.9909 0.339 0.444 0.556
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.890 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 1 0.9909 0.384 0.556 0.444
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.890 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.1414 0.848 0.980 0.020
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.856 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.8861 0.607 0.696 0.304
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.890 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.7815 0.683 0.232 0.768
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.890 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.856 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 2 0.1843 0.876 0.028 0.972
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.890 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.890 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.856 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.856 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.890 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.1414 0.881 0.020 0.980
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.9044 0.555 0.320 0.680
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.890 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0938 0.885 0.012 0.988
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.1414 0.877 0.020 0.980
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.9896 0.390 0.560 0.440
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1751 0.84758 0.012 0.028 0.960
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.6126 0.32707 0.400 0.000 0.600
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.80282 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.6286 0.40068 0.536 0.000 0.464
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.1289 0.79785 0.968 0.000 0.032
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.3028 0.79115 0.920 0.032 0.048
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.1753 0.79966 0.952 0.000 0.048
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.6012 0.63766 0.220 0.032 0.748
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.3028 0.83343 0.048 0.032 0.920
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0424 0.80346 0.992 0.000 0.008
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.6180 0.40900 0.584 0.000 0.416
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1753 0.79966 0.952 0.000 0.048
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.3148 0.83249 0.048 0.036 0.916
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1753 0.79966 0.952 0.000 0.048
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.3669 0.82340 0.040 0.064 0.896
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.6305 -0.00131 0.516 0.000 0.484
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1753 0.79966 0.952 0.000 0.048
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.80282 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.1289 0.84882 0.000 0.032 0.968
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.84069 0.000 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 3 0.4700 0.69163 0.008 0.180 0.812
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.5926 0.46799 0.000 0.644 0.356
#> F9C23182-91C4-4145-AE52-526FE8EB199D 1 0.4708 0.74602 0.844 0.036 0.120
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.5988 0.44283 0.000 0.632 0.368
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2165 0.80257 0.936 0.000 0.064
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.1289 0.84882 0.000 0.032 0.968
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.3237 0.81315 0.032 0.056 0.912
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.88229 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1753 0.79966 0.952 0.000 0.048
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.1753 0.79966 0.952 0.000 0.048
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.5178 0.63620 0.000 0.744 0.256
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.88229 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.1289 0.79785 0.968 0.000 0.032
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.3267 0.76875 0.884 0.000 0.116
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.88229 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.88229 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.88229 0.000 1.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.6260 0.36293 0.552 0.000 0.448
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.1753 0.79966 0.952 0.000 0.048
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.1753 0.82052 0.048 0.000 0.952
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.6215 0.32263 0.000 0.572 0.428
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.0000 0.84069 0.000 0.000 1.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.1289 0.84882 0.000 0.032 0.968
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.6180 0.40600 0.584 0.000 0.416
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.88229 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4974 0.65274 0.764 0.000 0.236
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.7141 0.37466 0.368 0.032 0.600
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.4974 0.66617 0.000 0.764 0.236
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.1289 0.84882 0.000 0.032 0.968
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2066 0.79738 0.940 0.000 0.060
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.6302 0.03220 0.000 0.520 0.480
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.88229 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.1289 0.79785 0.968 0.000 0.032
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.88229 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.80282 1.000 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.5905 0.52640 0.648 0.000 0.352
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.1289 0.84882 0.000 0.032 0.968
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.7141 0.37466 0.368 0.032 0.600
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.6126 0.43222 0.600 0.000 0.400
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.2356 0.78615 0.928 0.000 0.072
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.88229 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.88229 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.6260 0.36293 0.552 0.000 0.448
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.2711 0.81240 0.000 0.088 0.912
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.3816 0.73911 0.852 0.000 0.148
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.88229 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1529 0.82593 0.040 0.000 0.960
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.88229 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.4887 0.61542 0.228 0.000 0.772
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.80282 1.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.6291 0.32114 0.532 0.000 0.468
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.88229 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.6244 0.24882 0.440 0.000 0.560
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.1289 0.84882 0.000 0.032 0.968
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.80282 1.000 0.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.7377 0.27588 0.516 0.032 0.452
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.88229 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.88229 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.80282 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.2537 0.78743 0.920 0.000 0.080
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.1289 0.84882 0.000 0.032 0.968
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.1711 0.84087 0.032 0.008 0.960
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.7141 0.37466 0.368 0.032 0.600
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.88229 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.1289 0.84882 0.000 0.032 0.968
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.1289 0.84882 0.000 0.032 0.968
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.1753 0.82052 0.048 0.000 0.952
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.2345 0.851 0.100 0.000 0.900 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.933 0.000 0.000 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1474 0.926 0.948 0.000 0.000 0.052
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.3768 0.792 0.184 0.000 0.008 0.808
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.0592 0.914 0.016 0.000 0.000 0.984
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.2281 0.899 0.904 0.000 0.096 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0336 0.910 0.000 0.000 0.008 0.992
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.0592 0.928 0.000 0.000 0.984 0.016
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0188 0.930 0.996 0.000 0.000 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0672 0.913 0.008 0.000 0.008 0.984
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2216 0.910 0.908 0.000 0.000 0.092
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 4 0.4843 0.350 0.000 0.000 0.396 0.604
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 4 0.3383 0.842 0.052 0.000 0.076 0.872
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.4888 0.781 0.096 0.000 0.124 0.780
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.933 0.000 0.000 1.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.933 0.000 0.000 1.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.5646 0.298 0.008 0.592 0.384 0.016
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 1 0.2161 0.909 0.932 0.004 0.048 0.016
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.3172 0.773 0.840 0.000 0.000 0.160
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.2060 0.894 0.052 0.000 0.932 0.016
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0336 0.929 0.000 0.008 0.992 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1661 0.927 0.944 0.000 0.004 0.052
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.2281 0.899 0.904 0.000 0.096 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.1661 0.931 0.052 0.944 0.000 0.004
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0592 0.914 0.016 0.000 0.000 0.984
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.0927 0.923 0.976 0.000 0.008 0.016
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0188 0.911 0.000 0.000 0.004 0.996
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.2281 0.899 0.904 0.000 0.096 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0000 0.933 0.000 0.000 1.000 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.1174 0.948 0.000 0.968 0.020 0.012
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.4277 0.581 0.000 0.000 0.720 0.280
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.2060 0.894 0.052 0.000 0.932 0.016
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0592 0.914 0.016 0.000 0.000 0.984
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0592 0.914 0.016 0.000 0.000 0.984
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.933 0.000 0.000 1.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.2522 0.913 0.052 0.920 0.012 0.016
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.0592 0.928 0.000 0.000 0.984 0.016
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.930 1.000 0.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.0707 0.923 0.000 0.020 0.980 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.0707 0.913 0.020 0.000 0.000 0.980
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.2281 0.908 0.904 0.000 0.000 0.096
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.1452 0.897 0.036 0.000 0.008 0.956
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0188 0.933 0.000 0.000 0.996 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.933 0.000 0.000 1.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0592 0.914 0.016 0.000 0.000 0.984
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0592 0.914 0.016 0.000 0.000 0.984
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.911 0.000 0.000 0.000 1.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.7307 0.156 0.000 0.376 0.468 0.156
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.3335 0.862 0.856 0.000 0.128 0.016
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.933 0.000 0.000 1.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0592 0.914 0.016 0.000 0.000 0.984
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.2216 0.910 0.908 0.000 0.000 0.092
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0188 0.911 0.000 0.000 0.004 0.996
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0188 0.931 0.004 0.000 0.996 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0469 0.930 0.000 0.000 0.988 0.012
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.2773 0.891 0.880 0.000 0.004 0.116
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.5018 0.479 0.332 0.000 0.012 0.656
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1474 0.926 0.948 0.000 0.000 0.052
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.4290 0.716 0.212 0.000 0.016 0.772
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0188 0.933 0.000 0.000 0.996 0.004
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.4990 0.430 0.352 0.000 0.640 0.008
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0336 0.931 0.000 0.000 0.992 0.008
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0000 0.933 0.000 0.000 1.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.933 0.000 0.000 1.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.911 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.4087 0.608 0.036 0.000 0.756 0.000 0.208
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.932 0.000 0.000 1.000 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.878 1.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.4242 0.426 0.000 0.000 0.000 0.572 0.428
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 5 0.4489 0.322 0.420 0.000 0.008 0.000 0.572
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0162 0.877 0.996 0.000 0.000 0.000 0.004
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 5 0.4235 0.344 0.000 0.000 0.000 0.424 0.576
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.4182 0.407 0.000 0.000 0.400 0.000 0.600
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.878 1.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0162 0.877 0.996 0.000 0.000 0.004 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 5 0.5439 0.431 0.000 0.000 0.372 0.068 0.560
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.878 1.000 0.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.1430 0.598 0.000 0.000 0.004 0.052 0.944
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.4627 0.694 0.188 0.000 0.080 0.732 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.3274 0.751 0.780 0.000 0.000 0.000 0.220
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.3242 0.757 0.784 0.000 0.000 0.000 0.216
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.932 0.000 0.000 1.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.932 0.000 0.000 1.000 0.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 5 0.5324 0.491 0.004 0.056 0.340 0.000 0.600
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0794 0.925 0.000 0.972 0.000 0.000 0.028
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.0510 0.619 0.016 0.000 0.000 0.000 0.984
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.6318 0.402 0.444 0.000 0.000 0.156 0.400
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.0000 0.619 0.000 0.000 0.000 0.000 1.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0162 0.931 0.000 0.000 0.996 0.000 0.004
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0162 0.877 0.996 0.000 0.000 0.000 0.004
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.0162 0.877 0.996 0.000 0.000 0.000 0.004
#> 36EDD202-A845-4CE7-95D5-A515C471262E 5 0.1965 0.634 0.000 0.096 0.000 0.000 0.904
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2424 0.778 0.000 0.868 0.000 0.000 0.132
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.4273 0.523 0.552 0.000 0.000 0.000 0.448
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 5 0.4192 0.427 0.000 0.404 0.000 0.000 0.596
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0162 0.876 0.996 0.000 0.004 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0162 0.931 0.000 0.000 0.996 0.000 0.004
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 5 0.4517 0.449 0.000 0.388 0.012 0.000 0.600
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.3766 0.591 0.000 0.000 0.728 0.268 0.004
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.0290 0.623 0.000 0.000 0.008 0.000 0.992
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.932 0.000 0.000 1.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.4242 0.346 0.000 0.572 0.000 0.000 0.428
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.4182 0.407 0.000 0.000 0.400 0.000 0.600
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.4235 0.552 0.576 0.000 0.000 0.000 0.424
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.0290 0.926 0.000 0.008 0.992 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 5 0.4242 0.390 0.000 0.428 0.000 0.000 0.572
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.0290 0.919 0.008 0.000 0.000 0.992 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0162 0.877 0.996 0.000 0.000 0.004 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.1952 0.860 0.084 0.000 0.000 0.912 0.004
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0162 0.931 0.000 0.000 0.996 0.000 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.932 0.000 0.000 1.000 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0162 0.948 0.000 0.996 0.000 0.000 0.004
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 5 0.6152 0.539 0.000 0.004 0.256 0.168 0.572
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.2325 0.816 0.904 0.000 0.068 0.000 0.028
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.932 0.000 0.000 1.000 0.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 5 0.4242 0.390 0.000 0.428 0.000 0.000 0.572
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.878 1.000 0.000 0.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0324 0.919 0.000 0.000 0.004 0.992 0.004
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.932 0.000 0.000 1.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0162 0.931 0.000 0.000 0.996 0.000 0.004
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.1792 0.824 0.916 0.000 0.000 0.084 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 5 0.5811 0.506 0.120 0.000 0.004 0.272 0.604
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.878 1.000 0.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.4066 0.558 0.324 0.000 0.000 0.672 0.004
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0162 0.931 0.000 0.000 0.996 0.000 0.004
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.6387 0.184 0.272 0.000 0.512 0.000 0.216
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.932 0.000 0.000 1.000 0.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0162 0.931 0.000 0.000 0.996 0.000 0.004
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.932 0.000 0.000 1.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.924 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.3671 0.681 0.036 0.000 0.756 0.000 0.000 0.208
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.932 1.000 0.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.0146 0.987 0.000 0.000 0.000 0.000 0.996 0.004
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.0000 0.952 0.000 0.000 0.000 1.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 6 0.1151 0.926 0.032 0.000 0.012 0.000 0.000 0.956
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0858 0.925 0.968 0.000 0.000 0.000 0.028 0.004
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 6 0.0363 0.934 0.000 0.000 0.000 0.012 0.000 0.988
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 6 0.0146 0.934 0.000 0.000 0.004 0.000 0.000 0.996
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.932 1.000 0.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0000 0.952 0.000 0.000 0.000 1.000 0.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0458 0.930 0.984 0.000 0.000 0.016 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 6 0.1391 0.921 0.000 0.000 0.040 0.016 0.000 0.944
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1152 0.921 0.952 0.000 0.000 0.000 0.044 0.004
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.0632 0.984 0.000 0.000 0.000 0.000 0.976 0.024
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.3979 0.734 0.172 0.000 0.076 0.752 0.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.3163 0.733 0.764 0.000 0.000 0.000 0.232 0.004
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.3728 0.524 0.652 0.000 0.000 0.000 0.344 0.004
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 6 0.0146 0.934 0.000 0.000 0.004 0.000 0.000 0.996
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.2340 0.830 0.000 0.852 0.000 0.000 0.000 0.148
#> F9C23182-91C4-4145-AE52-526FE8EB199D 6 0.1007 0.922 0.000 0.000 0.000 0.000 0.044 0.956
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.0632 0.984 0.000 0.000 0.000 0.000 0.976 0.024
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0260 0.929 0.000 0.000 0.992 0.000 0.000 0.008
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0632 0.929 0.976 0.000 0.000 0.000 0.000 0.024
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.0603 0.929 0.980 0.000 0.004 0.000 0.000 0.016
#> 36EDD202-A845-4CE7-95D5-A515C471262E 6 0.2214 0.885 0.000 0.016 0.000 0.000 0.096 0.888
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2178 0.839 0.000 0.868 0.000 0.000 0.000 0.132
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0000 0.952 0.000 0.000 0.000 1.000 0.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.0260 0.987 0.000 0.000 0.000 0.000 0.992 0.008
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 6 0.1007 0.924 0.000 0.044 0.000 0.000 0.000 0.956
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0260 0.948 0.000 0.000 0.000 0.992 0.000 0.008
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0547 0.927 0.980 0.000 0.020 0.000 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 6 0.0458 0.935 0.000 0.016 0.000 0.000 0.000 0.984
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.3978 0.583 0.000 0.000 0.700 0.268 0.000 0.032
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 6 0.0790 0.928 0.000 0.000 0.000 0.000 0.032 0.968
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.952 0.000 0.000 0.000 1.000 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0000 0.952 0.000 0.000 0.000 1.000 0.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.0632 0.984 0.000 0.000 0.000 0.000 0.976 0.024
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 6 0.0547 0.931 0.000 0.000 0.020 0.000 0.000 0.980
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 5 0.0000 0.985 0.000 0.000 0.000 0.000 1.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.0146 0.932 0.000 0.004 0.996 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 6 0.1075 0.922 0.000 0.048 0.000 0.000 0.000 0.952
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.0260 0.948 0.008 0.000 0.000 0.992 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0547 0.928 0.980 0.000 0.000 0.020 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.2179 0.880 0.064 0.000 0.000 0.900 0.000 0.036
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0000 0.952 0.000 0.000 0.000 1.000 0.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.952 0.000 0.000 0.000 1.000 0.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2491 0.818 0.000 0.836 0.000 0.000 0.000 0.164
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.952 0.000 0.000 0.000 1.000 0.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 6 0.3978 0.760 0.000 0.000 0.084 0.160 0.000 0.756
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.2258 0.874 0.896 0.000 0.060 0.000 0.000 0.044
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 6 0.1075 0.922 0.000 0.048 0.000 0.000 0.000 0.952
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.0000 0.952 0.000 0.000 0.000 1.000 0.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.932 1.000 0.000 0.000 0.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0363 0.945 0.000 0.000 0.000 0.988 0.000 0.012
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0363 0.927 0.000 0.000 0.988 0.000 0.000 0.012
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.1753 0.873 0.912 0.000 0.000 0.084 0.000 0.004
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 6 0.3284 0.794 0.000 0.000 0.000 0.168 0.032 0.800
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.932 1.000 0.000 0.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.4146 0.596 0.288 0.000 0.000 0.676 0.000 0.036
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.5940 0.241 0.268 0.000 0.460 0.000 0.000 0.272
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.952 0.000 0.000 0.000 1.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17471 rows and 87 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 0.345 0.818 0.865 0.4629 0.505 0.505
#> 3 3 0.502 0.599 0.816 0.3222 0.649 0.431
#> 4 4 0.645 0.748 0.822 0.1212 0.737 0.426
#> 5 5 0.667 0.631 0.827 0.0929 0.921 0.741
#> 6 6 0.675 0.611 0.799 0.0507 0.915 0.693
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.311 0.8886 0.056 0.944
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.529 0.8689 0.120 0.880
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.625 0.8656 0.844 0.156
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.795 0.8316 0.760 0.240
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.615 0.8661 0.848 0.152
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.653 0.8145 0.168 0.832
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.625 0.8656 0.844 0.156
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.343 0.8200 0.936 0.064
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.795 0.8082 0.240 0.760
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.625 0.8656 0.844 0.156
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.204 0.8245 0.968 0.032
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.625 0.8656 0.844 0.156
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.518 0.8862 0.116 0.884
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.625 0.8656 0.844 0.156
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.327 0.8869 0.060 0.940
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.402 0.8089 0.920 0.080
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.625 0.8656 0.844 0.156
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.625 0.8656 0.844 0.156
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.738 0.8047 0.208 0.792
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.855 0.7525 0.280 0.720
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.388 0.8890 0.076 0.924
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.388 0.8890 0.076 0.924
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.402 0.8882 0.080 0.920
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.738 0.8305 0.208 0.792
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.634 0.8649 0.840 0.160
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.494 0.8571 0.108 0.892
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.295 0.8886 0.052 0.948
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.184 0.8775 0.028 0.972
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.634 0.8633 0.840 0.160
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.781 0.8444 0.768 0.232
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.295 0.8886 0.052 0.948
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.327 0.8900 0.060 0.940
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.595 0.8631 0.856 0.144
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.971 0.5731 0.600 0.400
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.388 0.8890 0.076 0.924
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.184 0.8775 0.028 0.972
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.388 0.8890 0.076 0.924
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.224 0.8244 0.964 0.036
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.714 0.8517 0.804 0.196
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.456 0.8666 0.096 0.904
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.402 0.8882 0.080 0.920
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.753 0.6611 0.784 0.216
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.295 0.8886 0.052 0.948
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.242 0.8270 0.960 0.040
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.184 0.8775 0.028 0.972
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.163 0.8265 0.976 0.024
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.373 0.8900 0.072 0.928
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.295 0.8886 0.052 0.948
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.876 0.6924 0.296 0.704
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.689 0.8545 0.816 0.184
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.430 0.8911 0.088 0.912
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.260 0.8855 0.044 0.956
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.625 0.8656 0.844 0.156
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.373 0.8896 0.072 0.928
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.662 0.8622 0.828 0.172
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.358 0.8104 0.932 0.068
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.738 0.8047 0.208 0.792
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.584 0.8763 0.140 0.860
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.242 0.8292 0.960 0.040
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.141 0.8240 0.980 0.020
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.260 0.8855 0.044 0.956
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.184 0.8775 0.028 0.972
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.416 0.8029 0.916 0.084
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.921 0.7144 0.336 0.664
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.990 -0.0525 0.440 0.560
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.260 0.8855 0.044 0.956
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.900 0.7283 0.316 0.684
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.260 0.8855 0.044 0.956
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.994 -0.1695 0.544 0.456
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.625 0.8656 0.844 0.156
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.260 0.8230 0.956 0.044
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.184 0.8775 0.028 0.972
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.722 0.8092 0.200 0.800
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.788 0.7915 0.236 0.764
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.722 0.8475 0.800 0.200
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.775 0.8269 0.772 0.228
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.242 0.8838 0.040 0.960
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.278 0.8867 0.048 0.952
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.625 0.8656 0.844 0.156
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.242 0.8270 0.960 0.040
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.706 0.8240 0.192 0.808
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.767 0.7988 0.224 0.776
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.814 0.7805 0.252 0.748
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.184 0.8775 0.028 0.972
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.295 0.8886 0.052 0.948
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.738 0.8047 0.208 0.792
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.876 0.4372 0.704 0.296
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.6305 -0.1112 0.000 0.484 0.516
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.6267 0.0184 0.000 0.452 0.548
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.9547 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.4047 0.5767 0.148 0.004 0.848
#> 2F38E3B1-4975-4877-9DCC-C00270602180 3 0.6314 0.1554 0.392 0.004 0.604
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.7853 0.2717 0.060 0.384 0.556
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.9547 1.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.3784 0.5956 0.132 0.004 0.864
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.6359 0.1731 0.004 0.404 0.592
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.9547 1.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.4931 0.5422 0.212 0.004 0.784
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.9547 1.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.7107 0.4178 0.036 0.624 0.340
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.9547 1.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.6286 -0.0290 0.000 0.464 0.536
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.4178 0.5708 0.172 0.000 0.828
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.9547 1.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.9547 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.5968 0.5473 0.000 0.636 0.364
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0237 0.6092 0.000 0.004 0.996
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.2959 0.8009 0.000 0.900 0.100
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.3482 0.7904 0.000 0.872 0.128
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5618 0.7048 0.008 0.732 0.260
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2165 0.8141 0.000 0.936 0.064
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.5024 0.7228 0.776 0.004 0.220
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.6244 0.0617 0.000 0.440 0.560
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.5327 0.7001 0.000 0.728 0.272
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8298 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.9547 1.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.9049 0.3181 0.400 0.136 0.464
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.5291 0.7040 0.000 0.732 0.268
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.8298 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 3 0.4931 0.5422 0.212 0.004 0.784
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.8937 0.0262 0.124 0.428 0.448
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0237 0.8261 0.000 0.996 0.004
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.8298 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.8298 0.000 1.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.4682 0.5590 0.192 0.004 0.804
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.4931 0.5422 0.212 0.004 0.784
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.6204 0.1004 0.000 0.424 0.576
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.5178 0.7135 0.000 0.744 0.256
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.0661 0.6099 0.008 0.004 0.988
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.5431 0.6852 0.000 0.716 0.284
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.4931 0.5422 0.212 0.004 0.784
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.8298 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 3 0.4978 0.5377 0.216 0.004 0.780
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.5397 0.6906 0.000 0.720 0.280
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5291 0.7040 0.000 0.732 0.268
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.1031 0.6114 0.000 0.024 0.976
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.9547 1.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0747 0.8272 0.000 0.984 0.016
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.8298 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.5058 0.6954 0.756 0.000 0.244
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.8298 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 3 0.5115 0.5215 0.228 0.004 0.768
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.3918 0.5918 0.140 0.004 0.856
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.6299 -0.0775 0.000 0.476 0.524
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.6260 0.0329 0.000 0.448 0.552
#> C41F3064-4483-4796-B860-82155BAA5157 3 0.5158 0.5148 0.232 0.004 0.764
#> F4232B90-51B9-43EE-9971-35B3A318758F 3 0.4931 0.5422 0.212 0.004 0.784
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.8298 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.8298 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.3112 0.6062 0.096 0.004 0.900
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.8466 0.2509 0.092 0.400 0.508
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.5968 0.2578 0.000 0.364 0.636
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.8298 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.6026 0.2342 0.000 0.376 0.624
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.8298 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.4931 0.5422 0.212 0.004 0.784
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.9547 1.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.3500 0.6018 0.116 0.004 0.880
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8298 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.6154 0.1578 0.000 0.408 0.592
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.6026 0.2343 0.000 0.376 0.624
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.1989 0.9055 0.948 0.004 0.048
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.3112 0.6061 0.096 0.004 0.900
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8298 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.8298 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.9547 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.4883 0.5460 0.208 0.004 0.788
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.6062 0.5023 0.000 0.616 0.384
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.5431 0.3855 0.000 0.284 0.716
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.1163 0.6116 0.000 0.028 0.972
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8298 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.6026 0.5216 0.000 0.624 0.376
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.6267 0.3024 0.000 0.548 0.452
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.3500 0.6016 0.116 0.004 0.880
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.5673 0.7519 0.000 0.372 0.596 0.032
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.6028 0.7561 0.000 0.364 0.584 0.052
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.9323 1.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.5345 0.3213 0.008 0.004 0.584 0.404
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.2589 0.8224 0.116 0.000 0.000 0.884
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.6263 0.7561 0.000 0.356 0.576 0.068
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.9323 1.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0817 0.8968 0.000 0.000 0.024 0.976
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.6446 0.7504 0.000 0.328 0.584 0.088
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.9323 1.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0000 0.8978 0.000 0.000 0.000 1.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.4553 0.7523 0.780 0.000 0.040 0.180
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.5681 0.7355 0.000 0.404 0.568 0.028
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.9323 1.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.5839 0.7566 0.000 0.352 0.604 0.044
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.1389 0.8892 0.000 0.000 0.048 0.952
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0188 0.9314 0.996 0.000 0.004 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.9323 1.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.5060 0.7238 0.000 0.412 0.584 0.004
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.4977 0.2080 0.000 0.000 0.540 0.460
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.4643 -0.0367 0.000 0.656 0.344 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.4477 -0.0798 0.000 0.312 0.688 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.4941 0.6970 0.000 0.436 0.564 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.3751 0.5594 0.000 0.800 0.196 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.3649 0.7026 0.796 0.000 0.000 0.204
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.5954 0.7575 0.000 0.344 0.604 0.052
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.4907 0.7119 0.000 0.420 0.580 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.9067 0.000 1.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1211 0.9190 0.960 0.000 0.040 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.5174 0.4646 0.092 0.000 0.756 0.152
#> 36EDD202-A845-4CE7-95D5-A515C471262E 3 0.4916 0.7052 0.000 0.424 0.576 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0707 0.8997 0.000 0.980 0.020 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0188 0.8960 0.000 0.000 0.004 0.996
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.6931 0.5795 0.228 0.184 0.588 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.4855 0.3945 0.000 0.600 0.400 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.9067 0.000 1.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0592 0.9030 0.000 0.984 0.016 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0469 0.8981 0.000 0.000 0.012 0.988
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.1902 0.8837 0.000 0.004 0.064 0.932
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.1211 0.4887 0.000 0.000 0.960 0.040
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.4907 0.7144 0.000 0.420 0.580 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.4790 0.2943 0.000 0.000 0.380 0.620
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.4866 0.7253 0.000 0.404 0.596 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.8978 0.000 0.000 0.000 1.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.9067 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0000 0.8978 0.000 0.000 0.000 1.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.4916 0.7114 0.000 0.424 0.576 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.4877 0.7204 0.000 0.408 0.592 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.5805 0.3996 0.000 0.036 0.576 0.388
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.9323 1.000 0.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.1211 0.8801 0.000 0.960 0.040 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0592 0.9026 0.000 0.984 0.016 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.5312 0.4815 0.268 0.000 0.040 0.692
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0336 0.9063 0.000 0.992 0.008 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.1211 0.8687 0.000 0.000 0.040 0.960
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.1211 0.8917 0.000 0.000 0.040 0.960
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.6028 0.7561 0.000 0.364 0.584 0.052
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.6028 0.7561 0.000 0.364 0.584 0.052
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0000 0.8978 0.000 0.000 0.000 1.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.8978 0.000 0.000 0.000 1.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0336 0.9063 0.000 0.992 0.008 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0469 0.9043 0.000 0.988 0.012 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2868 0.8155 0.000 0.000 0.136 0.864
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.6910 0.6416 0.000 0.252 0.584 0.164
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.6263 0.7561 0.000 0.356 0.576 0.068
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.9067 0.000 1.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.6616 0.7408 0.000 0.308 0.584 0.108
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0817 0.8978 0.000 0.976 0.024 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.1867 0.8731 0.000 0.000 0.072 0.928
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.9323 1.000 0.000 0.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.2704 0.8267 0.000 0.000 0.124 0.876
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.9067 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.6234 0.7562 0.000 0.348 0.584 0.068
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.6553 0.7452 0.000 0.316 0.584 0.100
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.5138 0.6565 0.600 0.000 0.392 0.008
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.5000 0.1015 0.000 0.000 0.504 0.496
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9067 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0707 0.8990 0.000 0.980 0.020 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1211 0.9190 0.960 0.000 0.040 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0000 0.8978 0.000 0.000 0.000 1.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.5193 0.7262 0.000 0.412 0.580 0.008
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.6897 0.6333 0.000 0.160 0.584 0.256
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.5735 0.3899 0.000 0.032 0.576 0.392
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.9067 0.000 1.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.4855 0.7273 0.000 0.400 0.600 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.5781 0.7495 0.000 0.380 0.584 0.036
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3266 0.7723 0.000 0.000 0.168 0.832
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1478 0.70321 0.000 0.000 0.936 0.000 0.064
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1211 0.71352 0.000 0.016 0.960 0.000 0.024
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0290 0.89110 0.992 0.000 0.000 0.000 0.008
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.6426 0.00992 0.044 0.000 0.500 0.068 0.388
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.3327 0.75639 0.072 0.000 0.036 0.864 0.028
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.1921 0.70455 0.000 0.044 0.932 0.012 0.012
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.89171 1.000 0.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3796 0.72142 0.000 0.000 0.300 0.700 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.0880 0.71085 0.000 0.000 0.968 0.000 0.032
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.89171 1.000 0.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.1197 0.79541 0.000 0.000 0.048 0.952 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.5102 0.66993 0.684 0.000 0.000 0.216 0.100
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.1557 0.70706 0.000 0.052 0.940 0.000 0.008
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.89171 1.000 0.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.3707 0.43605 0.000 0.000 0.716 0.000 0.284
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.4504 0.41352 0.000 0.000 0.428 0.564 0.008
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0162 0.89155 0.996 0.000 0.000 0.000 0.004
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.89171 1.000 0.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.1195 0.71642 0.000 0.012 0.960 0.000 0.028
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.4763 0.12386 0.000 0.000 0.632 0.336 0.032
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.4446 -0.05244 0.000 0.520 0.476 0.000 0.004
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.5060 0.40856 0.000 0.204 0.104 0.000 0.692
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.5562 0.35495 0.000 0.200 0.644 0.000 0.156
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 3 0.4291 0.02070 0.000 0.464 0.536 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.3074 0.70833 0.804 0.000 0.000 0.196 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.4446 0.20258 0.000 0.008 0.592 0.000 0.400
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.4885 0.16260 0.000 0.028 0.572 0.000 0.400
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0162 0.87191 0.000 0.996 0.004 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2983 0.83883 0.864 0.000 0.000 0.040 0.096
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.3559 0.60202 0.012 0.000 0.804 0.008 0.176
#> 36EDD202-A845-4CE7-95D5-A515C471262E 5 0.6480 0.18151 0.000 0.184 0.404 0.000 0.412
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2295 0.79537 0.000 0.900 0.088 0.008 0.004
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0510 0.76458 0.000 0.000 0.000 0.984 0.016
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.6684 0.26182 0.240 0.000 0.352 0.000 0.408
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 5 0.4278 -0.17398 0.000 0.452 0.000 0.000 0.548
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0162 0.87191 0.000 0.996 0.004 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2077 0.79091 0.000 0.908 0.008 0.000 0.084
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.3274 0.76335 0.000 0.000 0.220 0.780 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.5313 0.17580 0.004 0.000 0.504 0.452 0.040
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.4235 0.20656 0.000 0.000 0.424 0.000 0.576
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.4489 0.49207 0.000 0.192 0.740 0.000 0.068
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.4989 0.53148 0.000 0.000 0.416 0.552 0.032
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.5304 0.14786 0.000 0.056 0.560 0.000 0.384
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1043 0.79334 0.000 0.000 0.040 0.960 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0162 0.87191 0.000 0.996 0.004 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.1444 0.79195 0.000 0.000 0.040 0.948 0.012
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.2722 0.65273 0.000 0.108 0.872 0.000 0.020
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.5182 0.08097 0.000 0.044 0.544 0.000 0.412
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.1444 0.70519 0.000 0.000 0.948 0.040 0.012
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0794 0.87902 0.972 0.000 0.000 0.000 0.028
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.3766 0.45954 0.000 0.728 0.268 0.000 0.004
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0794 0.86031 0.000 0.972 0.028 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.3521 0.64895 0.040 0.000 0.000 0.820 0.140
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0290 0.87188 0.000 0.992 0.008 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.1502 0.74554 0.004 0.000 0.000 0.940 0.056
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.3671 0.75616 0.000 0.000 0.236 0.756 0.008
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.1041 0.71194 0.000 0.004 0.964 0.000 0.032
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0771 0.71535 0.000 0.020 0.976 0.000 0.004
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1331 0.79281 0.000 0.000 0.040 0.952 0.008
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.1444 0.79195 0.000 0.000 0.040 0.948 0.012
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0290 0.87188 0.000 0.992 0.008 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0290 0.87188 0.000 0.992 0.008 0.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.4118 0.68313 0.000 0.000 0.336 0.660 0.004
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.1740 0.68912 0.000 0.000 0.932 0.056 0.012
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.2770 0.67973 0.000 0.044 0.880 0.000 0.076
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0162 0.87191 0.000 0.996 0.004 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.1041 0.70954 0.000 0.000 0.964 0.004 0.032
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3074 0.61754 0.000 0.804 0.196 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.4383 0.25447 0.000 0.000 0.572 0.424 0.004
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0290 0.89110 0.992 0.000 0.000 0.000 0.008
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.3990 0.71285 0.000 0.000 0.308 0.688 0.004
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0162 0.87191 0.000 0.996 0.004 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0510 0.71435 0.000 0.000 0.984 0.000 0.016
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0963 0.71295 0.000 0.000 0.964 0.000 0.036
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.4549 0.42603 0.528 0.000 0.000 0.008 0.464
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.2843 0.62393 0.000 0.000 0.848 0.144 0.008
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0162 0.87191 0.000 0.996 0.004 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.1544 0.82206 0.000 0.932 0.068 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.3090 0.83659 0.856 0.000 0.000 0.040 0.104
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1410 0.79538 0.000 0.000 0.060 0.940 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0992 0.71475 0.000 0.024 0.968 0.000 0.008
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0609 0.71363 0.000 0.000 0.980 0.000 0.020
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.1211 0.71095 0.000 0.000 0.960 0.016 0.024
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0162 0.87191 0.000 0.996 0.004 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.4508 0.35450 0.000 0.020 0.648 0.000 0.332
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0794 0.71189 0.000 0.000 0.972 0.000 0.028
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.4268 0.66964 0.000 0.000 0.344 0.648 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.3103 0.5585 0.000 0.000 0.784 0.008 0.208 NA
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1588 0.6489 0.000 0.000 0.924 0.004 0.072 NA
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1267 0.8442 0.940 0.000 0.000 0.000 0.000 NA
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.5835 0.5175 0.116 0.000 0.392 0.004 0.476 NA
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.3017 0.7496 0.072 0.000 0.000 0.844 0.000 NA
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.1590 0.6701 0.000 0.000 0.936 0.008 0.048 NA
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.8535 1.000 0.000 0.000 0.000 0.000 NA
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.1663 0.8065 0.000 0.000 0.088 0.912 0.000 NA
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.2001 0.6706 0.000 0.000 0.912 0.012 0.068 NA
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0146 0.8529 0.996 0.000 0.000 0.000 0.000 NA
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0363 0.8128 0.000 0.000 0.012 0.988 0.000 NA
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.5350 0.6147 0.564 0.000 0.000 0.140 0.000 NA
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.1785 0.6629 0.000 0.008 0.928 0.000 0.016 NA
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0146 0.8535 0.996 0.000 0.000 0.000 0.000 NA
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.3684 -0.1311 0.000 0.000 0.628 0.000 0.372 NA
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.4492 0.6563 0.000 0.000 0.080 0.700 0.216 NA
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0260 0.8534 0.992 0.000 0.000 0.000 0.000 NA
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.8535 1.000 0.000 0.000 0.000 0.000 NA
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.1563 0.6799 0.000 0.000 0.932 0.000 0.056 NA
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.5258 0.0922 0.000 0.000 0.544 0.380 0.052 NA
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 3 0.5803 0.1489 0.000 0.312 0.560 0.000 0.068 NA
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.6036 0.1876 0.000 0.164 0.020 0.000 0.508 NA
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.5701 0.0601 0.000 0.104 0.588 0.000 0.272 NA
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 3 0.5546 0.3414 0.000 0.196 0.644 0.000 0.048 NA
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2980 0.6976 0.808 0.000 0.000 0.180 0.000 NA
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.3862 0.5239 0.000 0.000 0.476 0.000 0.524 NA
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.4089 0.5399 0.000 0.008 0.468 0.000 0.524 NA
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8449 0.000 1.000 0.000 0.000 0.000 NA
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.3578 0.7054 0.660 0.000 0.000 0.000 0.000 NA
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.6828 -0.1896 0.140 0.000 0.404 0.064 0.384 NA
#> 36EDD202-A845-4CE7-95D5-A515C471262E 5 0.4756 0.5714 0.000 0.052 0.408 0.000 0.540 NA
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.4526 0.5434 0.000 0.692 0.244 0.000 0.048 NA
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.1387 0.7977 0.000 0.000 0.000 0.932 0.000 NA
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.5840 0.3615 0.316 0.000 0.168 0.000 0.508 NA
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.6069 0.2642 0.000 0.404 0.000 0.000 0.288 NA
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0260 0.8443 0.000 0.992 0.000 0.000 0.000 NA
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2579 0.7819 0.000 0.872 0.000 0.000 0.088 NA
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0937 0.8117 0.000 0.000 0.040 0.960 0.000 NA
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.6980 -0.1126 0.000 0.000 0.340 0.372 0.068 NA
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.4173 0.3233 0.000 0.000 0.228 0.000 0.712 NA
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.3516 0.5857 0.000 0.036 0.832 0.000 0.076 NA
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.4667 0.5634 0.000 0.000 0.292 0.652 0.036 NA
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.3998 0.4612 0.000 0.004 0.492 0.000 0.504 NA
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0146 0.8117 0.000 0.000 0.000 0.996 0.004 NA
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0260 0.8441 0.000 0.992 0.000 0.000 0.000 NA
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0632 0.8094 0.000 0.000 0.000 0.976 0.000 NA
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.3216 0.6107 0.000 0.052 0.852 0.000 0.064 NA
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.4141 0.5731 0.000 0.012 0.432 0.000 0.556 NA
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.1584 0.6639 0.000 0.000 0.928 0.008 0.064 NA
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0520 0.8505 0.984 0.000 0.000 0.000 0.008 NA
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.5530 0.3372 0.000 0.560 0.320 0.000 0.016 NA
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.3669 0.6344 0.000 0.760 0.208 0.000 0.004 NA
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.4086 0.4623 0.008 0.000 0.000 0.528 0.000 NA
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1088 0.8361 0.000 0.960 0.016 0.000 0.000 NA
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.2854 0.7158 0.000 0.000 0.000 0.792 0.000 NA
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.1219 0.8114 0.000 0.000 0.048 0.948 0.004 NA
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.1398 0.6801 0.000 0.000 0.940 0.000 0.052 NA
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0146 0.6792 0.000 0.000 0.996 0.004 0.000 NA
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0363 0.8107 0.000 0.000 0.000 0.988 0.000 NA
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0458 0.8103 0.000 0.000 0.000 0.984 0.000 NA
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0964 0.8397 0.000 0.968 0.012 0.000 0.004 NA
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.1141 0.8341 0.000 0.948 0.000 0.000 0.000 NA
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2462 0.7824 0.000 0.000 0.132 0.860 0.004 NA
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.4832 0.4419 0.000 0.000 0.680 0.092 0.216 NA
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.3361 0.5342 0.000 0.000 0.788 0.004 0.188 NA
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0146 0.8448 0.000 0.996 0.000 0.000 0.000 NA
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.3972 0.4950 0.000 0.000 0.732 0.016 0.232 NA
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.4407 0.4805 0.000 0.664 0.292 0.000 0.008 NA
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.6274 0.2016 0.000 0.000 0.288 0.472 0.220 NA
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1745 0.8417 0.920 0.000 0.000 0.000 0.012 NA
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.2135 0.7810 0.000 0.000 0.128 0.872 0.000 NA
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8449 0.000 1.000 0.000 0.000 0.000 NA
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.1462 0.6570 0.000 0.000 0.936 0.008 0.056 NA
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.2339 0.6657 0.000 0.000 0.896 0.012 0.072 NA
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.5601 0.5229 0.560 0.000 0.000 0.008 0.148 NA
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.3697 0.4706 0.000 0.000 0.732 0.248 0.016 NA
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8449 0.000 1.000 0.000 0.000 0.000 NA
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.1889 0.8276 0.000 0.920 0.020 0.000 0.004 NA
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.3695 0.6906 0.624 0.000 0.000 0.000 0.000 NA
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0146 0.8105 0.000 0.000 0.000 0.996 0.000 NA
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0622 0.6763 0.000 0.000 0.980 0.000 0.012 NA
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.1453 0.6814 0.000 0.000 0.944 0.008 0.040 NA
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.1555 0.6814 0.000 0.000 0.940 0.008 0.040 NA
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8449 0.000 1.000 0.000 0.000 0.000 NA
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.3789 -0.2943 0.000 0.000 0.584 0.000 0.416 NA
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.2088 0.6711 0.000 0.000 0.904 0.000 0.068 NA
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3452 0.7238 0.000 0.000 0.184 0.788 0.012 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["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 17471 rows and 87 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.963 0.985 0.4995 0.502 0.502
#> 3 3 0.656 0.764 0.894 0.2896 0.746 0.541
#> 4 4 0.596 0.685 0.794 0.1466 0.767 0.443
#> 5 5 0.721 0.611 0.791 0.0664 0.909 0.680
#> 6 6 0.805 0.696 0.836 0.0392 0.936 0.731
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0000 0.979 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.979 0.000 1.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.991 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.991 1.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.991 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.4298 0.893 0.088 0.912
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.991 1.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0000 0.991 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.979 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.991 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.991 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.991 1.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.979 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.991 1.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.979 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.0000 0.991 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.991 1.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.991 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.979 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.9909 0.214 0.444 0.556
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.979 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.979 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0376 0.976 0.004 0.996
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.979 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.991 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0938 0.970 0.012 0.988
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0000 0.979 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.979 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.991 1.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.0000 0.991 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.979 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0000 0.979 0.000 1.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.991 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.6801 0.779 0.820 0.180
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.979 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.979 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.979 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.991 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.991 1.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0000 0.979 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.979 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.1633 0.970 0.976 0.024
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.979 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.991 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.979 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.991 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0000 0.979 0.000 1.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.979 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.9608 0.382 0.384 0.616
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.991 1.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.979 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.979 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.991 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.979 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.991 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.991 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.979 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0000 0.979 0.000 1.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.991 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.991 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.979 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.979 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0000 0.991 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.0000 0.979 0.000 1.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.4690 0.887 0.900 0.100
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.979 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0000 0.979 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.979 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.1414 0.973 0.980 0.020
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.991 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.991 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.979 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.1633 0.959 0.024 0.976
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.979 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.991 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0000 0.991 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.979 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.979 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.991 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.991 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.979 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0938 0.970 0.012 0.988
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.1843 0.955 0.028 0.972
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.979 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.979 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.979 0.000 1.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0376 0.988 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.4665 0.816 0.048 0.852 0.100
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.5760 0.528 0.000 0.672 0.328
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1860 0.893 0.948 0.000 0.052
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.2537 0.881 0.920 0.000 0.080
#> 2F38E3B1-4975-4877-9DCC-C00270602180 3 0.6079 0.153 0.388 0.000 0.612
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.5650 0.563 0.000 0.688 0.312
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0237 0.889 0.996 0.000 0.004
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.0237 0.843 0.000 0.004 0.996
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.5327 0.613 0.000 0.272 0.728
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.2165 0.889 0.936 0.000 0.064
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.0000 0.843 0.000 0.000 1.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2625 0.883 0.916 0.000 0.084
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0592 0.883 0.000 0.988 0.012
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.887 1.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.3038 0.826 0.000 0.896 0.104
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.0424 0.842 0.008 0.000 0.992
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1643 0.893 0.956 0.000 0.044
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0424 0.890 0.992 0.000 0.008
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.4842 0.697 0.000 0.776 0.224
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1163 0.835 0.000 0.028 0.972
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.884 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.2356 0.846 0.072 0.928 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 1 0.6291 0.133 0.532 0.468 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1860 0.863 0.000 0.948 0.052
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.3879 0.832 0.848 0.000 0.152
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.5551 0.690 0.016 0.760 0.224
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0424 0.882 0.008 0.992 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.884 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1031 0.893 0.976 0.000 0.024
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.1411 0.890 0.964 0.000 0.036
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0424 0.884 0.000 0.992 0.008
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0237 0.884 0.000 0.996 0.004
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 3 0.0747 0.838 0.016 0.000 0.984
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.1411 0.870 0.964 0.036 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.1964 0.857 0.056 0.944 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.1031 0.876 0.024 0.976 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.4750 0.669 0.216 0.784 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.0424 0.842 0.008 0.000 0.992
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4291 0.810 0.820 0.000 0.180
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.6421 0.258 0.004 0.424 0.572
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0424 0.884 0.000 0.992 0.008
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.0237 0.843 0.000 0.004 0.996
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.884 0.000 1.000 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.0000 0.843 0.000 0.000 1.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0424 0.882 0.008 0.992 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 3 0.2165 0.797 0.064 0.000 0.936
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0000 0.884 0.000 1.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.1163 0.874 0.028 0.972 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.5706 0.503 0.000 0.320 0.680
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0424 0.890 0.992 0.000 0.008
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0424 0.884 0.000 0.992 0.008
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0424 0.884 0.000 0.992 0.008
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.6215 0.393 0.572 0.000 0.428
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0424 0.884 0.000 0.992 0.008
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 3 0.5497 0.433 0.292 0.000 0.708
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.0424 0.842 0.008 0.000 0.992
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.6302 0.101 0.000 0.520 0.480
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.6079 0.393 0.000 0.612 0.388
#> C41F3064-4483-4796-B860-82155BAA5157 3 0.1860 0.809 0.052 0.000 0.948
#> F4232B90-51B9-43EE-9971-35B3A318758F 3 0.0747 0.838 0.016 0.000 0.984
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0424 0.884 0.000 0.992 0.008
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.2066 0.854 0.060 0.940 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.0237 0.843 0.000 0.004 0.996
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.6026 0.393 0.000 0.376 0.624
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.6975 0.703 0.732 0.124 0.144
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0424 0.882 0.008 0.992 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.5058 0.655 0.000 0.244 0.756
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.884 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.0237 0.843 0.000 0.004 0.996
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.3038 0.864 0.896 0.000 0.104
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.0424 0.842 0.008 0.000 0.992
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.884 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.6280 0.175 0.000 0.540 0.460
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.5363 0.606 0.000 0.276 0.724
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0424 0.884 0.992 0.008 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.0592 0.840 0.012 0.000 0.988
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0237 0.883 0.004 0.996 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0747 0.879 0.016 0.984 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1753 0.894 0.952 0.000 0.048
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.0424 0.842 0.008 0.000 0.992
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.3551 0.802 0.000 0.868 0.132
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.4887 0.677 0.000 0.228 0.772
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.3941 0.755 0.000 0.156 0.844
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.884 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.1129 0.880 0.004 0.976 0.020
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.6244 0.244 0.000 0.560 0.440
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.0424 0.842 0.000 0.008 0.992
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.2266 0.63037 0.004 0.084 0.912 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.6350 0.67889 0.000 0.252 0.636 0.112
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0469 0.88741 0.988 0.000 0.000 0.012
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.3464 0.85887 0.856 0.004 0.124 0.016
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.5267 0.50257 0.240 0.000 0.048 0.712
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.7381 0.42901 0.004 0.380 0.472 0.144
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0188 0.88781 0.996 0.000 0.004 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0779 0.82439 0.004 0.000 0.016 0.980
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.6548 0.15848 0.000 0.116 0.276 0.608
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.1489 0.88836 0.952 0.000 0.044 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0336 0.82487 0.000 0.000 0.008 0.992
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2281 0.86291 0.904 0.000 0.000 0.096
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0188 0.87755 0.000 0.996 0.000 0.004
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.88780 1.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.5649 0.62129 0.008 0.120 0.740 0.132
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.4250 0.47489 0.000 0.000 0.724 0.276
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0524 0.88830 0.988 0.000 0.004 0.008
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1474 0.88633 0.948 0.000 0.052 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.6326 0.67003 0.000 0.264 0.632 0.104
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.4543 0.59033 0.000 0.000 0.676 0.324
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.87900 0.000 1.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.4560 0.64797 0.004 0.700 0.296 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.4998 0.04086 0.488 0.512 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0779 0.86856 0.000 0.980 0.016 0.004
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.3505 0.86726 0.864 0.000 0.088 0.048
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.7602 0.58483 0.068 0.104 0.608 0.220
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.3208 0.64011 0.004 0.148 0.848 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0336 0.87508 0.000 0.992 0.008 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2675 0.85737 0.892 0.008 0.000 0.100
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.5163 -0.41253 0.480 0.000 0.516 0.004
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.2281 0.79387 0.000 0.904 0.096 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.1970 0.84197 0.060 0.932 0.008 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0188 0.82322 0.004 0.000 0.000 0.996
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.3547 0.84999 0.840 0.016 0.144 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.4509 0.65568 0.004 0.708 0.288 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0336 0.87634 0.000 0.992 0.008 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3523 0.80136 0.032 0.856 0.112 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1637 0.81084 0.000 0.000 0.060 0.940
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.6355 0.62367 0.656 0.004 0.112 0.228
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.2111 0.57462 0.000 0.024 0.932 0.044
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.87900 0.000 1.000 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.4989 0.39542 0.000 0.000 0.528 0.472
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.6844 0.32968 0.100 0.444 0.456 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0469 0.82476 0.000 0.000 0.012 0.988
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.87900 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0592 0.81892 0.016 0.000 0.000 0.984
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.4994 0.32882 0.000 0.480 0.520 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.7065 0.15680 0.124 0.404 0.472 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.6412 0.65359 0.004 0.084 0.616 0.296
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2654 0.87246 0.888 0.000 0.108 0.004
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.4866 -0.00631 0.000 0.596 0.404 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.87900 0.000 1.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.4624 0.34770 0.340 0.000 0.000 0.660
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.87900 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.3942 0.57871 0.236 0.000 0.000 0.764
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.4855 0.52661 0.000 0.000 0.600 0.400
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.6542 0.67490 0.000 0.128 0.620 0.252
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.6756 0.68135 0.000 0.188 0.612 0.200
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0524 0.82528 0.004 0.000 0.008 0.988
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.82408 0.000 0.000 0.000 1.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1474 0.83924 0.000 0.948 0.052 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3873 0.71927 0.000 0.772 0.228 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0592 0.82384 0.000 0.000 0.016 0.984
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.7627 0.26847 0.000 0.272 0.256 0.472
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.6078 0.45992 0.312 0.068 0.620 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.87900 0.000 1.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.5986 0.63185 0.000 0.060 0.620 0.320
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.87900 0.000 1.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.1557 0.80645 0.000 0.000 0.056 0.944
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.4624 0.80422 0.784 0.000 0.052 0.164
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.3123 0.67597 0.000 0.000 0.156 0.844
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.87900 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.6084 0.67746 0.000 0.096 0.660 0.244
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.6075 0.65537 0.000 0.076 0.636 0.288
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.6654 0.61583 0.588 0.000 0.296 0.116
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.3392 0.73176 0.020 0.000 0.124 0.856
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.87900 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.3400 0.76358 0.000 0.820 0.180 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2345 0.85949 0.900 0.000 0.000 0.100
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1022 0.81642 0.000 0.000 0.032 0.968
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.5269 0.55274 0.000 0.364 0.620 0.016
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.5966 0.63568 0.000 0.060 0.624 0.316
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.5917 0.63178 0.000 0.056 0.624 0.320
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.87900 0.000 1.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.4049 0.64045 0.008 0.212 0.780 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.6448 0.67537 0.000 0.120 0.628 0.252
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.2469 0.74669 0.000 0.000 0.108 0.892
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1740 0.8058 0.000 0.012 0.932 0.000 0.056
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1956 0.8460 0.000 0.076 0.916 0.008 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.4276 -0.0413 0.616 0.000 0.000 0.004 0.380
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.2761 0.3671 0.896 0.000 0.048 0.028 0.028
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.5064 0.7205 0.132 0.000 0.024 0.740 0.104
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.3492 0.8265 0.008 0.092 0.852 0.008 0.040
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.4114 -0.0229 0.624 0.000 0.000 0.000 0.376
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.1682 0.8381 0.004 0.000 0.012 0.940 0.044
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.6833 0.5122 0.024 0.204 0.032 0.604 0.136
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.3817 0.1736 0.740 0.000 0.004 0.004 0.252
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0771 0.8384 0.000 0.000 0.020 0.976 0.004
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 5 0.5148 0.2776 0.432 0.000 0.000 0.040 0.528
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0324 0.8811 0.000 0.992 0.004 0.000 0.004
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.4219 -0.1130 0.584 0.000 0.000 0.000 0.416
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 1 0.8613 0.1323 0.444 0.044 0.188 0.112 0.212
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.7028 0.0806 0.004 0.004 0.392 0.272 0.328
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.3857 0.0871 0.688 0.000 0.000 0.000 0.312
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.2462 0.2977 0.880 0.000 0.000 0.008 0.112
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.2130 0.8455 0.000 0.080 0.908 0.012 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3160 0.8032 0.040 0.000 0.876 0.052 0.032
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0486 0.8808 0.000 0.988 0.004 0.004 0.004
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.5940 0.4439 0.012 0.532 0.056 0.008 0.392
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5635 0.4325 0.240 0.636 0.004 0.000 0.120
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0960 0.8753 0.000 0.972 0.004 0.016 0.008
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2684 0.3641 0.900 0.000 0.032 0.044 0.024
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.8124 0.1498 0.472 0.012 0.216 0.148 0.152
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1992 0.8340 0.000 0.044 0.924 0.000 0.032
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0324 0.8812 0.000 0.992 0.004 0.000 0.004
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 5 0.5343 0.2875 0.388 0.004 0.000 0.048 0.560
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.6923 -0.0397 0.372 0.004 0.284 0.000 0.340
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4338 0.7497 0.040 0.800 0.048 0.000 0.112
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.1082 0.8702 0.008 0.964 0.000 0.000 0.028
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.2464 0.8167 0.000 0.000 0.016 0.888 0.096
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.2635 0.3634 0.900 0.004 0.020 0.012 0.064
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.5324 0.5346 0.004 0.600 0.056 0.000 0.340
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0693 0.8737 0.000 0.980 0.008 0.000 0.012
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2886 0.8242 0.012 0.884 0.036 0.000 0.068
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.4457 0.7398 0.104 0.000 0.016 0.784 0.096
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.7412 -0.1862 0.304 0.004 0.400 0.024 0.268
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.7814 -0.1607 0.328 0.008 0.148 0.084 0.432
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0162 0.8817 0.000 0.996 0.004 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.4504 0.2796 0.000 0.000 0.564 0.428 0.008
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.7912 -0.0574 0.356 0.396 0.152 0.008 0.088
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1386 0.8379 0.000 0.000 0.016 0.952 0.032
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0324 0.8812 0.000 0.992 0.004 0.000 0.004
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.2551 0.8252 0.044 0.000 0.012 0.904 0.040
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.2411 0.8314 0.000 0.108 0.884 0.000 0.008
#> 117673A3-2918-4702-8583-B66ADE6E4338 1 0.8023 0.1481 0.468 0.176 0.228 0.008 0.120
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.6713 0.6486 0.072 0.048 0.664 0.136 0.080
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2095 0.3705 0.928 0.000 0.028 0.020 0.024
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.2753 0.8133 0.000 0.136 0.856 0.000 0.008
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0162 0.8817 0.000 0.996 0.004 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.5256 0.5756 0.116 0.000 0.000 0.672 0.212
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0162 0.8817 0.000 0.996 0.004 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.4895 0.6735 0.072 0.000 0.012 0.728 0.188
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.3265 0.7980 0.040 0.000 0.860 0.088 0.012
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.2291 0.8489 0.000 0.056 0.908 0.036 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.2544 0.8430 0.000 0.028 0.900 0.064 0.008
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1280 0.8382 0.008 0.000 0.008 0.960 0.024
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.2233 0.8233 0.000 0.000 0.016 0.904 0.080
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1484 0.8504 0.000 0.944 0.048 0.000 0.008
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3759 0.7625 0.000 0.808 0.056 0.000 0.136
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.1843 0.8311 0.012 0.004 0.012 0.940 0.032
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.6482 0.4456 0.000 0.080 0.052 0.560 0.308
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.2388 0.8377 0.028 0.072 0.900 0.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0162 0.8817 0.000 0.996 0.004 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.2291 0.8345 0.000 0.012 0.908 0.072 0.008
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0486 0.8808 0.000 0.988 0.004 0.004 0.004
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3193 0.8043 0.000 0.000 0.028 0.840 0.132
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.5953 -0.1485 0.540 0.000 0.000 0.124 0.336
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.2046 0.8236 0.000 0.000 0.016 0.916 0.068
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0451 0.8800 0.000 0.988 0.004 0.000 0.008
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.1954 0.8472 0.000 0.032 0.932 0.028 0.008
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.5098 0.7311 0.052 0.020 0.776 0.076 0.076
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.5956 0.0927 0.228 0.004 0.056 0.056 0.656
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.3681 0.7895 0.048 0.008 0.016 0.848 0.080
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0162 0.8817 0.000 0.996 0.004 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.3255 0.7951 0.000 0.848 0.052 0.000 0.100
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 5 0.4953 0.2651 0.440 0.000 0.000 0.028 0.532
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1386 0.8369 0.000 0.000 0.016 0.952 0.032
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.2445 0.8319 0.000 0.108 0.884 0.004 0.004
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.2162 0.8378 0.000 0.012 0.916 0.064 0.008
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.2149 0.8469 0.000 0.036 0.916 0.048 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0324 0.8812 0.000 0.992 0.004 0.000 0.004
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.2393 0.8364 0.016 0.080 0.900 0.000 0.004
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.2284 0.8495 0.000 0.056 0.912 0.028 0.004
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.2548 0.8180 0.000 0.004 0.028 0.896 0.072
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.9127 0.000 0.000 1.000 0.000 0.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.9127 0.000 0.000 1.000 0.000 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.4775 0.6904 0.692 0.000 0.000 0.084 0.208 0.016
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.0508 0.7577 0.012 0.000 0.004 0.000 0.984 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 5 0.4702 0.0274 0.044 0.000 0.000 0.460 0.496 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.0858 0.8960 0.028 0.004 0.968 0.000 0.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.3229 0.6981 0.816 0.000 0.000 0.000 0.140 0.044
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3014 0.7552 0.000 0.000 0.000 0.804 0.012 0.184
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.6611 0.2126 0.000 0.116 0.000 0.404 0.080 0.400
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.4308 0.3270 0.516 0.000 0.000 0.012 0.468 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2869 0.7657 0.000 0.000 0.000 0.832 0.020 0.148
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3394 0.6864 0.776 0.000 0.000 0.200 0.024 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.8936 0.000 1.000 0.000 0.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.2728 0.7061 0.860 0.000 0.000 0.000 0.100 0.040
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.2649 0.7120 0.000 0.060 0.004 0.028 0.888 0.020
#> 0EA8288E-824D-4304-A053-5A833361F5C5 6 0.3809 0.4647 0.016 0.000 0.048 0.148 0.000 0.788
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.4110 0.4976 0.608 0.000 0.000 0.000 0.376 0.016
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 5 0.4139 0.1944 0.336 0.000 0.000 0.000 0.640 0.024
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.0000 0.9127 0.000 0.000 1.000 0.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0935 0.8937 0.000 0.000 0.964 0.004 0.032 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0937 0.8728 0.000 0.960 0.000 0.000 0.000 0.040
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 6 0.3053 0.6156 0.000 0.172 0.000 0.004 0.012 0.812
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0972 0.8724 0.028 0.964 0.000 0.000 0.008 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1349 0.8545 0.000 0.940 0.000 0.004 0.000 0.056
#> 932C8E98-A352-409C-8744-3D49FABCE425 5 0.0858 0.7552 0.028 0.000 0.000 0.004 0.968 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.1232 0.7458 0.000 0.000 0.004 0.016 0.956 0.024
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0000 0.9127 0.000 0.000 1.000 0.000 0.000 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8936 0.000 1.000 0.000 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1812 0.6935 0.912 0.000 0.000 0.080 0.000 0.008
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 6 0.6601 0.2551 0.148 0.000 0.280 0.000 0.076 0.496
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.3563 0.7196 0.000 0.800 0.000 0.000 0.108 0.092
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0622 0.8851 0.012 0.980 0.000 0.000 0.000 0.008
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.1387 0.7307 0.068 0.000 0.000 0.932 0.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.1838 0.7151 0.068 0.000 0.000 0.000 0.916 0.016
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 6 0.3076 0.5804 0.000 0.240 0.000 0.000 0.000 0.760
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0146 0.8928 0.000 0.996 0.000 0.000 0.000 0.004
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.4122 0.4434 0.020 0.660 0.000 0.000 0.004 0.316
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.5671 0.4525 0.000 0.000 0.000 0.508 0.312 0.180
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.4325 0.1043 0.480 0.000 0.504 0.008 0.000 0.008
#> FF7021BB-414B-4466-BC43-372D856E6A1D 6 0.3551 0.5524 0.000 0.000 0.040 0.012 0.144 0.804
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.8936 0.000 1.000 0.000 0.000 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.5598 0.4100 0.000 0.000 0.612 0.240 0.032 0.116
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.5061 0.0355 0.004 0.476 0.008 0.000 0.468 0.044
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.2357 0.7691 0.012 0.000 0.000 0.872 0.000 0.116
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0146 0.8928 0.000 0.996 0.000 0.000 0.000 0.004
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.2446 0.7329 0.012 0.000 0.000 0.864 0.124 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.9127 0.000 0.000 1.000 0.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.3171 0.6153 0.008 0.168 0.004 0.000 0.812 0.008
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.5907 0.3820 0.000 0.020 0.568 0.020 0.300 0.092
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 5 0.0935 0.7520 0.032 0.000 0.000 0.000 0.964 0.004
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.0865 0.8891 0.000 0.036 0.964 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.8936 0.000 1.000 0.000 0.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.2969 0.5534 0.224 0.000 0.000 0.776 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.8936 0.000 1.000 0.000 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.2340 0.6602 0.148 0.000 0.000 0.852 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.3388 0.7412 0.004 0.000 0.804 0.156 0.036 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.9127 0.000 0.000 1.000 0.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0146 0.9113 0.000 0.000 0.996 0.000 0.000 0.004
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2119 0.7702 0.000 0.000 0.000 0.904 0.060 0.036
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.1196 0.7426 0.040 0.000 0.000 0.952 0.008 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0363 0.8865 0.000 0.988 0.012 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.3860 -0.0177 0.000 0.528 0.000 0.000 0.000 0.472
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.4233 0.7185 0.000 0.004 0.000 0.736 0.180 0.080
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 6 0.2597 0.4179 0.000 0.000 0.000 0.176 0.000 0.824
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0146 0.9113 0.000 0.004 0.996 0.000 0.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0146 0.8928 0.000 0.996 0.000 0.000 0.000 0.004
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0146 0.9113 0.000 0.000 0.996 0.000 0.000 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1765 0.8294 0.000 0.904 0.000 0.000 0.000 0.096
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 4 0.3020 0.7396 0.080 0.000 0.000 0.844 0.000 0.076
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.6158 0.5939 0.568 0.000 0.000 0.248 0.076 0.108
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.3855 0.7113 0.000 0.000 0.000 0.704 0.024 0.272
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8936 0.000 1.000 0.000 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.9127 0.000 0.000 1.000 0.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.2571 0.8216 0.000 0.000 0.876 0.000 0.064 0.060
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.4362 0.3263 0.344 0.000 0.004 0.004 0.020 0.628
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.5089 0.6450 0.000 0.000 0.000 0.592 0.108 0.300
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8936 0.000 1.000 0.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 6 0.3854 0.0739 0.000 0.464 0.000 0.000 0.000 0.536
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2726 0.7016 0.848 0.000 0.000 0.136 0.008 0.008
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.3309 0.7557 0.016 0.000 0.000 0.788 0.004 0.192
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0146 0.9113 0.000 0.004 0.996 0.000 0.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0000 0.9127 0.000 0.000 1.000 0.000 0.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.9127 0.000 0.000 1.000 0.000 0.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8936 0.000 1.000 0.000 0.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0458 0.9058 0.000 0.000 0.984 0.000 0.016 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0000 0.9127 0.000 0.000 1.000 0.000 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.4408 0.6959 0.000 0.000 0.000 0.664 0.056 0.280
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 17471 rows and 87 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 0.953 0.960 0.967 0.3078 0.696 0.696
#> 3 3 0.396 0.526 0.745 0.8189 0.765 0.662
#> 4 4 0.578 0.561 0.809 0.2636 0.803 0.573
#> 5 5 0.628 0.638 0.767 0.0909 0.886 0.606
#> 6 6 0.690 0.690 0.792 0.0505 0.933 0.694
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 1 0.1184 0.958 0.984 0.016
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.970 0.000 1.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 2 0.2948 0.966 0.052 0.948
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.0000 0.970 0.000 1.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 2 0.0000 0.970 0.000 1.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.3431 0.963 0.064 0.936
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.7528 0.736 0.784 0.216
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 2 0.0000 0.970 0.000 1.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.3114 0.966 0.056 0.944
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 2 0.2948 0.966 0.052 0.948
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 2 0.0000 0.970 0.000 1.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 2 0.4690 0.930 0.100 0.900
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.3114 0.966 0.056 0.944
#> 694B0504-095E-431A-84F2-B4387072138E 2 0.4690 0.930 0.100 0.900
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.2948 0.968 0.052 0.948
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.1633 0.957 0.976 0.024
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 2 0.0000 0.970 0.000 1.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 2 0.2948 0.966 0.052 0.948
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.3584 0.960 0.068 0.932
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.0000 0.970 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.2948 0.967 0.052 0.948
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 1 0.0000 0.952 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.2948 0.968 0.052 0.948
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.970 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 2 0.0000 0.970 0.000 1.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0000 0.970 0.000 1.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 1 0.4562 0.914 0.904 0.096
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.1414 0.972 0.020 0.980
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 2 0.2948 0.966 0.052 0.948
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.0000 0.952 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0376 0.970 0.004 0.996
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2948 0.966 0.052 0.948
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 2 0.0000 0.970 0.000 1.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.2948 0.968 0.052 0.948
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 1 0.0000 0.952 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 1 0.4298 0.922 0.912 0.088
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 1 0.4298 0.922 0.912 0.088
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.0000 0.970 0.000 1.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.1843 0.971 0.028 0.972
#> FF7021BB-414B-4466-BC43-372D856E6A1D 1 0.1633 0.957 0.976 0.024
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2603 0.970 0.044 0.956
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 2 0.0000 0.970 0.000 1.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0376 0.970 0.004 0.996
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 2 0.0000 0.970 0.000 1.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.2948 0.967 0.052 0.948
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 2 0.0000 0.970 0.000 1.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.3584 0.960 0.068 0.932
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.2948 0.968 0.052 0.948
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.0000 0.970 0.000 1.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 2 0.4690 0.930 0.100 0.900
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.3584 0.960 0.068 0.932
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.970 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 2 0.2948 0.966 0.052 0.948
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.970 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 2 0.0000 0.970 0.000 1.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.2948 0.968 0.052 0.948
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.2948 0.967 0.052 0.948
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.1184 0.971 0.016 0.984
#> C41F3064-4483-4796-B860-82155BAA5157 2 0.0000 0.970 0.000 1.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 2 0.0000 0.970 0.000 1.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.3431 0.962 0.064 0.936
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 1 0.1184 0.958 0.984 0.016
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 2 0.0000 0.970 0.000 1.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 1 0.2236 0.953 0.964 0.036
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.0000 0.970 0.000 1.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.3733 0.957 0.072 0.928
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.2948 0.967 0.052 0.948
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.2948 0.967 0.052 0.948
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.1184 0.958 0.984 0.016
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.0000 0.952 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 2 0.1633 0.971 0.024 0.976
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.2603 0.969 0.044 0.956
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.2948 0.967 0.052 0.948
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.970 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.0000 0.952 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 2 0.1843 0.971 0.028 0.972
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.3733 0.957 0.072 0.928
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 1 0.3274 0.941 0.940 0.060
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 2 0.2948 0.966 0.052 0.948
#> AF8AB83D-2917-4752-8C38-CF84C565B565 2 0.1633 0.971 0.024 0.976
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.970 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.2948 0.967 0.052 0.948
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0000 0.970 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.970 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.3584 0.960 0.068 0.932
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.2948 0.967 0.052 0.948
#> 0D907A63-D23F-4684-80D9-0BA552435B86 2 0.0000 0.970 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1765 0.929777 0.004 0.040 0.956
#> F569915C-8F77-4D67-9730-30824DB57EE5 1 0.6307 0.369989 0.512 0.488 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 2 0.6075 0.542112 0.316 0.676 0.008
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.6180 0.375486 0.416 0.584 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.5178 0.369088 0.744 0.256 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.1267 0.629995 0.024 0.972 0.004
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 3 0.5480 0.594900 0.004 0.264 0.732
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.5760 0.498337 0.672 0.328 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.1031 0.624737 0.024 0.976 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 2 0.6075 0.542112 0.316 0.676 0.008
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0592 0.537402 0.988 0.012 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 2 0.7065 0.532487 0.288 0.664 0.048
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.1964 0.631599 0.056 0.944 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 2 0.7065 0.532487 0.288 0.664 0.048
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.4062 0.624317 0.164 0.836 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.1411 0.929220 0.000 0.036 0.964
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.5529 0.321647 0.704 0.296 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 2 0.6075 0.542112 0.316 0.676 0.008
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0848 0.621146 0.008 0.984 0.008
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.6307 0.369989 0.512 0.488 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0892 0.622593 0.020 0.980 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.0424 0.917475 0.008 0.000 0.992
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.4002 0.625643 0.160 0.840 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.5810 -0.000407 0.336 0.664 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.5529 0.321647 0.704 0.296 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.6008 0.451752 0.372 0.628 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.3644 0.882790 0.004 0.124 0.872
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.5529 0.111552 0.296 0.704 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 2 0.6047 0.545738 0.312 0.680 0.008
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.0424 0.917475 0.008 0.000 0.992
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.5678 0.059320 0.316 0.684 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.6047 0.545738 0.312 0.680 0.008
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 2 0.6307 0.252777 0.488 0.512 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.4178 0.622323 0.172 0.828 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.0424 0.917475 0.008 0.000 0.992
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 3 0.3644 0.884887 0.004 0.124 0.872
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 3 0.3644 0.884887 0.004 0.124 0.872
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.6286 0.276078 0.464 0.536 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.5650 0.549042 0.312 0.688 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.2096 0.928075 0.004 0.052 0.944
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.4121 0.624851 0.168 0.832 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.0592 0.537402 0.988 0.012 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.6252 -0.245098 0.444 0.556 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 2 0.6307 0.252777 0.488 0.512 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1031 0.620378 0.024 0.976 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 2 0.6305 0.263010 0.484 0.516 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0848 0.621146 0.008 0.984 0.008
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.4178 0.622323 0.172 0.828 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.6309 0.355007 0.504 0.496 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 2 0.7095 0.528531 0.292 0.660 0.048
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0848 0.621146 0.008 0.984 0.008
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 1 0.6225 0.442380 0.568 0.432 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 2 0.6047 0.545738 0.312 0.680 0.008
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.5810 -0.000407 0.336 0.664 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 2 0.6307 0.252777 0.488 0.512 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.4575 0.618547 0.184 0.812 0.004
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.1031 0.621855 0.024 0.976 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.6274 -0.276960 0.456 0.544 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.4974 0.395181 0.764 0.236 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 2 0.6305 0.262654 0.484 0.516 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0661 0.622114 0.008 0.988 0.004
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.1878 0.929509 0.004 0.044 0.952
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.2356 0.530588 0.928 0.072 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.2400 0.923317 0.004 0.064 0.932
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.6309 0.355007 0.504 0.496 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0829 0.619421 0.004 0.984 0.012
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0747 0.623576 0.016 0.984 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0892 0.622593 0.020 0.980 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.1031 0.927237 0.000 0.024 0.976
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.0424 0.917475 0.008 0.000 0.992
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 2 0.5733 0.533658 0.324 0.676 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.1031 0.616765 0.024 0.976 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0747 0.623576 0.016 0.984 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.6309 -0.385423 0.496 0.504 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.0424 0.917475 0.008 0.000 0.992
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 2 0.5678 0.542603 0.316 0.684 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.1182 0.617748 0.012 0.976 0.012
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.2945 0.909861 0.004 0.088 0.908
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 2 0.6047 0.545738 0.312 0.680 0.008
#> AF8AB83D-2917-4752-8C38-CF84C565B565 2 0.5706 0.538515 0.320 0.680 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 1 0.6215 0.445381 0.572 0.428 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0747 0.623576 0.016 0.984 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 1 0.6307 0.369989 0.512 0.488 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.5905 -0.054459 0.352 0.648 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0848 0.621146 0.008 0.984 0.008
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0747 0.623576 0.016 0.984 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.0592 0.537402 0.988 0.012 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.1677 0.9376 0.012 0.040 0.948 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 4 0.4999 0.1416 0.000 0.492 0.000 0.508
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0376 0.7623 0.992 0.004 0.000 0.004
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.7277 0.4308 0.540 0.228 0.000 0.232
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.4624 0.0383 0.340 0.000 0.000 0.660
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.3402 0.6224 0.164 0.832 0.000 0.004
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 3 0.4767 0.6803 0.256 0.020 0.724 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.4699 0.3893 0.004 0.320 0.000 0.676
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.1406 0.7203 0.024 0.960 0.000 0.016
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0376 0.7623 0.992 0.004 0.000 0.004
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0188 0.4889 0.004 0.000 0.000 0.996
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1913 0.7526 0.940 0.020 0.040 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.2859 0.6587 0.112 0.880 0.000 0.008
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1913 0.7526 0.940 0.020 0.040 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.5508 -0.0975 0.476 0.508 0.000 0.016
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.1452 0.9371 0.008 0.036 0.956 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.5039 -0.1005 0.404 0.004 0.000 0.592
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0376 0.7623 0.992 0.004 0.000 0.004
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0592 0.7243 0.016 0.984 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 4 0.4999 0.1416 0.000 0.492 0.000 0.508
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0469 0.7263 0.000 0.988 0.000 0.012
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.0000 0.9278 0.000 0.000 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5254 0.4274 0.300 0.672 0.000 0.028
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.4564 0.3401 0.000 0.672 0.000 0.328
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.5039 -0.1005 0.404 0.004 0.000 0.592
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.7241 0.4234 0.536 0.276 0.000 0.188
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.3161 0.8934 0.012 0.124 0.864 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.4331 0.4189 0.000 0.712 0.000 0.288
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0188 0.7619 0.996 0.004 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.0000 0.9278 0.000 0.000 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4454 0.3814 0.000 0.692 0.000 0.308
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.0336 0.7628 0.992 0.008 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.3801 0.6906 0.780 0.000 0.000 0.220
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.5607 -0.1304 0.484 0.496 0.000 0.020
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.0000 0.9278 0.000 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 3 0.3224 0.8954 0.016 0.120 0.864 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 3 0.3224 0.8954 0.016 0.120 0.864 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.7099 0.5045 0.552 0.168 0.000 0.280
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4679 0.6803 0.772 0.184 0.000 0.044
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.1938 0.9356 0.012 0.052 0.936 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.5222 0.4544 0.280 0.688 0.000 0.032
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0188 0.4889 0.004 0.000 0.000 0.996
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.5366 -0.0166 0.012 0.548 0.000 0.440
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.3801 0.6906 0.780 0.000 0.000 0.220
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0592 0.7255 0.000 0.984 0.000 0.016
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.3801 0.6913 0.780 0.000 0.000 0.220
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0592 0.7243 0.016 0.984 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5607 -0.1304 0.484 0.496 0.000 0.020
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.5406 0.1652 0.012 0.480 0.000 0.508
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2099 0.7529 0.936 0.020 0.040 0.004
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0592 0.7243 0.016 0.984 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 4 0.5105 0.2683 0.004 0.432 0.000 0.564
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0188 0.7619 0.996 0.004 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.4564 0.3401 0.000 0.672 0.000 0.328
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.3801 0.6906 0.780 0.000 0.000 0.220
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.5530 0.4923 0.632 0.336 0.000 0.032
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0592 0.7251 0.000 0.984 0.000 0.016
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.4967 -0.0205 0.000 0.548 0.000 0.452
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.4500 0.0808 0.316 0.000 0.000 0.684
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.3801 0.6921 0.780 0.000 0.000 0.220
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0469 0.7257 0.012 0.988 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.1767 0.9373 0.012 0.044 0.944 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2053 0.4441 0.072 0.004 0.000 0.924
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.2179 0.9319 0.012 0.064 0.924 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 4 0.5406 0.1652 0.012 0.480 0.000 0.508
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0779 0.7226 0.016 0.980 0.004 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0336 0.7275 0.000 0.992 0.000 0.008
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0469 0.7263 0.000 0.988 0.000 0.012
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.1151 0.9355 0.008 0.024 0.968 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.0000 0.9278 0.000 0.000 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.6936 0.4995 0.568 0.284 0.000 0.148
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0592 0.7235 0.000 0.984 0.000 0.016
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0336 0.7275 0.000 0.992 0.000 0.008
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.4999 -0.1696 0.000 0.508 0.000 0.492
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.0000 0.9278 0.000 0.000 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.6955 0.4918 0.560 0.296 0.000 0.144
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.1114 0.7245 0.016 0.972 0.004 0.008
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.2610 0.9187 0.012 0.088 0.900 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0188 0.7619 0.996 0.004 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.6917 0.4987 0.568 0.288 0.000 0.144
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 4 0.5097 0.2745 0.004 0.428 0.000 0.568
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0336 0.7275 0.000 0.992 0.000 0.008
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 4 0.4999 0.1416 0.000 0.492 0.000 0.508
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.4661 0.2903 0.000 0.652 0.000 0.348
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0707 0.7248 0.020 0.980 0.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0336 0.7275 0.000 0.992 0.000 0.008
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0188 0.4889 0.004 0.000 0.000 0.996
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 5 0.088 0.91981 0.000 0.032 0.000 0.000 0.968
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.338 0.82879 0.000 0.176 0.808 0.016 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.408 0.46113 0.668 0.000 0.004 0.328 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.833 -0.02747 0.312 0.132 0.256 0.300 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.187 0.47348 0.020 0.000 0.052 0.928 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.348 0.70572 0.168 0.812 0.012 0.008 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 5 0.419 0.64365 0.256 0.012 0.000 0.008 0.724
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.429 0.60727 0.000 0.080 0.768 0.152 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.131 0.81375 0.024 0.956 0.020 0.000 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.408 0.46113 0.668 0.000 0.004 0.328 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.414 0.37557 0.000 0.000 0.384 0.616 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.255 0.60767 0.904 0.012 0.000 0.040 0.044
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.325 0.75228 0.080 0.864 0.016 0.040 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.255 0.60767 0.904 0.012 0.000 0.040 0.044
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.702 0.18437 0.292 0.484 0.028 0.196 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 5 0.208 0.91941 0.000 0.032 0.016 0.024 0.928
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.365 0.43779 0.092 0.000 0.084 0.824 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.408 0.46113 0.668 0.000 0.004 0.328 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.131 0.81661 0.000 0.960 0.016 0.008 0.016
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.338 0.82879 0.000 0.176 0.808 0.016 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.112 0.80724 0.000 0.956 0.044 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.175 0.90766 0.000 0.000 0.028 0.036 0.936
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.601 0.51177 0.208 0.648 0.036 0.108 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 3 0.415 0.75200 0.000 0.344 0.652 0.004 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.365 0.43779 0.092 0.000 0.084 0.824 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.847 0.00318 0.332 0.180 0.252 0.236 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.223 0.88061 0.000 0.116 0.000 0.000 0.884
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 3 0.436 0.65032 0.000 0.412 0.584 0.004 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.189 0.60382 0.916 0.000 0.080 0.004 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.175 0.90766 0.000 0.000 0.028 0.036 0.936
#> 36EDD202-A845-4CE7-95D5-A515C471262E 3 0.429 0.69793 0.000 0.384 0.612 0.004 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.205 0.60411 0.912 0.004 0.080 0.004 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.520 0.51579 0.684 0.000 0.128 0.188 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.717 0.15968 0.296 0.472 0.036 0.196 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 5 0.175 0.90766 0.000 0.000 0.028 0.036 0.936
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 5 0.234 0.88161 0.000 0.112 0.004 0.000 0.884
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 5 0.234 0.88161 0.000 0.112 0.004 0.000 0.884
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.786 -0.07594 0.336 0.148 0.116 0.400 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.521 0.48302 0.732 0.156 0.044 0.068 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.112 0.91866 0.000 0.044 0.000 0.000 0.956
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.594 0.53416 0.192 0.664 0.044 0.100 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.414 0.37557 0.000 0.000 0.384 0.616 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.422 0.81243 0.000 0.260 0.716 0.024 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.520 0.51579 0.684 0.000 0.128 0.188 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.120 0.80419 0.000 0.952 0.048 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.571 0.47624 0.592 0.000 0.116 0.292 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.131 0.81661 0.000 0.960 0.016 0.008 0.016
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.717 0.15968 0.296 0.472 0.036 0.196 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.458 0.81268 0.000 0.196 0.732 0.072 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.543 0.44808 0.620 0.012 0.004 0.320 0.044
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.128 0.81470 0.000 0.960 0.020 0.004 0.016
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 3 0.374 0.78446 0.000 0.140 0.808 0.052 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.189 0.60382 0.916 0.000 0.080 0.004 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 3 0.415 0.75200 0.000 0.344 0.652 0.004 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.520 0.51579 0.684 0.000 0.128 0.188 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.706 0.23871 0.496 0.320 0.056 0.128 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.104 0.81125 0.000 0.964 0.032 0.000 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.399 0.81176 0.000 0.252 0.732 0.016 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.194 0.48194 0.012 0.000 0.068 0.920 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.531 0.51442 0.672 0.000 0.132 0.196 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.168 0.80335 0.000 0.940 0.044 0.004 0.012
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 5 0.120 0.91999 0.000 0.040 0.000 0.004 0.956
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.457 0.43755 0.020 0.000 0.348 0.632 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 5 0.134 0.91544 0.000 0.056 0.000 0.000 0.944
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.458 0.81268 0.000 0.196 0.732 0.072 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.147 0.80498 0.000 0.952 0.024 0.004 0.020
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.106 0.81624 0.000 0.968 0.020 0.008 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.112 0.80724 0.000 0.956 0.044 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 5 0.181 0.91797 0.000 0.020 0.016 0.024 0.940
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 5 0.175 0.90766 0.000 0.000 0.028 0.036 0.936
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.759 0.04381 0.296 0.256 0.048 0.400 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.157 0.78902 0.000 0.936 0.060 0.004 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.106 0.81624 0.000 0.968 0.020 0.008 0.004
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.353 0.83232 0.000 0.192 0.792 0.016 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.175 0.90766 0.000 0.000 0.028 0.036 0.936
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.761 0.04268 0.292 0.268 0.048 0.392 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.146 0.80533 0.000 0.952 0.028 0.004 0.016
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 5 0.173 0.90442 0.000 0.080 0.000 0.000 0.920
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.104 0.61046 0.960 0.000 0.000 0.040 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.755 0.04203 0.296 0.260 0.044 0.400 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.369 0.78024 0.000 0.136 0.812 0.052 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.106 0.81624 0.000 0.968 0.020 0.008 0.004
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.338 0.82879 0.000 0.176 0.808 0.016 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 3 0.417 0.77249 0.000 0.320 0.672 0.008 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.147 0.81740 0.004 0.956 0.016 0.008 0.016
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.106 0.81624 0.000 0.968 0.020 0.008 0.004
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.415 0.37008 0.000 0.000 0.388 0.612 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 6 0.0912 0.8775 0.000 0.012 0.004 0.008 0.004 0.972
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.1405 0.8405 0.000 0.024 0.948 0.024 0.004 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 5 0.3201 0.4130 0.208 0.000 0.000 0.012 0.780 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.6369 0.3979 0.008 0.092 0.268 0.080 0.552 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.3563 0.6755 0.000 0.000 0.000 0.664 0.336 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.3461 0.7224 0.152 0.804 0.008 0.000 0.036 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 6 0.5166 0.6451 0.144 0.000 0.000 0.044 0.120 0.692
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.3201 0.6864 0.000 0.012 0.780 0.208 0.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.1498 0.8717 0.000 0.940 0.032 0.000 0.028 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 5 0.3201 0.4130 0.208 0.000 0.000 0.012 0.780 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2092 0.7059 0.000 0.000 0.124 0.876 0.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.4833 0.2540 0.516 0.000 0.000 0.000 0.428 0.056
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.2830 0.7569 0.000 0.836 0.020 0.000 0.144 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.4833 0.2540 0.516 0.000 0.000 0.000 0.428 0.056
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.4744 0.3583 0.000 0.440 0.032 0.000 0.520 0.008
#> 0EA8288E-824D-4304-A053-5A833361F5C5 6 0.2822 0.8743 0.008 0.016 0.000 0.012 0.096 0.868
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.4601 0.5897 0.004 0.000 0.032 0.556 0.408 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 5 0.3201 0.4130 0.208 0.000 0.000 0.012 0.780 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.1596 0.8775 0.000 0.944 0.012 0.004 0.020 0.020
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1405 0.8405 0.000 0.024 0.948 0.024 0.004 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.1542 0.8709 0.000 0.936 0.052 0.008 0.004 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 6 0.3977 0.8467 0.008 0.000 0.004 0.060 0.152 0.776
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.4371 0.2488 0.000 0.620 0.036 0.000 0.344 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 3 0.3419 0.7846 0.000 0.152 0.804 0.040 0.004 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.4601 0.5897 0.004 0.000 0.032 0.556 0.408 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.6120 0.4565 0.008 0.132 0.272 0.032 0.556 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 6 0.1897 0.8471 0.000 0.084 0.000 0.004 0.004 0.908
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 3 0.4088 0.7131 0.000 0.240 0.716 0.040 0.004 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0260 0.6568 0.992 0.000 0.000 0.000 0.008 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 6 0.3977 0.8467 0.008 0.000 0.004 0.060 0.152 0.776
#> 36EDD202-A845-4CE7-95D5-A515C471262E 3 0.4013 0.7266 0.000 0.228 0.728 0.040 0.004 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.0405 0.6563 0.988 0.004 0.000 0.000 0.008 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.3702 0.5986 0.760 0.000 0.008 0.208 0.024 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.4736 0.3766 0.000 0.432 0.032 0.000 0.528 0.008
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 6 0.3977 0.8467 0.008 0.000 0.004 0.060 0.152 0.776
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 6 0.1897 0.8470 0.000 0.084 0.000 0.004 0.004 0.908
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 6 0.1897 0.8470 0.000 0.084 0.000 0.004 0.004 0.908
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 5 0.6519 0.3825 0.008 0.124 0.096 0.204 0.568 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 5 0.6719 0.0444 0.388 0.148 0.032 0.020 0.412 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 6 0.0603 0.8750 0.000 0.016 0.000 0.000 0.004 0.980
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.4348 0.3094 0.000 0.640 0.040 0.000 0.320 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2092 0.7059 0.000 0.000 0.124 0.876 0.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.3184 0.8169 0.000 0.120 0.836 0.016 0.028 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.3702 0.5986 0.760 0.000 0.008 0.208 0.024 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1889 0.8631 0.000 0.920 0.056 0.020 0.004 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.5828 0.4081 0.516 0.000 0.004 0.208 0.272 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.1596 0.8775 0.000 0.944 0.012 0.004 0.020 0.020
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.4736 0.3766 0.000 0.432 0.032 0.000 0.528 0.008
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.3309 0.8218 0.000 0.052 0.844 0.076 0.028 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 5 0.4051 0.3975 0.172 0.000 0.000 0.012 0.760 0.056
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.1317 0.8777 0.000 0.956 0.016 0.004 0.008 0.016
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 3 0.2070 0.8129 0.000 0.012 0.896 0.092 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0260 0.6568 0.992 0.000 0.000 0.000 0.008 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 3 0.3419 0.7846 0.000 0.152 0.804 0.040 0.004 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.3702 0.5986 0.760 0.000 0.008 0.208 0.024 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 5 0.6825 0.3483 0.280 0.300 0.044 0.000 0.376 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.1299 0.8794 0.000 0.952 0.036 0.004 0.004 0.004
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.2766 0.8081 0.000 0.124 0.852 0.020 0.004 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.3601 0.6851 0.000 0.000 0.004 0.684 0.312 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.4053 0.5908 0.744 0.000 0.012 0.204 0.040 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1768 0.8697 0.000 0.932 0.044 0.004 0.008 0.012
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 6 0.0603 0.8758 0.000 0.016 0.000 0.004 0.000 0.980
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.3492 0.7193 0.000 0.000 0.120 0.804 0.076 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 6 0.0790 0.8729 0.000 0.032 0.000 0.000 0.000 0.968
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.3309 0.8218 0.000 0.052 0.844 0.076 0.028 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.1350 0.8696 0.000 0.952 0.020 0.000 0.008 0.020
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.1623 0.8713 0.000 0.940 0.020 0.004 0.032 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1542 0.8709 0.000 0.936 0.052 0.008 0.004 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 6 0.3051 0.8719 0.008 0.012 0.004 0.020 0.096 0.860
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 6 0.3977 0.8467 0.008 0.000 0.004 0.060 0.152 0.776
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 5 0.6048 0.5378 0.020 0.232 0.032 0.116 0.600 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.2265 0.8345 0.000 0.896 0.076 0.024 0.004 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.1706 0.8711 0.000 0.936 0.024 0.004 0.032 0.004
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.1708 0.8438 0.000 0.040 0.932 0.024 0.004 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.3977 0.8467 0.008 0.000 0.004 0.060 0.152 0.776
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 5 0.5995 0.5412 0.016 0.244 0.032 0.112 0.596 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.1705 0.8686 0.000 0.940 0.024 0.012 0.008 0.016
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 6 0.1204 0.8651 0.000 0.056 0.000 0.000 0.000 0.944
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.3804 0.2605 0.576 0.000 0.000 0.000 0.424 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 5 0.6032 0.5405 0.020 0.236 0.032 0.112 0.600 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.2019 0.8116 0.000 0.012 0.900 0.088 0.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.1706 0.8711 0.000 0.936 0.024 0.004 0.032 0.004
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.1405 0.8405 0.000 0.024 0.948 0.024 0.004 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 3 0.3207 0.7930 0.000 0.124 0.828 0.044 0.004 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.1448 0.8788 0.000 0.948 0.012 0.000 0.024 0.016
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.1623 0.8713 0.000 0.940 0.020 0.004 0.032 0.004
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.2135 0.7032 0.000 0.000 0.128 0.872 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", "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 17471 rows and 87 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.656 0.819 0.919 0.4366 0.543 0.543
#> 3 3 0.837 0.918 0.948 0.4504 0.603 0.393
#> 4 4 0.753 0.755 0.884 0.1449 0.836 0.586
#> 5 5 0.645 0.623 0.785 0.0787 0.854 0.544
#> 6 6 0.661 0.467 0.659 0.0508 0.844 0.466
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.000 0.837 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 1 0.000 0.937 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.662 0.749 0.828 0.172
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.000 0.937 1.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.000 0.937 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.653 0.768 0.832 0.168
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 2 0.000 0.837 0.000 1.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.000 0.937 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.985 0.417 0.428 0.572
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.000 0.937 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.000 0.937 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 2 0.932 0.557 0.348 0.652
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 1 0.443 0.866 0.908 0.092
#> 694B0504-095E-431A-84F2-B4387072138E 2 0.936 0.551 0.352 0.648
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 1 0.373 0.884 0.928 0.072
#> 0EA8288E-824D-4304-A053-5A833361F5C5 2 0.000 0.837 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.000 0.937 1.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.662 0.749 0.828 0.172
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.795 0.693 0.240 0.760
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.000 0.937 1.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 1 0.000 0.937 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.000 0.837 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 1 0.000 0.937 1.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 1 0.000 0.937 1.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.000 0.937 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.000 0.937 1.000 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.000 0.837 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 1 0.416 0.874 0.916 0.084
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.662 0.749 0.828 0.172
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.000 0.837 0.000 1.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 1 0.416 0.874 0.916 0.084
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.000 0.937 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.000 0.937 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.909 0.583 0.324 0.676
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.000 0.837 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.000 0.837 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.000 0.837 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.000 0.937 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.000 0.937 1.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.000 0.837 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 1 0.000 0.937 1.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.000 0.937 1.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 1 0.000 0.937 1.000 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.000 0.937 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 1 0.946 0.319 0.636 0.364
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.000 0.937 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.000 0.837 0.000 1.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.999 0.266 0.484 0.516
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.000 0.937 1.000 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 2 0.936 0.551 0.352 0.648
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.966 0.491 0.392 0.608
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 1 0.000 0.937 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.662 0.749 0.828 0.172
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 1 0.430 0.870 0.912 0.088
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.000 0.937 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.000 0.937 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 1 0.430 0.870 0.912 0.088
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 1 0.000 0.937 1.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.000 0.937 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.000 0.937 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.992 0.367 0.448 0.552
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.000 0.837 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.000 0.937 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.000 0.837 0.000 1.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.000 0.937 1.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.000 0.837 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.990 0.388 0.440 0.560
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 1 0.936 0.358 0.648 0.352
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 2 0.000 0.837 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 2 0.000 0.837 0.000 1.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.000 0.937 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 1 0.949 0.306 0.632 0.368
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 1 0.416 0.874 0.916 0.084
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 1 0.000 0.937 1.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 2 0.000 0.837 0.000 1.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.000 0.937 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.625 0.746 0.156 0.844
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.000 0.837 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.662 0.749 0.828 0.172
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.000 0.937 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 1 0.000 0.937 1.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 1 0.000 0.937 1.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 1 0.000 0.937 1.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 1 0.000 0.937 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.891 0.621 0.308 0.692
#> A247D92D-253A-4BEC-B450-184AF90D17D0 1 0.430 0.870 0.912 0.088
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.000 0.937 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0747 0.992 0.000 0.016 0.984
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.3482 0.869 0.128 0.872 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.2804 0.910 0.924 0.060 0.016
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.1163 0.931 0.972 0.028 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.924 1.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0592 0.940 0.012 0.988 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 3 0.0747 0.975 0.016 0.000 0.984
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.1163 0.931 0.972 0.028 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.1267 0.935 0.004 0.972 0.024
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.2269 0.917 0.944 0.040 0.016
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.1163 0.931 0.972 0.028 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.4731 0.845 0.840 0.128 0.032
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0592 0.940 0.012 0.988 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.4931 0.833 0.828 0.140 0.032
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0424 0.941 0.008 0.992 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.0000 0.986 0.000 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.1031 0.930 0.976 0.024 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.2804 0.910 0.924 0.060 0.016
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.1163 0.934 0.000 0.972 0.028
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.5327 0.639 0.728 0.272 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0424 0.940 0.008 0.992 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.0747 0.992 0.000 0.016 0.984
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1031 0.939 0.024 0.976 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2448 0.905 0.076 0.924 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1163 0.931 0.972 0.028 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.4842 0.758 0.224 0.776 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0747 0.992 0.000 0.016 0.984
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.942 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2902 0.908 0.920 0.064 0.016
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.0000 0.986 0.000 0.000 1.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.942 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2356 0.914 0.072 0.928 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.1163 0.931 0.972 0.028 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.3690 0.875 0.100 0.884 0.016
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.0747 0.992 0.000 0.016 0.984
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 3 0.0747 0.992 0.000 0.016 0.984
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.5138 0.666 0.000 0.748 0.252
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.1031 0.930 0.976 0.024 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.2448 0.920 0.924 0.076 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0747 0.992 0.000 0.016 0.984
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0747 0.938 0.016 0.984 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.1163 0.931 0.972 0.028 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.2356 0.908 0.072 0.928 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.1163 0.931 0.972 0.028 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0747 0.939 0.000 0.984 0.016
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.924 1.000 0.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.1529 0.928 0.000 0.960 0.040
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0592 0.940 0.012 0.988 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.1163 0.931 0.972 0.028 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.4731 0.845 0.840 0.128 0.032
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.1163 0.934 0.000 0.972 0.028
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.3816 0.851 0.148 0.852 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.2804 0.910 0.924 0.060 0.016
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.942 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.1163 0.931 0.972 0.028 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.2448 0.920 0.924 0.076 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.942 0.000 1.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0747 0.938 0.016 0.984 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.1163 0.931 0.972 0.028 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0747 0.929 0.984 0.016 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0747 0.939 0.000 0.984 0.016
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.0747 0.992 0.000 0.016 0.984
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.1163 0.931 0.972 0.028 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.0747 0.992 0.000 0.016 0.984
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.5363 0.631 0.724 0.276 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.1163 0.934 0.000 0.972 0.028
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0892 0.937 0.000 0.980 0.020
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0747 0.939 0.000 0.984 0.016
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.0747 0.992 0.000 0.016 0.984
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.0747 0.975 0.016 0.000 0.984
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.2356 0.921 0.928 0.072 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0747 0.939 0.000 0.984 0.016
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0000 0.942 0.000 1.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.2711 0.898 0.088 0.912 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.0000 0.986 0.000 0.000 1.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.2625 0.920 0.916 0.084 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.1163 0.934 0.000 0.972 0.028
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.0747 0.992 0.000 0.016 0.984
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2804 0.910 0.924 0.060 0.016
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.2356 0.921 0.928 0.072 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.3816 0.851 0.148 0.852 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0747 0.938 0.016 0.984 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.5363 0.673 0.276 0.724 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.2796 0.896 0.092 0.908 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.1163 0.934 0.000 0.972 0.028
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.942 0.000 1.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.1163 0.931 0.972 0.028 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 4 0.4277 0.545 0.000 0.280 0.000 0.720
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0592 0.722 0.984 0.000 0.000 0.016
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.0707 0.776 0.020 0.000 0.000 0.980
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.4679 0.553 0.648 0.000 0.000 0.352
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.1042 0.936 0.008 0.972 0.000 0.020
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 3 0.4967 0.354 0.452 0.000 0.548 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.1022 0.776 0.032 0.000 0.000 0.968
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0895 0.937 0.004 0.976 0.000 0.020
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0592 0.722 0.984 0.000 0.000 0.016
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.1022 0.776 0.032 0.000 0.000 0.968
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0672 0.716 0.984 0.008 0.000 0.008
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.1151 0.936 0.008 0.968 0.000 0.024
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0672 0.716 0.984 0.008 0.000 0.008
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.1004 0.936 0.004 0.972 0.000 0.024
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.4250 0.486 0.276 0.000 0.000 0.724
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0592 0.722 0.984 0.000 0.000 0.016
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0895 0.937 0.004 0.976 0.000 0.020
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 4 0.0592 0.771 0.000 0.016 0.000 0.984
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0376 0.936 0.004 0.992 0.000 0.004
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.1151 0.936 0.008 0.968 0.000 0.024
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.3444 0.769 0.000 0.816 0.000 0.184
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.1022 0.776 0.032 0.000 0.000 0.968
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 4 0.3052 0.691 0.004 0.136 0.000 0.860
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0524 0.924 0.008 0.004 0.988 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0336 0.935 0.000 0.992 0.000 0.008
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0336 0.720 0.992 0.000 0.000 0.008
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0336 0.935 0.000 0.992 0.000 0.008
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3710 0.745 0.192 0.804 0.000 0.004
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.3311 0.652 0.172 0.000 0.000 0.828
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.5510 0.212 0.600 0.376 0.000 0.024
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 3 0.5444 0.237 0.016 0.424 0.560 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.1297 0.925 0.016 0.964 0.020 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.4406 0.408 0.300 0.000 0.000 0.700
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4916 0.465 0.576 0.000 0.000 0.424
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0524 0.924 0.008 0.004 0.988 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0921 0.936 0.000 0.972 0.000 0.028
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.1022 0.776 0.032 0.000 0.000 0.968
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.4605 0.548 0.000 0.664 0.000 0.336
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.3311 0.652 0.172 0.000 0.000 0.828
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0524 0.935 0.004 0.988 0.000 0.008
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4522 0.580 0.680 0.000 0.000 0.320
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.1297 0.934 0.016 0.964 0.000 0.020
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.1151 0.936 0.008 0.968 0.000 0.024
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.0336 0.773 0.000 0.008 0.000 0.992
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0672 0.716 0.984 0.008 0.000 0.008
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0188 0.935 0.000 0.996 0.000 0.004
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 4 0.4193 0.576 0.000 0.268 0.000 0.732
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0592 0.722 0.984 0.000 0.000 0.016
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0336 0.935 0.000 0.992 0.000 0.008
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.3649 0.605 0.204 0.000 0.000 0.796
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.5080 0.474 0.576 0.004 0.000 0.420
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0817 0.936 0.000 0.976 0.000 0.024
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.3569 0.773 0.000 0.804 0.000 0.196
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.4454 0.386 0.308 0.000 0.000 0.692
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.4955 0.432 0.556 0.000 0.000 0.444
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0336 0.935 0.000 0.992 0.000 0.008
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.0188 0.927 0.004 0.000 0.996 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.1022 0.776 0.032 0.000 0.000 0.968
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.0524 0.924 0.008 0.004 0.988 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 4 0.0592 0.771 0.000 0.016 0.000 0.984
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0779 0.931 0.016 0.980 0.000 0.004
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0707 0.937 0.000 0.980 0.000 0.020
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0336 0.935 0.000 0.992 0.000 0.008
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.4961 0.431 0.552 0.000 0.000 0.448
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0336 0.935 0.000 0.992 0.000 0.008
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0817 0.936 0.000 0.976 0.000 0.024
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 4 0.4661 0.391 0.000 0.348 0.000 0.652
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.6148 0.318 0.484 0.048 0.000 0.468
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0524 0.933 0.008 0.988 0.000 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.0336 0.925 0.008 0.000 0.992 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0592 0.722 0.984 0.000 0.000 0.016
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.4985 0.383 0.532 0.000 0.000 0.468
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 4 0.3688 0.624 0.000 0.208 0.000 0.792
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.3172 0.814 0.000 0.840 0.000 0.160
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 4 0.2281 0.725 0.000 0.096 0.000 0.904
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.4888 0.306 0.000 0.588 0.000 0.412
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.1042 0.936 0.008 0.972 0.000 0.020
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0921 0.936 0.000 0.972 0.000 0.028
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.1022 0.776 0.032 0.000 0.000 0.968
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 5 0.0865 0.947 0.004 0.000 0.024 0.000 0.972
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.5490 0.634 0.000 0.324 0.592 0.084 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0963 0.915 0.964 0.000 0.000 0.036 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.4010 0.597 0.008 0.032 0.176 0.784 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.2690 0.651 0.156 0.000 0.000 0.844 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.2361 0.676 0.012 0.892 0.096 0.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.4141 0.582 0.728 0.000 0.024 0.000 0.248
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3452 0.604 0.000 0.000 0.244 0.756 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0404 0.664 0.000 0.988 0.012 0.000 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.1792 0.898 0.916 0.000 0.000 0.084 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.3366 0.611 0.000 0.000 0.232 0.768 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0613 0.917 0.984 0.004 0.004 0.008 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.1124 0.672 0.004 0.960 0.036 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0727 0.917 0.980 0.004 0.004 0.012 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.2074 0.626 0.004 0.920 0.060 0.016 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 5 0.0609 0.947 0.000 0.000 0.020 0.000 0.980
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.2079 0.697 0.064 0.000 0.020 0.916 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1792 0.898 0.916 0.000 0.000 0.084 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.1792 0.676 0.000 0.916 0.084 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 4 0.4829 0.096 0.000 0.020 0.484 0.496 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3949 0.608 0.000 0.668 0.332 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.1628 0.941 0.008 0.000 0.056 0.000 0.936
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.2206 0.637 0.004 0.912 0.068 0.016 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 3 0.3480 0.291 0.000 0.248 0.752 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.1697 0.689 0.008 0.000 0.060 0.932 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.6371 0.578 0.000 0.292 0.508 0.200 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.2074 0.922 0.000 0.036 0.044 0.000 0.920
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.4278 0.462 0.000 0.548 0.452 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1331 0.911 0.952 0.000 0.008 0.040 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.1697 0.940 0.008 0.000 0.060 0.000 0.932
#> 36EDD202-A845-4CE7-95D5-A515C471262E 3 0.4304 -0.415 0.000 0.484 0.516 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.6848 0.457 0.144 0.516 0.304 0.036 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.2707 0.684 0.024 0.000 0.100 0.876 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.5925 0.253 0.260 0.624 0.024 0.092 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 5 0.1628 0.941 0.008 0.000 0.056 0.000 0.936
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.6323 0.305 0.000 0.476 0.164 0.000 0.360
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.4948 0.604 0.000 0.708 0.184 0.000 0.108
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.2017 0.694 0.080 0.000 0.008 0.912 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.7447 0.299 0.272 0.232 0.048 0.448 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.1522 0.937 0.000 0.012 0.044 0.000 0.944
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.3741 0.363 0.004 0.732 0.264 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3424 0.605 0.000 0.000 0.240 0.760 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.4937 0.466 0.000 0.428 0.544 0.028 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.2669 0.683 0.020 0.000 0.104 0.876 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.3837 0.621 0.000 0.692 0.308 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.3635 0.551 0.248 0.000 0.004 0.748 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.3734 0.636 0.000 0.812 0.128 0.000 0.060
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.1862 0.641 0.004 0.932 0.048 0.016 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.4977 0.141 0.000 0.028 0.472 0.500 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2284 0.883 0.896 0.004 0.004 0.096 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.3561 0.640 0.000 0.740 0.260 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 3 0.4276 0.541 0.000 0.068 0.764 0.168 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.1331 0.911 0.952 0.000 0.008 0.040 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.4306 0.386 0.000 0.508 0.492 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.2616 0.692 0.036 0.000 0.076 0.888 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.7438 0.299 0.264 0.236 0.048 0.452 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.1851 0.637 0.000 0.912 0.088 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.4464 -0.166 0.000 0.584 0.408 0.008 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1732 0.693 0.080 0.000 0.000 0.920 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.3177 0.600 0.208 0.000 0.000 0.792 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.3661 0.636 0.000 0.724 0.276 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 5 0.0794 0.945 0.000 0.000 0.028 0.000 0.972
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.3424 0.607 0.000 0.000 0.240 0.760 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 5 0.2446 0.906 0.000 0.044 0.056 0.000 0.900
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.5862 0.264 0.000 0.112 0.544 0.344 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.4891 0.595 0.000 0.640 0.316 0.000 0.044
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.1282 0.649 0.000 0.952 0.044 0.000 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3796 0.625 0.000 0.700 0.300 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 5 0.0510 0.947 0.000 0.000 0.016 0.000 0.984
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 5 0.1764 0.939 0.008 0.000 0.064 0.000 0.928
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.6254 0.499 0.236 0.128 0.028 0.608 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.3895 0.619 0.000 0.680 0.320 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.2068 0.606 0.004 0.904 0.092 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.5420 0.629 0.000 0.332 0.592 0.076 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.1697 0.940 0.008 0.000 0.060 0.000 0.932
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.6318 0.530 0.172 0.160 0.040 0.628 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.3895 0.619 0.000 0.680 0.320 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 5 0.1626 0.934 0.000 0.016 0.044 0.000 0.940
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1043 0.913 0.960 0.000 0.000 0.040 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.6215 0.503 0.236 0.124 0.028 0.612 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.6006 0.587 0.000 0.220 0.584 0.196 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.4270 0.071 0.004 0.656 0.336 0.004 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.6076 0.532 0.000 0.196 0.572 0.232 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 3 0.3650 0.432 0.000 0.176 0.796 0.028 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0290 0.663 0.000 0.992 0.008 0.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.1965 0.605 0.000 0.904 0.096 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3424 0.605 0.000 0.000 0.240 0.760 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 6 0.1074 0.87878 0.000 0.000 0.012 0.000 0.028 0.960
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.5612 0.19512 0.000 0.008 0.564 0.272 0.156 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1908 0.84785 0.900 0.000 0.004 0.000 0.096 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.6140 -0.09221 0.012 0.000 0.188 0.408 0.392 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.5035 0.34077 0.084 0.000 0.000 0.556 0.360 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.5235 -0.02669 0.016 0.496 0.432 0.000 0.056 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.3110 0.70539 0.792 0.000 0.000 0.000 0.012 0.196
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0146 0.54469 0.000 0.000 0.000 0.996 0.004 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.5312 0.24031 0.000 0.408 0.504 0.000 0.080 0.008
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.2520 0.82089 0.844 0.000 0.004 0.000 0.152 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0937 0.54638 0.000 0.000 0.000 0.960 0.040 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0692 0.85716 0.976 0.000 0.004 0.000 0.020 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.5157 0.31606 0.000 0.360 0.544 0.000 0.096 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0777 0.85712 0.972 0.000 0.004 0.000 0.024 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.4673 0.43813 0.000 0.272 0.648 0.000 0.080 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 6 0.0717 0.87762 0.000 0.000 0.008 0.000 0.016 0.976
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.4201 0.44997 0.036 0.000 0.000 0.664 0.300 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.2631 0.81912 0.840 0.000 0.008 0.000 0.152 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.5113 0.17635 0.000 0.592 0.332 0.000 0.056 0.020
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 4 0.5012 0.30241 0.000 0.000 0.236 0.632 0.132 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.3041 0.60073 0.000 0.832 0.128 0.000 0.040 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 6 0.3176 0.85467 0.000 0.000 0.032 0.000 0.156 0.812
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.4684 0.43015 0.000 0.256 0.656 0.000 0.088 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.6752 0.29253 0.000 0.492 0.252 0.088 0.168 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.3360 0.48435 0.004 0.000 0.000 0.732 0.264 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.5661 0.07986 0.000 0.004 0.552 0.192 0.252 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 6 0.3066 0.82826 0.000 0.060 0.024 0.000 0.056 0.860
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.3914 0.58553 0.000 0.768 0.128 0.000 0.104 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2605 0.81375 0.864 0.000 0.028 0.000 0.108 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 6 0.3176 0.85375 0.000 0.000 0.032 0.000 0.156 0.812
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4425 0.54635 0.000 0.716 0.152 0.000 0.132 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.6306 0.42264 0.116 0.584 0.128 0.000 0.172 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.4166 0.50539 0.048 0.000 0.008 0.728 0.216 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.6543 0.18225 0.128 0.152 0.556 0.000 0.164 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 6 0.3176 0.85467 0.000 0.000 0.032 0.000 0.156 0.812
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.6292 0.20525 0.000 0.468 0.080 0.000 0.080 0.372
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.6314 0.34326 0.000 0.580 0.180 0.000 0.104 0.136
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.5040 0.31160 0.044 0.000 0.020 0.572 0.364 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 3 0.6524 -0.47907 0.156 0.004 0.472 0.044 0.324 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 6 0.2335 0.85607 0.000 0.028 0.024 0.000 0.044 0.904
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.4117 0.44031 0.000 0.228 0.716 0.000 0.056 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0458 0.54224 0.000 0.000 0.000 0.984 0.016 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.5857 0.23082 0.000 0.040 0.600 0.156 0.204 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.4166 0.50539 0.048 0.000 0.008 0.728 0.216 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1461 0.63974 0.000 0.940 0.044 0.000 0.016 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.5704 0.22430 0.140 0.000 0.004 0.456 0.400 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.6329 0.25647 0.000 0.540 0.268 0.000 0.084 0.108
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.4764 0.42556 0.000 0.292 0.628 0.000 0.080 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.5288 0.25525 0.000 0.000 0.252 0.592 0.156 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.3014 0.79798 0.804 0.000 0.012 0.000 0.184 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.2752 0.57930 0.000 0.856 0.108 0.000 0.036 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 4 0.7554 0.04208 0.000 0.224 0.216 0.364 0.196 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.2383 0.81952 0.880 0.000 0.024 0.000 0.096 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.4074 0.57577 0.000 0.752 0.140 0.000 0.108 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.4337 0.49611 0.056 0.000 0.008 0.712 0.224 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.6629 -0.56775 0.140 0.004 0.440 0.056 0.360 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.4864 0.20857 0.000 0.384 0.552 0.000 0.064 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.4128 0.34741 0.000 0.072 0.788 0.044 0.096 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.4459 0.40520 0.032 0.000 0.008 0.640 0.320 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.5479 0.27710 0.132 0.000 0.000 0.500 0.368 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1649 0.63055 0.000 0.932 0.032 0.000 0.036 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 6 0.0935 0.87959 0.000 0.004 0.000 0.000 0.032 0.964
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0146 0.54647 0.000 0.000 0.000 0.996 0.004 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 6 0.3756 0.78727 0.000 0.096 0.032 0.000 0.060 0.812
#> 1CC36859-357A-49E0-A367-4F57D47288BA 4 0.5609 0.13636 0.000 0.000 0.336 0.504 0.160 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.1757 0.62464 0.000 0.928 0.008 0.000 0.052 0.012
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.4880 0.32198 0.000 0.344 0.596 0.000 0.048 0.012
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1204 0.63763 0.000 0.944 0.056 0.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 6 0.0260 0.87921 0.000 0.000 0.008 0.000 0.000 0.992
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 6 0.3700 0.84149 0.020 0.000 0.032 0.000 0.156 0.792
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 5 0.7156 0.98069 0.100 0.000 0.348 0.192 0.360 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.1930 0.63993 0.000 0.916 0.036 0.000 0.048 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.3863 0.42622 0.000 0.260 0.712 0.000 0.028 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.5811 0.19060 0.000 0.012 0.544 0.272 0.172 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.3213 0.85325 0.000 0.000 0.032 0.000 0.160 0.808
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.7139 -0.95368 0.080 0.004 0.364 0.196 0.356 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0547 0.63783 0.000 0.980 0.000 0.000 0.020 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 6 0.2407 0.84860 0.000 0.056 0.004 0.000 0.048 0.892
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1524 0.84536 0.932 0.000 0.008 0.000 0.060 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 5 0.7194 0.98070 0.100 0.000 0.340 0.204 0.356 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.5925 0.00553 0.000 0.004 0.424 0.392 0.180 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.3018 0.38680 0.000 0.112 0.848 0.016 0.024 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.5631 -0.00553 0.000 0.000 0.444 0.408 0.148 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.7201 0.22381 0.000 0.444 0.228 0.156 0.172 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.4855 0.14693 0.000 0.460 0.484 0.000 0.056 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.3859 0.40598 0.000 0.288 0.692 0.000 0.020 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0458 0.54224 0.000 0.000 0.000 0.984 0.016 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 17471 rows and 87 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 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 0.907 0.966 0.984 0.5047 0.495 0.495
#> 3 3 0.925 0.894 0.954 0.3295 0.728 0.503
#> 4 4 0.719 0.653 0.859 0.1177 0.804 0.496
#> 5 5 0.724 0.725 0.849 0.0642 0.871 0.558
#> 6 6 0.741 0.584 0.769 0.0370 0.916 0.627
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.0000 0.975 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 1 0.0000 0.990 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.990 1.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0000 0.990 1.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.990 1.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0000 0.975 0.000 1.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 2 0.0000 0.975 0.000 1.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.0000 0.990 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.975 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.990 1.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.990 1.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 2 0.4939 0.880 0.108 0.892
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.975 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 2 0.4939 0.880 0.108 0.892
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.975 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 2 0.0000 0.975 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.990 1.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.990 1.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.975 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.0000 0.990 1.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 1 0.4815 0.886 0.896 0.104
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.0000 0.975 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 1 0.5737 0.845 0.864 0.136
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 1 0.4690 0.890 0.900 0.100
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.990 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.0000 0.990 1.000 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.0000 0.975 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.7602 0.733 0.220 0.780
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2043 0.962 0.968 0.032
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.0000 0.975 0.000 1.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.7602 0.733 0.220 0.780
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.0000 0.990 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.990 1.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.0000 0.975 0.000 1.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.0000 0.975 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.975 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.975 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.990 1.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.990 1.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.0000 0.975 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 1 0.2043 0.962 0.968 0.032
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.0000 0.990 1.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 1 0.0000 0.990 1.000 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.990 1.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.975 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.990 1.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0000 0.975 0.000 1.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.975 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.0000 0.990 1.000 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 2 0.5946 0.840 0.144 0.856
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.975 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 1 0.0000 0.990 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.990 1.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.7219 0.762 0.200 0.800
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.990 1.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.990 1.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0376 0.972 0.004 0.996
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 1 0.0000 0.990 1.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.990 1.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.990 1.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.975 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.0000 0.975 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.0000 0.990 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.0000 0.975 0.000 1.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.0000 0.990 1.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.975 0.000 1.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0000 0.975 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.975 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 2 0.0000 0.975 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 2 0.0000 0.975 0.000 1.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.990 1.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.975 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0376 0.972 0.004 0.996
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 1 0.0000 0.990 1.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 2 0.0000 0.975 0.000 1.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0000 0.990 1.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.975 0.000 1.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.975 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.990 1.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.990 1.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 1 0.0000 0.990 1.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 1 0.0000 0.990 1.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 1 0.0000 0.990 1.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 1 0.0000 0.990 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.975 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.975 0.000 1.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.9415 0.000 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.9305 0.000 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.9711 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0424 0.9680 0.992 0.008 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.9711 1.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.5968 0.4196 0.000 0.364 0.636
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 3 0.0237 0.9393 0.004 0.000 0.996
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.2066 0.9443 0.940 0.060 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.3192 0.8429 0.000 0.112 0.888
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.9711 1.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.1964 0.9464 0.944 0.056 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 3 0.2066 0.8988 0.060 0.000 0.940
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.5810 0.4869 0.000 0.336 0.664
#> 694B0504-095E-431A-84F2-B4387072138E 3 0.2066 0.8988 0.060 0.000 0.940
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.5706 0.5234 0.000 0.680 0.320
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.0000 0.9415 0.000 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.9711 1.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.9711 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.5988 0.4104 0.000 0.368 0.632
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.2261 0.9386 0.932 0.068 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.9305 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.0000 0.9415 0.000 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.4413 0.8260 0.104 0.860 0.036
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.9305 0.000 1.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.9711 1.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.2711 0.9216 0.912 0.088 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0000 0.9415 0.000 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.9305 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.9711 1.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.0000 0.9415 0.000 0.000 1.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.9305 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.6140 0.2992 0.404 0.596 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.9711 1.000 0.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.0592 0.9344 0.012 0.000 0.988
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.0000 0.9415 0.000 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 3 0.0000 0.9415 0.000 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 3 0.0000 0.9415 0.000 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.9711 1.000 0.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.9711 1.000 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0000 0.9415 0.000 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.9305 0.000 1.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.2066 0.9443 0.940 0.060 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.9305 0.000 1.000 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.9711 1.000 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0424 0.9269 0.000 0.992 0.008
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.9711 1.000 0.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.9415 0.000 0.000 1.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.0000 0.9415 0.000 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 1 0.2066 0.9443 0.940 0.060 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 3 0.2066 0.8988 0.060 0.000 0.940
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.2066 0.8864 0.000 0.940 0.060
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9305 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.9711 1.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.9305 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.9711 1.000 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.9711 1.000 0.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.9305 0.000 1.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0000 0.9305 0.000 1.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.9711 1.000 0.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.9711 1.000 0.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0747 0.9220 0.000 0.984 0.016
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.0000 0.9415 0.000 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.2066 0.9443 0.940 0.060 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.0000 0.9415 0.000 0.000 1.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 1 0.2261 0.9386 0.932 0.068 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.6309 -0.0138 0.000 0.504 0.496
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.4555 0.7306 0.000 0.800 0.200
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0592 0.9246 0.000 0.988 0.012
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.0000 0.9415 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.0000 0.9415 0.000 0.000 1.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.9711 1.000 0.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0424 0.9269 0.000 0.992 0.008
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0000 0.9305 0.000 1.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.9305 0.000 1.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.0000 0.9415 0.000 0.000 1.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0000 0.9711 1.000 0.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.2356 0.8769 0.000 0.928 0.072
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.0000 0.9415 0.000 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.9711 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.9711 1.000 0.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.9305 0.000 1.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0000 0.9305 0.000 1.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 1 0.5529 0.6034 0.704 0.296 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.9305 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0592 0.9339 0.000 0.012 0.988
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.9305 0.000 1.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.2066 0.9443 0.940 0.060 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 4 0.4977 -0.1375 0.000 0.460 0.000 0.540
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.8078 1.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.0336 0.7407 0.008 0.000 0.000 0.992
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.4933 0.3635 0.432 0.000 0.000 0.568
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.7341 0.0596 0.164 0.476 0.360 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.4877 0.2471 0.592 0.000 0.408 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.0188 0.7409 0.004 0.000 0.000 0.996
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.0336 0.8916 0.000 0.008 0.992 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.8078 1.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0188 0.7409 0.004 0.000 0.000 0.996
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0707 0.8047 0.980 0.000 0.020 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.5155 0.2224 0.004 0.468 0.528 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0707 0.8047 0.980 0.000 0.020 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.6637 0.4224 0.000 0.260 0.608 0.132
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.3942 0.6482 0.236 0.000 0.000 0.764
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.8078 1.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.3801 0.7040 0.000 0.220 0.780 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 4 0.0000 0.7397 0.000 0.000 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.8465 0.000 1.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 1 0.5285 0.1077 0.524 0.468 0.000 0.008
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0592 0.8443 0.000 0.984 0.000 0.016
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.1716 0.7303 0.064 0.000 0.000 0.936
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 4 0.0000 0.7397 0.000 0.000 0.000 1.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8465 0.000 1.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.8078 1.000 0.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.8465 0.000 1.000 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.4164 0.5824 0.264 0.736 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.3356 0.6927 0.176 0.000 0.000 0.824
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.4888 0.2433 0.588 0.000 0.412 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.3610 0.6786 0.200 0.000 0.000 0.800
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3123 0.6272 0.844 0.000 0.000 0.156
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2973 0.7793 0.000 0.856 0.000 0.144
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0188 0.7409 0.004 0.000 0.000 0.996
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.4830 0.4540 0.000 0.608 0.000 0.392
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.3219 0.6982 0.164 0.000 0.000 0.836
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.8465 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.4992 0.2592 0.476 0.000 0.000 0.524
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.0592 0.8865 0.000 0.016 0.984 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.0000 0.7397 0.000 0.000 0.000 1.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0707 0.8047 0.980 0.000 0.020 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0188 0.8448 0.000 0.996 0.004 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4877 0.4133 0.000 0.592 0.000 0.408
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.8078 1.000 0.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.8465 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.3610 0.6788 0.200 0.000 0.000 0.800
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.4925 -0.0698 0.572 0.000 0.000 0.428
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.1211 0.8376 0.000 0.960 0.000 0.040
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.5000 0.1956 0.000 0.504 0.000 0.496
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.3569 0.6815 0.196 0.000 0.000 0.804
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.4907 0.3864 0.420 0.000 0.000 0.580
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.8465 0.000 1.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0188 0.7409 0.004 0.000 0.000 0.996
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 4 0.0000 0.7397 0.000 0.000 0.000 1.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 3 0.4843 0.4175 0.000 0.396 0.604 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.2888 0.7995 0.000 0.124 0.872 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.8465 0.000 1.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.4972 0.0910 0.456 0.000 0.544 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.4925 0.3710 0.428 0.000 0.000 0.572
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8465 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.4194 0.7424 0.000 0.800 0.028 0.172
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 4 0.4998 -0.2149 0.000 0.488 0.000 0.512
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.3486 0.6870 0.188 0.000 0.000 0.812
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.1474 0.8097 0.000 0.948 0.052 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.0000 0.8955 0.000 0.000 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.8078 1.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.4730 0.4811 0.364 0.000 0.000 0.636
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 4 0.4996 -0.2043 0.000 0.484 0.000 0.516
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 4 0.4999 -0.2265 0.000 0.492 0.000 0.508
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 4 0.1302 0.7140 0.000 0.044 0.000 0.956
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.1022 0.8395 0.000 0.968 0.000 0.032
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0336 0.8913 0.000 0.008 0.992 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.3074 0.7726 0.000 0.848 0.000 0.152
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0188 0.7409 0.004 0.000 0.000 0.996
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.3146 0.7174 0.000 0.028 0.844 0.128 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.2020 0.8231 0.900 0.000 0.000 0.100 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.3675 0.7304 0.024 0.000 0.188 0.788 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.2179 0.7723 0.112 0.000 0.000 0.888 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.7149 0.5747 0.156 0.592 0.136 0.008 0.108
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.3796 0.5939 0.700 0.000 0.000 0.000 0.300
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3177 0.7308 0.000 0.000 0.208 0.792 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 5 0.0833 0.9155 0.004 0.004 0.016 0.000 0.976
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.2179 0.8195 0.888 0.000 0.000 0.112 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2929 0.7515 0.000 0.000 0.180 0.820 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1469 0.8274 0.948 0.000 0.000 0.036 0.016
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.3631 0.7398 0.012 0.820 0.024 0.000 0.144
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.1300 0.8251 0.956 0.000 0.000 0.028 0.016
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.8116 0.1734 0.052 0.172 0.416 0.036 0.324
#> 0EA8288E-824D-4304-A053-5A833361F5C5 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.1568 0.8176 0.036 0.000 0.020 0.944 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1851 0.8228 0.912 0.000 0.000 0.088 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 5 0.5981 0.5412 0.012 0.152 0.212 0.000 0.624
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3932 0.4566 0.000 0.000 0.672 0.328 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0880 0.8262 0.000 0.968 0.032 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5415 0.5998 0.260 0.660 0.060 0.020 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2536 0.7621 0.000 0.868 0.128 0.004 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.1732 0.8004 0.000 0.000 0.080 0.920 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 4 0.4974 0.4184 0.024 0.008 0.364 0.604 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0404 0.8312 0.000 0.988 0.012 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.1831 0.8201 0.920 0.000 0.004 0.076 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0963 0.8255 0.000 0.964 0.036 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.4488 0.6768 0.212 0.736 0.004 0.048 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.1915 0.8148 0.040 0.000 0.032 0.928 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.4893 0.6381 0.704 0.000 0.020 0.036 0.240
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1012 0.8172 0.020 0.000 0.012 0.968 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4321 0.3192 0.600 0.000 0.004 0.396 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.5225 0.0858 0.020 0.540 0.424 0.016 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.3586 0.6642 0.000 0.000 0.264 0.736 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.4793 0.6248 0.000 0.216 0.708 0.076 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1836 0.8145 0.036 0.000 0.032 0.932 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0162 0.8316 0.000 0.996 0.004 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.2806 0.7379 0.152 0.000 0.004 0.844 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 5 0.0771 0.9155 0.004 0.000 0.020 0.000 0.976
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.4001 0.8014 0.052 0.040 0.040 0.024 0.844
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.4074 0.4790 0.000 0.000 0.364 0.636 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2452 0.8072 0.908 0.000 0.012 0.052 0.028
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.3081 0.7390 0.012 0.832 0.156 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 3 0.5649 0.1294 0.000 0.452 0.472 0.076 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.1892 0.8187 0.916 0.000 0.004 0.080 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0404 0.8312 0.000 0.988 0.012 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.1981 0.8145 0.048 0.000 0.028 0.924 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.4101 0.4979 0.332 0.000 0.004 0.664 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.4193 0.4071 0.012 0.304 0.684 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.1579 0.7250 0.000 0.032 0.944 0.024 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1364 0.8156 0.036 0.000 0.012 0.952 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.2439 0.7696 0.120 0.000 0.004 0.876 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.2416 0.7862 0.012 0.888 0.100 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.3109 0.7375 0.000 0.000 0.200 0.800 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.4045 0.3907 0.000 0.000 0.644 0.356 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.2969 0.7560 0.000 0.852 0.020 0.000 0.128
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 5 0.6285 0.1584 0.016 0.096 0.428 0.000 0.460
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0162 0.8316 0.000 0.996 0.004 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.4283 0.2611 0.544 0.000 0.000 0.000 0.456
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.2377 0.7696 0.128 0.000 0.000 0.872 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0162 0.8304 0.000 0.996 0.004 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.2573 0.6503 0.016 0.104 0.880 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.2782 0.7342 0.000 0.048 0.880 0.072 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.1740 0.8084 0.056 0.000 0.012 0.932 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0671 0.8273 0.000 0.980 0.016 0.000 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 5 0.0000 0.9294 0.000 0.000 0.000 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.1831 0.8201 0.920 0.000 0.004 0.076 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.2286 0.7881 0.108 0.000 0.004 0.888 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.3460 0.7179 0.000 0.044 0.828 0.128 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.1493 0.7248 0.000 0.028 0.948 0.024 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.3109 0.6523 0.000 0.000 0.800 0.200 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.3519 0.6558 0.000 0.776 0.216 0.008 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 5 0.3716 0.7788 0.012 0.024 0.152 0.000 0.812
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.3527 0.5697 0.016 0.192 0.792 0.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3143 0.7342 0.000 0.000 0.204 0.796 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 5 0.5165 0.0203 0.000 0.012 0.448 0.056 0.484 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1588 0.8288 0.924 0.000 0.000 0.072 0.004 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.4074 0.1250 0.016 0.000 0.004 0.324 0.656 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.1387 0.7572 0.068 0.000 0.000 0.932 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.8036 0.2252 0.128 0.392 0.320 0.016 0.072 0.072
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.3126 0.6017 0.752 0.000 0.000 0.000 0.000 0.248
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.3979 0.4611 0.000 0.000 0.012 0.628 0.360 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 6 0.2110 0.8499 0.000 0.004 0.084 0.000 0.012 0.900
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.2006 0.8272 0.892 0.000 0.000 0.104 0.004 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.3426 0.5904 0.000 0.000 0.004 0.720 0.276 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0547 0.8328 0.980 0.000 0.000 0.020 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.6057 0.5833 0.008 0.628 0.116 0.000 0.164 0.084
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0363 0.8305 0.988 0.000 0.000 0.012 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.7490 0.0862 0.036 0.076 0.212 0.028 0.524 0.124
#> 0EA8288E-824D-4304-A053-5A833361F5C5 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.1995 0.7744 0.036 0.000 0.000 0.912 0.052 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.1867 0.8208 0.916 0.000 0.000 0.064 0.020 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.5698 0.0868 0.000 0.096 0.484 0.000 0.020 0.400
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 5 0.5757 0.2202 0.000 0.000 0.320 0.192 0.488 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0260 0.8205 0.000 0.992 0.008 0.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 5 0.6644 -0.0151 0.148 0.312 0.060 0.004 0.476 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2277 0.7597 0.000 0.892 0.032 0.000 0.076 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.2454 0.7028 0.000 0.000 0.000 0.840 0.160 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.3752 0.3177 0.016 0.004 0.036 0.148 0.796 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0146 0.8209 0.000 0.996 0.004 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2948 0.8099 0.860 0.000 0.012 0.084 0.044 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0993 0.8126 0.000 0.964 0.012 0.000 0.024 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.5247 0.5733 0.232 0.668 0.016 0.036 0.048 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.2294 0.7584 0.020 0.000 0.008 0.896 0.076 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.6783 -0.1894 0.368 0.000 0.040 0.024 0.436 0.132
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1700 0.7730 0.024 0.000 0.000 0.928 0.048 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.5102 0.2202 0.508 0.000 0.012 0.428 0.052 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 5 0.6042 -0.0653 0.012 0.408 0.144 0.004 0.432 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.4463 0.3792 0.000 0.000 0.036 0.588 0.376 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.6108 0.1650 0.000 0.160 0.276 0.032 0.532 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.2114 0.7586 0.012 0.000 0.008 0.904 0.076 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0260 0.8222 0.000 0.992 0.008 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.3024 0.6854 0.116 0.000 0.008 0.844 0.032 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 6 0.1757 0.8649 0.000 0.000 0.076 0.000 0.008 0.916
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.7048 0.0610 0.096 0.016 0.100 0.004 0.468 0.316
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.5187 -0.0606 0.000 0.000 0.088 0.440 0.472 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.3737 0.7267 0.812 0.000 0.016 0.048 0.116 0.008
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.4118 0.5050 0.000 0.628 0.352 0.000 0.020 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 5 0.6328 0.1403 0.000 0.400 0.148 0.036 0.416 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3048 0.8025 0.848 0.000 0.008 0.100 0.044 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0260 0.8206 0.000 0.992 0.008 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.2675 0.7576 0.040 0.000 0.008 0.876 0.076 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.4978 -0.1061 0.448 0.000 0.008 0.496 0.048 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.2572 0.5131 0.000 0.136 0.852 0.000 0.012 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.4321 0.2955 0.000 0.016 0.668 0.020 0.296 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1572 0.7716 0.036 0.000 0.000 0.936 0.028 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.2249 0.7398 0.064 0.000 0.004 0.900 0.032 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.3711 0.6363 0.000 0.720 0.260 0.000 0.020 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.3758 0.5252 0.000 0.000 0.008 0.668 0.324 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 5 0.5825 0.2397 0.000 0.000 0.288 0.224 0.488 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.2833 0.7549 0.000 0.860 0.024 0.000 0.012 0.104
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.3668 0.4383 0.000 0.028 0.744 0.000 0.000 0.228
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0260 0.8222 0.000 0.992 0.008 0.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 6 0.3823 0.1937 0.436 0.000 0.000 0.000 0.000 0.564
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1845 0.7537 0.072 0.000 0.004 0.916 0.008 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0260 0.8222 0.000 0.992 0.008 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.1245 0.5479 0.000 0.032 0.952 0.000 0.016 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.5147 -0.1271 0.000 0.016 0.480 0.048 0.456 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.2437 0.7507 0.036 0.000 0.004 0.888 0.072 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0964 0.8176 0.000 0.968 0.016 0.000 0.012 0.004
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 6 0.0000 0.9347 0.000 0.000 0.000 0.000 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2658 0.8165 0.876 0.000 0.008 0.080 0.036 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1605 0.7671 0.044 0.000 0.004 0.936 0.016 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 5 0.5446 0.0816 0.000 0.016 0.416 0.076 0.492 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.3900 0.3646 0.000 0.012 0.724 0.016 0.248 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 5 0.5423 0.1392 0.000 0.000 0.392 0.120 0.488 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.3422 0.6456 0.000 0.792 0.040 0.000 0.168 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 6 0.4428 0.4439 0.000 0.012 0.324 0.000 0.024 0.640
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.1265 0.5485 0.000 0.044 0.948 0.000 0.008 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3774 0.5190 0.000 0.000 0.008 0.664 0.328 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 17471 rows and 87 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 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 0.659 0.892 0.930 0.4244 0.543 0.543
#> 3 3 0.679 0.899 0.945 0.4239 0.655 0.463
#> 4 4 0.686 0.726 0.871 0.2169 0.676 0.338
#> 5 5 0.762 0.787 0.866 0.0798 0.899 0.640
#> 6 6 0.790 0.713 0.868 0.0341 0.856 0.455
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 1 0.1633 0.840 0.976 0.024
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.963 0.000 1.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.7219 0.878 0.800 0.200
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.1633 0.953 0.024 0.976
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.7219 0.878 0.800 0.200
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0000 0.963 0.000 1.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.1633 0.840 0.976 0.024
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 2 0.1633 0.953 0.024 0.976
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.963 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.7219 0.878 0.800 0.200
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 2 0.1633 0.953 0.024 0.976
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.7219 0.878 0.800 0.200
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.963 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.7219 0.878 0.800 0.200
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.963 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.1633 0.840 0.976 0.024
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 2 0.1633 0.953 0.024 0.976
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.7219 0.878 0.800 0.200
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.963 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.1633 0.953 0.024 0.976
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.963 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 1 0.1633 0.840 0.976 0.024
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0000 0.963 0.000 1.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.963 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 2 0.1633 0.953 0.024 0.976
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0000 0.963 0.000 1.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 1 0.1633 0.840 0.976 0.024
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.963 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.7219 0.878 0.800 0.200
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.1633 0.840 0.976 0.024
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.963 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.0672 0.960 0.008 0.992
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 2 0.1633 0.953 0.024 0.976
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.7674 0.864 0.776 0.224
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 1 0.1633 0.840 0.976 0.024
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.2236 0.934 0.036 0.964
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2236 0.934 0.036 0.964
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.1843 0.950 0.028 0.972
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.8813 0.743 0.700 0.300
#> FF7021BB-414B-4466-BC43-372D856E6A1D 1 0.1633 0.840 0.976 0.024
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.963 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 2 0.1633 0.953 0.024 0.976
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.963 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 2 0.1633 0.953 0.024 0.976
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.963 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.7219 0.878 0.800 0.200
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.2236 0.934 0.036 0.964
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.9170 0.331 0.332 0.668
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.1633 0.953 0.024 0.976
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.7674 0.864 0.776 0.224
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.963 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0376 0.962 0.004 0.996
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.7219 0.878 0.800 0.200
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.963 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 2 0.9998 -0.242 0.492 0.508
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.7219 0.878 0.800 0.200
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.963 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0000 0.963 0.000 1.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.7219 0.878 0.800 0.200
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.7219 0.878 0.800 0.200
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.963 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 1 0.9635 0.353 0.612 0.388
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 2 0.1633 0.953 0.024 0.976
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.4298 0.874 0.088 0.912
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.1633 0.953 0.024 0.976
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.2236 0.934 0.036 0.964
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0000 0.963 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.963 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.1633 0.840 0.976 0.024
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1633 0.840 0.976 0.024
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.7219 0.878 0.800 0.200
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.963 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0000 0.963 0.000 1.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.963 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.1633 0.840 0.976 0.024
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 2 0.0000 0.963 0.000 1.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.2236 0.934 0.036 0.964
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.7299 0.714 0.204 0.796
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.7219 0.878 0.800 0.200
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.7219 0.878 0.800 0.200
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.963 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0000 0.963 0.000 1.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.1633 0.953 0.024 0.976
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.963 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.963 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.963 0.000 1.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 2 0.1633 0.953 0.024 0.976
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.000 0.951 0.000 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.000 0.946 0.000 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.000 0.896 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.375 0.884 0.856 0.144 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.000 0.896 1.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.000 0.946 0.000 1.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 3 0.375 0.880 0.144 0.000 0.856
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.388 0.876 0.848 0.152 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.000 0.946 0.000 1.000 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.000 0.896 1.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.375 0.884 0.856 0.144 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.000 0.896 1.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.000 0.946 0.000 1.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 2 0.375 0.824 0.144 0.856 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.000 0.946 0.000 1.000 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.375 0.880 0.144 0.000 0.856
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.375 0.884 0.856 0.144 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.000 0.896 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.000 0.946 0.000 1.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 1 0.375 0.884 0.856 0.144 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.000 0.946 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.000 0.951 0.000 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.000 0.946 0.000 1.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.000 0.946 0.000 1.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.375 0.884 0.856 0.144 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.000 0.946 0.000 1.000 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.000 0.951 0.000 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.000 0.946 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 2 0.630 0.199 0.480 0.520 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.375 0.880 0.144 0.000 0.856
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.000 0.946 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.000 0.946 0.000 1.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.375 0.884 0.856 0.144 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.375 0.824 0.144 0.856 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.000 0.951 0.000 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.435 0.797 0.000 0.816 0.184
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.429 0.802 0.000 0.820 0.180
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.369 0.885 0.860 0.140 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.406 0.810 0.164 0.836 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.000 0.951 0.000 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.000 0.946 0.000 1.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.375 0.884 0.856 0.144 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.000 0.946 0.000 1.000 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.375 0.884 0.856 0.144 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.000 0.946 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.000 0.896 1.000 0.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.153 0.920 0.000 0.960 0.040
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.355 0.837 0.132 0.868 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.000 0.946 0.000 1.000 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.141 0.878 0.964 0.036 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.000 0.946 0.000 1.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.000 0.946 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.000 0.896 1.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.000 0.946 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.000 0.896 1.000 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.000 0.896 1.000 0.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.000 0.946 0.000 1.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.000 0.946 0.000 1.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.000 0.896 1.000 0.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.000 0.896 1.000 0.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.000 0.946 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.000 0.951 0.000 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.375 0.884 0.856 0.144 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.497 0.735 0.000 0.764 0.236
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.000 0.946 0.000 1.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.435 0.797 0.000 0.816 0.184
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.254 0.890 0.000 0.920 0.080
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.000 0.946 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.000 0.951 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.375 0.880 0.144 0.000 0.856
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.000 0.896 1.000 0.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.000 0.946 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.000 0.946 0.000 1.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.000 0.946 0.000 1.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.000 0.951 0.000 0.000 1.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.435 0.841 0.816 0.184 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.435 0.797 0.000 0.816 0.184
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.000 0.951 0.000 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.000 0.896 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.000 0.896 1.000 0.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.000 0.946 0.000 1.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.000 0.946 0.000 1.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.355 0.818 0.132 0.868 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.000 0.946 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.000 0.946 0.000 1.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.000 0.946 0.000 1.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 1 0.375 0.884 0.856 0.144 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.8845 0.000 0.000 1.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 4 0.4454 0.6241 0.000 0.308 0.000 0.692
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.8502 1.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.0000 0.7350 0.000 0.000 0.000 1.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.4222 0.4044 0.272 0.000 0.000 0.728
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.1557 0.8334 0.056 0.944 0.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.2345 0.8051 0.900 0.000 0.100 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.1118 0.7377 0.000 0.036 0.000 0.964
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.8502 1.000 0.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0000 0.7350 0.000 0.000 0.000 1.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.1557 0.8330 0.944 0.056 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0592 0.8576 0.016 0.984 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.2011 0.8174 0.920 0.080 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.3610 0.7212 0.800 0.000 0.200 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.0707 0.7249 0.020 0.000 0.000 0.980
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.8502 1.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 4 0.0000 0.7350 0.000 0.000 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0469 0.8583 0.000 0.988 0.000 0.012
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.0000 0.8845 0.000 0.000 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0592 0.8576 0.016 0.984 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1792 0.8223 0.000 0.932 0.000 0.068
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.0000 0.7350 0.000 0.000 0.000 1.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 4 0.4961 0.3816 0.000 0.448 0.000 0.552
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.1557 0.8850 0.000 0.056 0.944 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.1302 0.8398 0.000 0.956 0.000 0.044
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.8502 1.000 0.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.4898 0.2111 0.416 0.000 0.584 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.1792 0.8248 0.068 0.932 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.6817 0.0839 0.100 0.408 0.000 0.492
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.2345 0.7988 0.900 0.100 0.000 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.0000 0.8845 0.000 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 3 0.3172 0.8495 0.000 0.160 0.840 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 3 0.3219 0.8459 0.000 0.164 0.836 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.2345 0.6672 0.100 0.000 0.000 0.900
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3311 0.7270 0.828 0.172 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.1902 0.8824 0.004 0.064 0.932 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2345 0.7904 0.000 0.900 0.000 0.100
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0000 0.7350 0.000 0.000 0.000 1.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 4 0.4564 0.6006 0.000 0.328 0.000 0.672
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 2 0.6726 0.2192 0.100 0.536 0.000 0.364
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4891 0.6319 0.680 0.012 0.000 0.308
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.5143 0.0133 0.004 0.540 0.456 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.5599 0.4878 0.288 0.664 0.000 0.048
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.4454 0.6241 0.000 0.308 0.000 0.692
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.1637 0.8311 0.940 0.060 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4250 0.4949 0.000 0.724 0.000 0.276
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.8502 1.000 0.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.2281 0.6708 0.096 0.000 0.000 0.904
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.2760 0.7992 0.872 0.000 0.000 0.128
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.1118 0.8471 0.000 0.964 0.000 0.036
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.1940 0.8155 0.000 0.924 0.000 0.076
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.2345 0.6672 0.100 0.000 0.000 0.900
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.4454 0.6400 0.692 0.000 0.000 0.308
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.0000 0.8845 0.000 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0000 0.7350 0.000 0.000 0.000 1.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.3123 0.8518 0.000 0.156 0.844 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 4 0.4454 0.6241 0.000 0.308 0.000 0.692
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 3 0.3172 0.8495 0.000 0.160 0.840 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.3311 0.7025 0.000 0.828 0.172 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.0376 0.8849 0.004 0.004 0.992 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.3074 0.7688 0.848 0.000 0.152 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.4454 0.6400 0.692 0.000 0.000 0.308
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.1792 0.8247 0.000 0.932 0.000 0.068
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 4 0.4454 0.6241 0.000 0.308 0.000 0.692
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.0000 0.8845 0.000 0.000 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 2 0.5866 0.3731 0.052 0.624 0.000 0.324
#> 3C582264-85BC-4569-A888-8EB3008E5198 3 0.3172 0.8495 0.000 0.160 0.840 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.0707 0.8866 0.000 0.020 0.980 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.8502 1.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.4454 0.6400 0.692 0.000 0.000 0.308
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 4 0.4454 0.6241 0.000 0.308 0.000 0.692
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 4 0.4624 0.5860 0.000 0.340 0.000 0.660
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 4 0.4454 0.6241 0.000 0.308 0.000 0.692
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 4 0.4972 0.3564 0.000 0.456 0.000 0.544
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.8619 0.000 1.000 0.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.4661 0.2560 0.000 0.652 0.000 0.348
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0000 0.7350 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 5 0.3432 0.8472 0.132 0.000 0.000 0.040 0.828
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.2424 0.8338 0.868 0.000 0.000 0.132 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.1282 0.9030 0.004 0.000 0.044 0.952 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0794 0.8534 0.028 0.972 0.000 0.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.8109 1.000 0.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.8588 0.000 1.000 0.000 0.000 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.2852 0.8094 0.828 0.000 0.000 0.172 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.4302 0.1199 0.000 0.000 0.480 0.520 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2905 0.8269 0.868 0.096 0.000 0.036 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0703 0.8547 0.024 0.976 0.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.2424 0.8129 0.868 0.132 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.8588 0.000 1.000 0.000 0.000 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.1670 0.7776 0.936 0.000 0.000 0.012 0.052
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 3 0.1608 0.8421 0.000 0.000 0.928 0.072 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.2424 0.8338 0.868 0.000 0.000 0.132 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.1792 0.8493 0.000 0.916 0.000 0.000 0.084
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0609 0.8574 0.000 0.980 0.020 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.3578 0.8457 0.132 0.000 0.000 0.048 0.820
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0703 0.8547 0.024 0.976 0.000 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2825 0.8220 0.000 0.860 0.124 0.000 0.016
#> 932C8E98-A352-409C-8744-3D49FABCE425 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.4291 0.1022 0.000 0.464 0.536 0.000 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.3750 0.8416 0.084 0.072 0.000 0.012 0.832
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.2516 0.8454 0.000 0.860 0.000 0.000 0.140
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.2424 0.8338 0.868 0.000 0.000 0.132 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.5236 -0.0701 0.544 0.000 0.000 0.048 0.408
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.2280 0.8491 0.000 0.880 0.000 0.000 0.120
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2351 0.8358 0.000 0.896 0.000 0.088 0.016
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.1197 0.9028 0.000 0.000 0.048 0.952 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.2424 0.8129 0.868 0.132 0.000 0.000 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 5 0.3578 0.8457 0.132 0.000 0.000 0.048 0.820
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 5 0.0794 0.8417 0.000 0.028 0.000 0.000 0.972
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 5 0.0880 0.8395 0.000 0.032 0.000 0.000 0.968
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.1197 0.9028 0.000 0.000 0.048 0.952 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4676 0.7075 0.720 0.208 0.000 0.072 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.3482 0.8378 0.052 0.096 0.000 0.008 0.844
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.2020 0.8353 0.000 0.900 0.100 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.0794 0.8832 0.000 0.028 0.972 0.000 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.1331 0.8890 0.000 0.040 0.008 0.952 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.2813 0.8361 0.000 0.832 0.000 0.000 0.168
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.1364 0.8891 0.036 0.012 0.000 0.952 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 5 0.4307 0.0225 0.000 0.496 0.000 0.000 0.504
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.6643 0.4589 0.124 0.584 0.240 0.000 0.052
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2848 0.8248 0.868 0.104 0.000 0.028 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.2852 0.8341 0.000 0.828 0.000 0.000 0.172
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.3752 0.6177 0.000 0.708 0.292 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.2424 0.8338 0.868 0.000 0.000 0.132 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.2773 0.8380 0.000 0.836 0.000 0.000 0.164
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.1270 0.9000 0.000 0.000 0.052 0.948 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.4114 0.5007 0.624 0.000 0.000 0.376 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.2852 0.7420 0.000 0.828 0.172 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.2605 0.8053 0.000 0.852 0.148 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.1197 0.9028 0.000 0.000 0.048 0.952 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.1197 0.8845 0.048 0.000 0.000 0.952 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.2852 0.8341 0.000 0.828 0.000 0.000 0.172
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 5 0.3432 0.8472 0.132 0.000 0.000 0.040 0.828
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 5 0.2230 0.8209 0.000 0.116 0.000 0.000 0.884
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 5 0.0794 0.8417 0.000 0.028 0.000 0.000 0.972
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.3452 0.6785 0.000 0.756 0.000 0.000 0.244
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1121 0.8621 0.000 0.956 0.000 0.000 0.044
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 5 0.3446 0.8460 0.144 0.016 0.000 0.012 0.828
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.1493 0.7872 0.948 0.000 0.000 0.024 0.028
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1399 0.8897 0.028 0.020 0.000 0.952 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.2852 0.8341 0.000 0.828 0.000 0.000 0.172
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.3636 0.5913 0.000 0.728 0.272 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.3578 0.8457 0.132 0.000 0.000 0.048 0.820
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.2516 0.7924 0.000 0.140 0.000 0.860 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 5 0.0794 0.8417 0.000 0.028 0.000 0.000 0.972
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 5 0.0703 0.8425 0.000 0.024 0.000 0.000 0.976
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.2424 0.8338 0.868 0.000 0.000 0.132 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1197 0.8845 0.048 0.000 0.000 0.952 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.1478 0.8540 0.000 0.064 0.936 0.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.9002 0.000 0.000 1.000 0.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 3 0.4341 0.2480 0.000 0.404 0.592 0.000 0.004
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0609 0.8609 0.000 0.980 0.000 0.000 0.020
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.5027 0.4750 0.000 0.304 0.640 0.000 0.056
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.1544 0.8438 0.000 0.000 0.932 0.068 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 6 0.4215 0.7477 0.056 0.244 0.000 0.000 0.000 0.700
#> F569915C-8F77-4D67-9730-30824DB57EE5 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.0000 0.9353 0.000 0.000 0.000 1.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.3804 0.1195 0.424 0.000 0.576 0.000 0.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.0000 0.6879 0.000 0.000 1.000 0.000 0.000 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.2762 0.7541 0.804 0.000 0.000 0.196 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.3860 0.0728 0.000 0.000 0.000 0.528 0.472 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.0000 0.6879 0.000 0.000 1.000 0.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.0000 0.6879 0.000 0.000 1.000 0.000 0.000 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.3017 0.7632 0.816 0.020 0.000 0.000 0.000 0.164
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 5 0.1444 0.8195 0.000 0.000 0.000 0.072 0.928 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.2378 0.6340 0.000 0.152 0.848 0.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 3 0.0000 0.6879 0.000 0.000 1.000 0.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 6 0.0000 0.8964 0.000 0.000 0.000 0.000 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.0000 0.6879 0.000 0.000 1.000 0.000 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 3 0.3956 0.2350 0.000 0.264 0.704 0.000 0.032 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.1501 0.8284 0.000 0.000 0.076 0.000 0.924 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.5202 0.4542 0.000 0.260 0.600 0.000 0.000 0.140
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.3563 0.6931 0.000 0.664 0.336 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 6 0.0000 0.8964 0.000 0.000 0.000 0.000 0.000 1.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.3747 0.6198 0.000 0.604 0.396 0.000 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.5606 0.4979 0.000 0.468 0.412 0.112 0.008 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.0000 0.9353 0.000 0.000 0.000 1.000 0.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 6 0.0000 0.8964 0.000 0.000 0.000 0.000 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.0000 0.6864 0.000 1.000 0.000 0.000 0.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0000 0.6864 0.000 1.000 0.000 0.000 0.000 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0000 0.9353 0.000 0.000 0.000 1.000 0.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.3619 0.6523 0.744 0.000 0.232 0.024 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.5042 0.4812 0.000 0.288 0.604 0.000 0.000 0.108
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 5 0.3765 0.3904 0.000 0.000 0.404 0.000 0.596 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 5 0.1327 0.8311 0.000 0.000 0.064 0.000 0.936 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.0000 0.9353 0.000 0.000 0.000 1.000 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.3175 0.7536 0.000 0.744 0.256 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.0000 0.9353 0.000 0.000 0.000 1.000 0.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.3727 0.5061 0.000 0.388 0.612 0.000 0.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.2907 0.6347 0.020 0.152 0.828 0.000 0.000 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.3101 0.7568 0.000 0.756 0.244 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 5 0.2562 0.7228 0.000 0.000 0.172 0.000 0.828 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.3330 0.7381 0.000 0.716 0.284 0.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.0146 0.9308 0.000 0.000 0.000 0.996 0.004 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.3198 0.6406 0.740 0.000 0.000 0.260 0.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.6879 0.000 0.000 1.000 0.000 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 5 0.3747 0.4056 0.000 0.000 0.396 0.000 0.604 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0000 0.9353 0.000 0.000 0.000 1.000 0.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.0000 0.9353 0.000 0.000 0.000 1.000 0.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.3101 0.7568 0.000 0.756 0.244 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 6 0.3101 0.7699 0.000 0.244 0.000 0.000 0.000 0.756
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.3747 0.4991 0.000 0.396 0.604 0.000 0.000 0.000
#> 1CC36859-357A-49E0-A367-4F57D47288BA 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.6864 0.000 1.000 0.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.2340 0.6364 0.000 0.148 0.852 0.000 0.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 3 0.3607 -0.0318 0.000 0.348 0.652 0.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.7488 0.0962 0.220 0.244 0.372 0.000 0.000 0.164
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.2762 0.7513 0.804 0.000 0.000 0.000 0.000 0.196
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0000 0.9353 0.000 0.000 0.000 1.000 0.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.3101 0.7568 0.000 0.756 0.244 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0000 0.6879 0.000 0.000 1.000 0.000 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 5 0.3647 0.3582 0.000 0.000 0.360 0.000 0.640 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.0000 0.8964 0.000 0.000 0.000 0.000 0.000 1.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.3684 0.3920 0.000 0.000 0.628 0.372 0.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.6864 0.000 1.000 0.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.0000 0.6864 0.000 1.000 0.000 0.000 0.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.9101 1.000 0.000 0.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0000 0.9353 0.000 0.000 0.000 1.000 0.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.3774 0.2724 0.000 0.000 0.592 0.000 0.408 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 5 0.0000 0.8636 0.000 0.000 0.000 0.000 1.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 5 0.4609 0.1579 0.000 0.420 0.040 0.000 0.540 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0000 0.6879 0.000 0.000 1.000 0.000 0.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.4348 0.5940 0.000 0.152 0.724 0.000 0.124 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 5 0.1387 0.8207 0.000 0.000 0.000 0.068 0.932 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 17471 rows and 87 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.134 0.504 0.737 0.4400 0.513 0.513
#> 3 3 0.335 0.817 0.847 0.3155 0.565 0.359
#> 4 4 0.464 0.628 0.817 0.1283 0.590 0.294
#> 5 5 0.505 0.550 0.764 0.1202 0.866 0.639
#> 6 6 0.584 0.486 0.685 0.0621 0.890 0.642
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 2 0.5629 0.63307 0.132 0.868
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.8861 0.54036 0.304 0.696
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.8207 0.62492 0.744 0.256
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.1184 0.63981 0.016 0.984
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.8207 0.62492 0.744 0.256
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.8327 0.62450 0.736 0.264
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 2 0.9393 0.22151 0.356 0.644
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 2 0.6531 0.59779 0.168 0.832
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.9286 0.59666 0.344 0.656
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.8207 0.62492 0.744 0.256
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 2 0.5519 0.63710 0.128 0.872
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.9896 0.43382 0.560 0.440
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 1 0.9833 -0.11887 0.576 0.424
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.8909 0.60030 0.692 0.308
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.1414 0.64419 0.020 0.980
#> 0EA8288E-824D-4304-A053-5A833361F5C5 2 0.5519 0.63579 0.128 0.872
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.9580 0.52919 0.620 0.380
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.8207 0.62492 0.744 0.256
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.9896 0.48641 0.440 0.560
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.5946 0.63244 0.144 0.856
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 1 0.4431 0.51062 0.908 0.092
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 2 0.5519 0.63579 0.128 0.872
#> F9C23182-91C4-4145-AE52-526FE8EB199D 1 0.9850 0.06222 0.572 0.428
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 1 0.8144 0.38715 0.748 0.252
#> 932C8E98-A352-409C-8744-3D49FABCE425 2 0.6048 0.55217 0.148 0.852
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.7674 0.59158 0.224 0.776
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 2 0.5519 0.63579 0.128 0.872
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 1 0.7745 0.41969 0.772 0.228
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.7815 0.61024 0.768 0.232
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 2 0.5629 0.63380 0.132 0.868
#> 36EDD202-A845-4CE7-95D5-A515C471262E 1 0.9866 -0.00817 0.568 0.432
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.7950 0.61473 0.760 0.240
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.8081 0.62094 0.752 0.248
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.5629 0.63462 0.132 0.868
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 2 0.5519 0.63579 0.128 0.872
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.9491 0.58364 0.368 0.632
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.9427 0.58934 0.360 0.640
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.8207 0.30995 0.256 0.744
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.8443 0.62227 0.728 0.272
#> FF7021BB-414B-4466-BC43-372D856E6A1D 2 0.5737 0.62963 0.136 0.864
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.9710 0.40066 0.400 0.600
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 2 0.0938 0.64186 0.012 0.988
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.9850 0.32470 0.428 0.572
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.8386 0.62261 0.732 0.268
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 1 0.7883 0.41092 0.764 0.236
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.8207 0.62492 0.744 0.256
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.9580 0.57227 0.380 0.620
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.2236 0.65075 0.036 0.964
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.9686 0.17693 0.396 0.604
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 2 0.6623 0.60392 0.172 0.828
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 1 0.9393 0.13754 0.644 0.356
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 1 0.9248 0.19983 0.660 0.340
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.7815 0.61024 0.768 0.232
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 1 0.7950 0.40560 0.760 0.240
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.8207 0.62492 0.744 0.256
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.9248 0.57614 0.660 0.340
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.9977 0.30586 0.472 0.528
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.9970 0.22437 0.468 0.532
#> C41F3064-4483-4796-B860-82155BAA5157 2 0.9850 -0.04246 0.428 0.572
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.8327 0.62290 0.736 0.264
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 1 0.8713 0.30832 0.708 0.292
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 2 0.5519 0.63579 0.128 0.872
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 2 0.0672 0.63735 0.008 0.992
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.9393 0.59161 0.356 0.644
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.8608 0.54448 0.284 0.716
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 1 0.9491 0.09537 0.632 0.368
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.8144 0.59586 0.252 0.748
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 1 0.8443 0.35060 0.728 0.272
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 2 0.5737 0.62963 0.136 0.864
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 2 0.6247 0.60975 0.156 0.844
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 2 0.5737 0.58516 0.136 0.864
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 1 0.5842 0.48329 0.860 0.140
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.7745 0.58637 0.228 0.772
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.7745 0.58637 0.228 0.772
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 2 0.5629 0.63380 0.132 0.868
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 2 0.0672 0.64064 0.008 0.992
#> 3C582264-85BC-4569-A888-8EB3008E5198 1 0.9427 0.12393 0.640 0.360
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.9358 0.59178 0.352 0.648
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.7815 0.61024 0.768 0.232
#> AF8AB83D-2917-4752-8C38-CF84C565B565 2 0.6712 0.48228 0.176 0.824
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.9044 0.53420 0.320 0.680
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.7745 0.58637 0.228 0.772
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.9795 0.35540 0.416 0.584
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 1 0.7883 0.41109 0.764 0.236
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.9393 0.59161 0.356 0.644
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.8608 0.60481 0.284 0.716
#> 0D907A63-D23F-4684-80D9-0BA552435B86 2 0.0376 0.63908 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 1 0.6621 0.804 0.752 0.100 0.148
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.3412 0.833 0.124 0.000 0.876
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0592 0.830 0.988 0.000 0.012
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.4346 0.775 0.816 0.000 0.184
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0592 0.830 0.988 0.000 0.012
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.4931 0.814 0.828 0.140 0.032
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.3412 0.822 0.876 0.000 0.124
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 1 0.5254 0.775 0.736 0.000 0.264
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.0000 0.842 0.000 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0592 0.830 0.988 0.000 0.012
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.3192 0.825 0.888 0.000 0.112
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3619 0.823 0.864 0.000 0.136
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.0000 0.842 0.000 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.3965 0.821 0.860 0.008 0.132
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.3686 0.823 0.140 0.000 0.860
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.3484 0.832 0.904 0.048 0.048
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.3619 0.823 0.864 0.000 0.136
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0747 0.831 0.984 0.000 0.016
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.2165 0.791 0.000 0.064 0.936
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1860 0.855 0.052 0.000 0.948
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 1 0.5690 0.754 0.708 0.004 0.288
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 1 0.6793 0.801 0.740 0.100 0.160
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.0424 0.840 0.008 0.000 0.992
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.5098 0.979 0.000 0.752 0.248
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.2711 0.832 0.912 0.000 0.088
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.3619 0.826 0.136 0.000 0.864
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 1 0.6783 0.311 0.588 0.016 0.396
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.5058 0.981 0.000 0.756 0.244
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.3816 0.798 0.852 0.148 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.2959 0.818 0.900 0.100 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 3 0.3686 0.669 0.000 0.140 0.860
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.4811 0.809 0.828 0.148 0.024
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.4099 0.803 0.852 0.140 0.008
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.4452 0.765 0.808 0.000 0.192
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 1 0.6848 0.799 0.736 0.100 0.164
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 1 0.6264 0.641 0.616 0.004 0.380
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 3 0.4110 0.675 0.152 0.004 0.844
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.2878 0.831 0.904 0.000 0.096
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.4351 0.816 0.828 0.004 0.168
#> FF7021BB-414B-4466-BC43-372D856E6A1D 1 0.3769 0.824 0.880 0.016 0.104
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.3267 0.838 0.116 0.000 0.884
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.5216 0.672 0.740 0.000 0.260
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.1529 0.854 0.040 0.000 0.960
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.4902 0.822 0.844 0.092 0.064
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.5058 0.981 0.000 0.756 0.244
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.3619 0.823 0.864 0.000 0.136
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.5650 0.340 0.312 0.000 0.688
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.3686 0.823 0.140 0.000 0.860
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.2537 0.790 0.080 0.000 0.920
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.2625 0.833 0.916 0.000 0.084
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.5098 0.979 0.000 0.752 0.248
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.5098 0.979 0.000 0.752 0.248
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.3816 0.798 0.852 0.148 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.5098 0.979 0.000 0.752 0.248
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.4277 0.807 0.852 0.132 0.016
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.4291 0.816 0.820 0.000 0.180
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.0000 0.842 0.000 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.0000 0.842 0.000 0.000 1.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.2711 0.832 0.912 0.000 0.088
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.3619 0.823 0.864 0.000 0.136
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.5058 0.981 0.000 0.756 0.244
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 1 0.7108 0.789 0.716 0.100 0.184
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.5810 0.568 0.664 0.000 0.336
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.3192 0.743 0.112 0.000 0.888
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0892 0.849 0.020 0.000 0.980
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.5058 0.981 0.000 0.756 0.244
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.3192 0.840 0.112 0.000 0.888
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.5988 0.791 0.000 0.632 0.368
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.6500 0.805 0.760 0.100 0.140
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.2796 0.820 0.908 0.092 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.3116 0.827 0.892 0.000 0.108
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.5058 0.981 0.000 0.756 0.244
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.2625 0.849 0.084 0.000 0.916
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.3412 0.833 0.124 0.000 0.876
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.2959 0.818 0.900 0.100 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.4121 0.790 0.832 0.000 0.168
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.5058 0.981 0.000 0.756 0.244
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 1 0.6742 0.712 0.656 0.028 0.316
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.3816 0.798 0.852 0.148 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.3116 0.827 0.892 0.000 0.108
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.0000 0.842 0.000 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.3412 0.833 0.124 0.000 0.876
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0000 0.842 0.000 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.5058 0.981 0.000 0.756 0.244
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.0000 0.842 0.000 0.000 1.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.3412 0.833 0.124 0.000 0.876
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.3879 0.819 0.152 0.000 0.848
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.3375 0.751 0.016 0.092 0.876 0.016
#> F569915C-8F77-4D67-9730-30824DB57EE5 4 0.4327 0.653 0.000 0.216 0.016 0.768
#> E3B71CB7-673B-4741-8607-4F0A11633036 4 0.5472 0.522 0.280 0.044 0.000 0.676
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.0336 0.711 0.008 0.000 0.000 0.992
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.2996 0.645 0.064 0.044 0.000 0.892
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.5165 -0.210 0.484 0.512 0.000 0.004
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 3 0.7499 0.385 0.284 0.152 0.548 0.016
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.4936 0.503 0.020 0.280 0.000 0.700
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.5253 0.384 0.000 0.624 0.016 0.360
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 4 0.5446 0.526 0.276 0.044 0.000 0.680
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.0707 0.709 0.020 0.000 0.000 0.980
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 4 0.7748 0.308 0.280 0.280 0.000 0.440
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.3852 0.746 0.000 0.800 0.008 0.192
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.6722 0.183 0.500 0.408 0.000 0.092
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 4 0.3982 0.652 0.000 0.220 0.004 0.776
#> 0EA8288E-824D-4304-A053-5A833361F5C5 4 0.5517 0.236 0.020 0.000 0.412 0.568
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.5835 0.502 0.064 0.280 0.000 0.656
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 4 0.5446 0.526 0.276 0.044 0.000 0.680
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.1970 0.846 0.000 0.932 0.008 0.060
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 4 0.1124 0.710 0.004 0.012 0.012 0.972
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.1362 0.789 0.020 0.964 0.004 0.012
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.0592 0.763 0.000 0.000 0.984 0.016
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5004 0.316 0.000 0.604 0.004 0.392
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.1302 0.850 0.000 0.956 0.000 0.044
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.0707 0.709 0.020 0.000 0.000 0.980
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 4 0.1674 0.713 0.004 0.032 0.012 0.952
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.4040 0.545 0.000 0.000 0.752 0.248
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.1302 0.850 0.000 0.956 0.000 0.044
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0188 0.636 0.996 0.000 0.000 0.004
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.1297 0.767 0.020 0.000 0.964 0.016
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.1854 0.847 0.000 0.940 0.012 0.048
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.4483 0.481 0.712 0.284 0.000 0.004
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.5309 0.559 0.700 0.044 0.000 0.256
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 4 0.4086 0.659 0.008 0.216 0.000 0.776
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.0592 0.763 0.000 0.000 0.984 0.016
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.2111 0.845 0.000 0.932 0.024 0.044
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.2363 0.842 0.000 0.920 0.024 0.056
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 4 0.0707 0.709 0.020 0.000 0.000 0.980
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.5860 0.238 0.040 0.580 0.000 0.380
#> FF7021BB-414B-4466-BC43-372D856E6A1D 4 0.5428 0.321 0.020 0.000 0.380 0.600
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 4 0.4599 0.620 0.000 0.248 0.016 0.736
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.0336 0.711 0.008 0.000 0.000 0.992
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 4 0.4804 0.594 0.000 0.276 0.016 0.708
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.7740 -0.203 0.348 0.236 0.000 0.416
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.1302 0.850 0.000 0.956 0.000 0.044
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.5835 0.502 0.064 0.280 0.000 0.656
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.2282 0.844 0.000 0.924 0.024 0.052
#> 117673A3-2918-4702-8583-B66ADE6E4338 4 0.4018 0.648 0.000 0.224 0.004 0.772
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.4343 0.529 0.004 0.264 0.000 0.732
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 4 0.3907 0.616 0.232 0.000 0.000 0.768
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.1302 0.850 0.000 0.956 0.000 0.044
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.1557 0.844 0.000 0.944 0.000 0.056
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0188 0.636 0.996 0.000 0.000 0.004
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.1302 0.850 0.000 0.956 0.000 0.044
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.5865 0.493 0.612 0.048 0.000 0.340
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.5130 0.512 0.020 0.312 0.000 0.668
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.4019 0.732 0.000 0.792 0.012 0.196
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.4804 0.598 0.000 0.708 0.016 0.276
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0707 0.709 0.020 0.000 0.000 0.980
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.5599 0.520 0.052 0.276 0.000 0.672
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1302 0.850 0.000 0.956 0.000 0.044
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.2610 0.754 0.000 0.088 0.900 0.012
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.0188 0.711 0.004 0.000 0.000 0.996
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.4832 0.709 0.000 0.768 0.176 0.056
#> 1CC36859-357A-49E0-A367-4F57D47288BA 4 0.3113 0.682 0.004 0.108 0.012 0.876
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.1302 0.850 0.000 0.956 0.000 0.044
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 4 0.4630 0.619 0.000 0.252 0.016 0.732
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.1661 0.849 0.000 0.944 0.004 0.052
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.4847 0.646 0.020 0.200 0.764 0.016
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.1884 0.763 0.020 0.016 0.948 0.016
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0707 0.709 0.020 0.000 0.000 0.980
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.1302 0.850 0.000 0.956 0.000 0.044
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 4 0.4599 0.626 0.000 0.248 0.016 0.736
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 4 0.4434 0.640 0.000 0.228 0.016 0.756
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.1297 0.767 0.020 0.000 0.964 0.016
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0188 0.711 0.004 0.000 0.000 0.996
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.1302 0.850 0.000 0.956 0.000 0.044
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.6050 0.188 0.000 0.432 0.524 0.044
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0188 0.636 0.996 0.000 0.000 0.004
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0707 0.709 0.020 0.000 0.000 0.980
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 4 0.4690 0.553 0.000 0.260 0.016 0.724
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 4 0.4364 0.649 0.000 0.220 0.016 0.764
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 4 0.4896 0.497 0.004 0.280 0.012 0.704
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.1302 0.850 0.000 0.956 0.000 0.044
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.4468 0.662 0.000 0.752 0.016 0.232
#> A247D92D-253A-4BEC-B450-184AF90D17D0 4 0.4088 0.641 0.000 0.232 0.004 0.764
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.0657 0.708 0.004 0.000 0.012 0.984
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 5 0.3616 0.79631 0.052 0.004 0.000 0.116 0.828
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.4075 0.69512 0.000 0.160 0.780 0.060 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.1544 0.43895 0.932 0.000 0.068 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.1648 0.69460 0.040 0.000 0.940 0.020 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.3895 0.51432 0.680 0.000 0.320 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.6749 -0.29979 0.268 0.396 0.000 0.336 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.4450 0.23553 0.736 0.004 0.000 0.044 0.216
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.1502 0.68850 0.056 0.004 0.940 0.000 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.5294 0.18302 0.000 0.564 0.380 0.056 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.2516 0.46035 0.860 0.000 0.140 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.1341 0.68809 0.056 0.000 0.944 0.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.2806 0.34203 0.844 0.152 0.000 0.004 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.5249 0.31439 0.004 0.608 0.336 0.052 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.4559 0.26107 0.748 0.152 0.000 0.100 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.6801 0.61590 0.152 0.168 0.600 0.080 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.9689 0.00846 0.208 0.132 0.300 0.132 0.228
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.4949 0.45131 0.572 0.000 0.396 0.032 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0162 0.37141 0.996 0.000 0.004 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.2269 0.74370 0.000 0.920 0.032 0.020 0.028
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.0324 0.71568 0.004 0.004 0.992 0.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.4230 0.57984 0.192 0.764 0.036 0.008 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.1043 0.80727 0.040 0.000 0.000 0.000 0.960
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.5459 -0.09450 0.000 0.472 0.468 0.060 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2519 0.73186 0.000 0.884 0.100 0.016 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 3 0.1410 0.68497 0.060 0.000 0.940 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.3437 0.71808 0.000 0.120 0.832 0.048 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.7385 0.46413 0.028 0.244 0.040 0.148 0.540
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.1942 0.74326 0.000 0.920 0.068 0.012 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 4 0.3424 0.66345 0.240 0.000 0.000 0.760 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.1430 0.80534 0.052 0.000 0.000 0.004 0.944
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.3246 0.68859 0.000 0.808 0.184 0.008 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 4 0.6273 0.29872 0.292 0.184 0.000 0.524 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.6418 -0.12927 0.412 0.000 0.172 0.416 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.7219 0.61422 0.180 0.164 0.556 0.100 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 5 0.0703 0.80165 0.024 0.000 0.000 0.000 0.976
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.3758 0.64930 0.000 0.816 0.004 0.128 0.052
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3804 0.64836 0.000 0.812 0.004 0.132 0.052
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.1341 0.68809 0.056 0.000 0.944 0.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.7658 0.27612 0.428 0.156 0.328 0.088 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.8095 0.51025 0.208 0.052 0.516 0.144 0.080
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.4337 0.66719 0.000 0.196 0.748 0.056 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.0963 0.70180 0.036 0.000 0.964 0.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.4303 0.67076 0.000 0.192 0.752 0.056 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.6947 0.04921 0.400 0.008 0.248 0.344 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0162 0.75004 0.000 0.996 0.000 0.004 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4366 0.50896 0.664 0.000 0.320 0.016 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.2032 0.73960 0.000 0.924 0.004 0.020 0.052
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.6760 0.61354 0.152 0.176 0.600 0.072 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.0693 0.71342 0.012 0.008 0.980 0.000 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.5112 0.17904 0.684 0.044 0.252 0.020 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0162 0.75004 0.000 0.996 0.000 0.004 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4384 0.57965 0.000 0.728 0.228 0.044 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.3424 0.66345 0.240 0.000 0.000 0.760 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.2305 0.73583 0.000 0.896 0.092 0.012 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.6392 -0.10687 0.432 0.000 0.168 0.400 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.6215 -0.12512 0.348 0.152 0.500 0.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.4576 0.55407 0.000 0.692 0.268 0.040 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.5381 0.19796 0.000 0.428 0.516 0.056 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 3 0.2690 0.66411 0.156 0.000 0.844 0.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.3999 0.50555 0.656 0.000 0.344 0.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0162 0.75004 0.000 0.996 0.000 0.004 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 5 0.2563 0.76698 0.000 0.008 0.000 0.120 0.872
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.0609 0.71025 0.020 0.000 0.980 0.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.6124 0.56555 0.000 0.668 0.072 0.152 0.108
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.0324 0.71568 0.004 0.004 0.992 0.000 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0162 0.75004 0.000 0.996 0.000 0.004 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.5635 0.47584 0.012 0.320 0.600 0.068 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0162 0.75070 0.000 0.996 0.004 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 5 0.3739 0.79527 0.052 0.008 0.000 0.116 0.824
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 5 0.4359 0.29033 0.412 0.000 0.000 0.004 0.584
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.3724 0.64332 0.184 0.000 0.788 0.028 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0162 0.75004 0.000 0.996 0.000 0.004 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.4537 0.67156 0.000 0.184 0.740 0.076 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4114 0.69187 0.000 0.164 0.776 0.060 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.1430 0.80534 0.052 0.000 0.000 0.004 0.944
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.3612 0.65115 0.172 0.000 0.800 0.028 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0162 0.75004 0.000 0.996 0.000 0.004 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 2 0.5720 0.33325 0.000 0.604 0.000 0.128 0.268
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 4 0.3424 0.66345 0.240 0.000 0.000 0.760 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.3687 0.64575 0.180 0.000 0.792 0.028 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.3876 0.68595 0.000 0.192 0.776 0.032 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.4177 0.69024 0.000 0.164 0.772 0.064 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.0854 0.71738 0.004 0.012 0.976 0.008 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.2625 0.72812 0.000 0.876 0.108 0.016 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.4584 0.42205 0.000 0.660 0.312 0.028 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.6782 0.61394 0.152 0.172 0.600 0.076 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.0162 0.71438 0.004 0.000 0.996 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 6 0.4835 0.69282 0.000 0.016 0.000 0.040 0.340 0.604
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.4078 0.53821 0.004 0.180 0.748 0.000 0.068 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 4 0.2741 0.60490 0.092 0.000 0.032 0.868 0.008 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 3 0.4526 0.57557 0.020 0.000 0.748 0.128 0.100 0.004
#> 2F38E3B1-4975-4877-9DCC-C00270602180 4 0.1913 0.62198 0.016 0.000 0.044 0.924 0.016 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 1 0.6626 0.42977 0.516 0.200 0.060 0.220 0.004 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 4 0.6023 0.33166 0.256 0.016 0.000 0.584 0.028 0.116
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.4790 0.57221 0.020 0.012 0.732 0.144 0.092 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.5758 0.07688 0.000 0.284 0.504 0.000 0.212 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 4 0.2639 0.60857 0.084 0.000 0.032 0.876 0.008 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.4926 0.53733 0.020 0.000 0.696 0.124 0.160 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 4 0.2895 0.57472 0.116 0.016 0.000 0.852 0.016 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 3 0.5706 -0.00951 0.000 0.388 0.476 0.008 0.128 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 4 0.4543 0.28584 0.384 0.016 0.000 0.584 0.016 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 3 0.5218 0.48061 0.000 0.188 0.640 0.008 0.164 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 5 0.7662 0.26844 0.004 0.096 0.172 0.056 0.488 0.184
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 4 0.2873 0.59927 0.068 0.012 0.044 0.872 0.004 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 4 0.2858 0.59905 0.092 0.000 0.016 0.864 0.028 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.4281 0.53099 0.000 0.708 0.072 0.000 0.220 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.3356 0.59770 0.004 0.000 0.824 0.072 0.100 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.5241 0.55816 0.028 0.720 0.072 0.048 0.132 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 6 0.3271 0.71107 0.000 0.000 0.000 0.008 0.232 0.760
#> F9C23182-91C4-4145-AE52-526FE8EB199D 3 0.4696 0.36102 0.000 0.276 0.660 0.016 0.048 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.3822 0.52118 0.008 0.784 0.168 0.016 0.024 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 3 0.4447 0.57106 0.020 0.000 0.744 0.144 0.092 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 3 0.1341 0.60476 0.000 0.028 0.948 0.000 0.024 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.6939 0.20500 0.000 0.212 0.040 0.048 0.528 0.172
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.1275 0.63281 0.000 0.956 0.016 0.016 0.012 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0865 0.75122 0.964 0.000 0.000 0.036 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 6 0.1082 0.66728 0.004 0.000 0.000 0.040 0.000 0.956
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.4322 0.40293 0.000 0.672 0.288 0.008 0.032 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 1 0.5476 0.53235 0.644 0.076 0.060 0.220 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.4305 0.13374 0.436 0.000 0.020 0.544 0.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 3 0.5380 0.50639 0.016 0.032 0.692 0.068 0.184 0.008
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 6 0.3707 0.68694 0.000 0.000 0.000 0.008 0.312 0.680
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.4468 0.42621 0.000 0.640 0.040 0.000 0.316 0.004
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.4497 0.40585 0.000 0.624 0.048 0.000 0.328 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 3 0.4136 0.58493 0.020 0.000 0.772 0.132 0.076 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.6596 0.28566 0.148 0.100 0.220 0.532 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.6618 -0.25054 0.000 0.136 0.376 0.056 0.428 0.004
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 3 0.4431 0.51376 0.000 0.200 0.704 0.000 0.096 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.5344 0.54292 0.020 0.016 0.680 0.120 0.164 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 3 0.4482 0.52165 0.000 0.188 0.712 0.004 0.096 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.5368 0.21827 0.364 0.012 0.084 0.540 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.2219 0.60885 0.000 0.864 0.000 0.000 0.136 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.1624 0.62158 0.044 0.012 0.008 0.936 0.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.4274 0.48704 0.000 0.676 0.048 0.000 0.276 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 3 0.5768 0.44184 0.000 0.188 0.592 0.024 0.196 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.2922 0.61290 0.016 0.012 0.864 0.096 0.012 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 4 0.6922 0.27216 0.088 0.004 0.248 0.500 0.156 0.004
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0547 0.64116 0.000 0.980 0.000 0.000 0.020 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.4107 0.50404 0.008 0.764 0.176 0.016 0.036 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0865 0.75122 0.964 0.000 0.000 0.036 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.3384 0.53974 0.000 0.820 0.132 0.016 0.032 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.4579 0.17457 0.404 0.012 0.020 0.564 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.4854 0.27708 0.020 0.028 0.340 0.608 0.004 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.5145 0.21523 0.000 0.556 0.372 0.016 0.056 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.3979 0.51501 0.000 0.160 0.772 0.016 0.052 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 3 0.5404 0.50762 0.020 0.000 0.636 0.148 0.196 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.1296 0.61956 0.000 0.012 0.032 0.952 0.004 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.1007 0.64473 0.000 0.956 0.000 0.000 0.044 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 6 0.4526 0.56110 0.000 0.032 0.000 0.000 0.456 0.512
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.3850 0.59075 0.020 0.000 0.800 0.084 0.096 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 5 0.5935 0.15986 0.000 0.360 0.132 0.000 0.488 0.020
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.2705 0.61169 0.004 0.000 0.872 0.072 0.052 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.2416 0.59850 0.000 0.844 0.000 0.000 0.156 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.5061 0.47048 0.000 0.228 0.648 0.008 0.116 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.3202 0.60624 0.000 0.816 0.040 0.000 0.144 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 6 0.4933 0.69478 0.008 0.016 0.000 0.032 0.336 0.608
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 6 0.3864 0.24666 0.004 0.000 0.000 0.344 0.004 0.648
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.5556 0.51847 0.024 0.004 0.656 0.120 0.188 0.008
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.2135 0.61517 0.000 0.872 0.000 0.000 0.128 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.4710 0.50661 0.000 0.196 0.688 0.004 0.112 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.4201 0.53770 0.004 0.176 0.740 0.000 0.080 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.1082 0.66728 0.004 0.000 0.000 0.040 0.000 0.956
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.4675 0.57774 0.020 0.012 0.760 0.088 0.112 0.008
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0363 0.64296 0.000 0.988 0.000 0.000 0.012 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 5 0.5480 0.19576 0.000 0.328 0.000 0.000 0.528 0.144
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0865 0.75122 0.964 0.000 0.000 0.036 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.5560 0.52374 0.024 0.004 0.660 0.140 0.164 0.008
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.4651 0.54180 0.008 0.176 0.724 0.012 0.080 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.4389 0.52007 0.000 0.188 0.712 0.000 0.100 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.1844 0.61340 0.000 0.016 0.928 0.040 0.016 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.3822 0.52376 0.008 0.784 0.168 0.016 0.024 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.5877 -0.10797 0.000 0.372 0.428 0.000 0.200 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.5162 0.49034 0.000 0.164 0.648 0.008 0.180 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.4924 0.56757 0.020 0.020 0.724 0.080 0.156 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", "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 17471 rows and 87 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 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 0.976 0.954 0.979 0.3100 0.668 0.668
#> 3 3 0.958 0.915 0.970 0.9729 0.621 0.472
#> 4 4 0.646 0.726 0.862 0.1803 0.779 0.496
#> 5 5 0.836 0.839 0.919 0.0821 0.889 0.623
#> 6 6 0.721 0.707 0.807 0.0526 0.899 0.579
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
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 1 0.000 0.900 1.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.000 0.998 0.000 1.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 2 0.000 0.998 0.000 1.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 2 0.000 0.998 0.000 1.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 2 0.000 0.998 0.000 1.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.000 0.998 0.000 1.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.955 0.497 0.624 0.376
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 2 0.000 0.998 0.000 1.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.000 0.998 0.000 1.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 2 0.000 0.998 0.000 1.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 2 0.000 0.998 0.000 1.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 2 0.000 0.998 0.000 1.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.000 0.998 0.000 1.000
#> 694B0504-095E-431A-84F2-B4387072138E 2 0.000 0.998 0.000 1.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.000 0.998 0.000 1.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 1 0.000 0.900 1.000 0.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 2 0.000 0.998 0.000 1.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 2 0.000 0.998 0.000 1.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.000 0.998 0.000 1.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.000 0.998 0.000 1.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.000 0.998 0.000 1.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 1 0.000 0.900 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.000 0.998 0.000 1.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.000 0.998 0.000 1.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 2 0.000 0.998 0.000 1.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.000 0.998 0.000 1.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 1 0.000 0.900 1.000 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.000 0.998 0.000 1.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 2 0.000 0.998 0.000 1.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 1 0.000 0.900 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.000 0.998 0.000 1.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.000 0.998 0.000 1.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 2 0.000 0.998 0.000 1.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 2 0.000 0.998 0.000 1.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 1 0.000 0.900 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 1 0.000 0.900 1.000 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 1 0.861 0.657 0.716 0.284
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 2 0.000 0.998 0.000 1.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 2 0.000 0.998 0.000 1.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 1 0.000 0.900 1.000 0.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.000 0.998 0.000 1.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 2 0.000 0.998 0.000 1.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.000 0.998 0.000 1.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 2 0.000 0.998 0.000 1.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.000 0.998 0.000 1.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 2 0.000 0.998 0.000 1.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.541 0.839 0.124 0.876
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.000 0.998 0.000 1.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.000 0.998 0.000 1.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 2 0.000 0.998 0.000 1.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.000 0.998 0.000 1.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.000 0.998 0.000 1.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 2 0.000 0.998 0.000 1.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.000 0.998 0.000 1.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 2 0.000 0.998 0.000 1.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 2 0.000 0.998 0.000 1.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.000 0.998 0.000 1.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.000 0.998 0.000 1.000
#> C41F3064-4483-4796-B860-82155BAA5157 2 0.000 0.998 0.000 1.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 2 0.000 0.998 0.000 1.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.000 0.998 0.000 1.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 1 0.000 0.900 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 2 0.000 0.998 0.000 1.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 1 0.913 0.589 0.672 0.328
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.000 0.998 0.000 1.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 1 0.689 0.769 0.816 0.184
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.000 0.998 0.000 1.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.000 0.998 0.000 1.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 1 0.000 0.900 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 1 0.000 0.900 1.000 0.000
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 2 0.000 0.998 0.000 1.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.000 0.998 0.000 1.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.000 0.998 0.000 1.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.000 0.998 0.000 1.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 1 0.000 0.900 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 2 0.000 0.998 0.000 1.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 1 1.000 0.145 0.500 0.500
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 1 0.000 0.900 1.000 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 2 0.000 0.998 0.000 1.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 2 0.000 0.998 0.000 1.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.000 0.998 0.000 1.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.000 0.998 0.000 1.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.000 0.998 0.000 1.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.000 0.998 0.000 1.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.141 0.975 0.020 0.980
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.000 0.998 0.000 1.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 2 0.000 0.998 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.9643 0.000 0.000 1.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 2 0.0000 0.9607 0.000 1.000 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.0000 0.9668 1.000 0.000 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0747 0.9504 0.984 0.016 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.0000 0.9668 1.000 0.000 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.0424 0.9548 0.008 0.992 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.0000 0.9668 1.000 0.000 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 2 0.6299 0.0756 0.476 0.524 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.0000 0.9607 0.000 1.000 0.000
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.0000 0.9668 1.000 0.000 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 1 0.0000 0.9668 1.000 0.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.9668 1.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0424 0.9550 0.008 0.992 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0000 0.9668 1.000 0.000 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 2 0.0000 0.9607 0.000 1.000 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.0000 0.9643 0.000 0.000 1.000
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.0000 0.9668 1.000 0.000 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0000 0.9668 1.000 0.000 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.9607 0.000 1.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 2 0.0000 0.9607 0.000 1.000 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.9607 0.000 1.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.0000 0.9643 0.000 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.2537 0.8834 0.080 0.920 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.9607 0.000 1.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.0000 0.9668 1.000 0.000 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 2 0.0424 0.9550 0.008 0.992 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0000 0.9643 0.000 0.000 1.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.9607 0.000 1.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.9668 1.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.0000 0.9643 0.000 0.000 1.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.9607 0.000 1.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3941 0.7849 0.156 0.844 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.0000 0.9668 1.000 0.000 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.1267 0.9431 0.972 0.004 0.024
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.0000 0.9643 0.000 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.6309 -0.0347 0.000 0.500 0.500
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 3 0.5968 0.4004 0.000 0.364 0.636
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.0000 0.9668 1.000 0.000 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.9668 1.000 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.0000 0.9643 0.000 0.000 1.000
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.9607 0.000 1.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 1 0.6295 0.0783 0.528 0.472 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0000 0.9607 0.000 1.000 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 1 0.0000 0.9668 1.000 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.9607 0.000 1.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0000 0.9668 1.000 0.000 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0000 0.9607 0.000 1.000 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.0000 0.9607 0.000 1.000 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 2 0.4178 0.7700 0.172 0.828 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0000 0.9668 1.000 0.000 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.9607 0.000 1.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.9607 0.000 1.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.9668 1.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.9607 0.000 1.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0000 0.9668 1.000 0.000 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.0000 0.9668 1.000 0.000 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.0000 0.9607 0.000 1.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 2 0.0000 0.9607 0.000 1.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.0000 0.9668 1.000 0.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.0000 0.9668 1.000 0.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.9607 0.000 1.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.0000 0.9643 0.000 0.000 1.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 1 0.4750 0.6798 0.784 0.216 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 2 0.1411 0.9306 0.000 0.964 0.036
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.0747 0.9485 0.016 0.984 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.9607 0.000 1.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 2 0.0000 0.9607 0.000 1.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.9607 0.000 1.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.0000 0.9643 0.000 0.000 1.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.0237 0.9608 0.004 0.000 0.996
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 1 0.0000 0.9668 1.000 0.000 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.9607 0.000 1.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 2 0.0000 0.9607 0.000 1.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 2 0.0000 0.9607 0.000 1.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.0000 0.9643 0.000 0.000 1.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 1 0.0424 0.9590 0.992 0.008 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9607 0.000 1.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.0000 0.9643 0.000 0.000 1.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.9668 1.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 1 0.0000 0.9668 1.000 0.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.0000 0.9607 0.000 1.000 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 2 0.0000 0.9607 0.000 1.000 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 2 0.0000 0.9607 0.000 1.000 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.9607 0.000 1.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.9607 0.000 1.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 2 0.0000 0.9607 0.000 1.000 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 2 0.1289 0.9336 0.032 0.968 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 3 0.0000 0.9143 0.000 0.000 1.000 0.000
#> F569915C-8F77-4D67-9730-30824DB57EE5 4 0.4543 0.6843 0.000 0.324 0.000 0.676
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.3688 0.8033 0.792 0.000 0.000 0.208
#> DAF84798-FE3F-403C-B589-7F256AF752BE 4 0.4454 0.1099 0.308 0.000 0.000 0.692
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.4277 0.7600 0.720 0.000 0.000 0.280
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.3024 0.7470 0.148 0.852 0.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.1042 0.8466 0.972 0.000 0.008 0.020
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.4820 0.7125 0.012 0.296 0.000 0.692
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 2 0.4877 0.0432 0.000 0.592 0.000 0.408
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.4072 0.7810 0.748 0.000 0.000 0.252
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.1867 0.7389 0.000 0.072 0.000 0.928
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.0000 0.8457 1.000 0.000 0.000 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.0336 0.8472 0.992 0.000 0.000 0.008
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 4 0.1716 0.7305 0.000 0.064 0.000 0.936
#> 0EA8288E-824D-4304-A053-5A833361F5C5 3 0.1637 0.8798 0.000 0.000 0.940 0.060
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.2281 0.8391 0.904 0.000 0.000 0.096
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.4103 0.7784 0.744 0.000 0.000 0.256
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 4 0.3726 0.7606 0.000 0.212 0.000 0.788
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 3 0.0000 0.9143 0.000 0.000 1.000 0.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0469 0.8737 0.012 0.988 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 4 0.5151 -0.3594 0.464 0.004 0.000 0.532
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 4 0.3610 0.6811 0.000 0.200 0.000 0.800
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 3 0.0000 0.9143 0.000 0.000 1.000 0.000
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0000 0.8457 1.000 0.000 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 3 0.0000 0.9143 0.000 0.000 1.000 0.000
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3837 0.6616 0.224 0.776 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.3873 0.6120 0.772 0.000 0.000 0.228
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.6682 0.6490 0.576 0.000 0.112 0.312
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 3 0.0000 0.9143 0.000 0.000 1.000 0.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.3074 0.7385 0.000 0.848 0.152 0.000
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.3688 0.6553 0.000 0.792 0.208 0.000
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.4981 0.4919 0.536 0.000 0.000 0.464
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0000 0.8457 1.000 0.000 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 3 0.5000 0.0571 0.000 0.000 0.500 0.500
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.2589 0.7601 0.000 0.116 0.000 0.884
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0188 0.8790 0.000 0.996 0.000 0.004
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.4996 0.1310 0.484 0.000 0.000 0.516
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.0817 0.8493 0.976 0.000 0.000 0.024
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.0188 0.8795 0.000 0.996 0.004 0.000
#> 117673A3-2918-4702-8583-B66ADE6E4338 2 0.4999 0.1240 0.000 0.508 0.000 0.492
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.5093 0.6444 0.012 0.348 0.000 0.640
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.4500 0.7290 0.684 0.000 0.000 0.316
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.0000 0.8457 1.000 0.000 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.0188 0.8454 0.996 0.000 0.000 0.004
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 1 0.1302 0.8484 0.956 0.000 0.000 0.044
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 2 0.4193 0.4860 0.000 0.732 0.000 0.268
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 4 0.4304 0.7255 0.000 0.284 0.000 0.716
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.0188 0.6801 0.004 0.000 0.000 0.996
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.3444 0.7164 0.816 0.000 0.000 0.184
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 3 0.0000 0.9143 0.000 0.000 1.000 0.000
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.2408 0.7561 0.000 0.104 0.000 0.896
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.6563 0.6721 0.000 0.208 0.160 0.632
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.4452 0.4875 0.008 0.732 0.000 0.260
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0188 0.8795 0.000 0.996 0.004 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 4 0.4250 0.7326 0.000 0.276 0.000 0.724
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 3 0.0000 0.9143 0.000 0.000 1.000 0.000
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 3 0.1022 0.8943 0.000 0.000 0.968 0.032
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.0000 0.6821 0.000 0.000 0.000 1.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 4 0.4250 0.7326 0.000 0.276 0.000 0.724
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 4 0.4193 0.7382 0.000 0.268 0.000 0.732
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 3 0.0000 0.9143 0.000 0.000 1.000 0.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.0000 0.6821 0.000 0.000 0.000 1.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 3 0.4008 0.6365 0.000 0.244 0.756 0.000
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0000 0.8457 1.000 0.000 0.000 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.0336 0.6818 0.008 0.000 0.000 0.992
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 2 0.4933 -0.0640 0.000 0.568 0.000 0.432
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 4 0.4040 0.7482 0.000 0.248 0.000 0.752
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 4 0.4941 0.4733 0.000 0.436 0.000 0.564
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.0000 0.8820 0.000 1.000 0.000 0.000
#> A247D92D-253A-4BEC-B450-184AF90D17D0 4 0.4164 0.7400 0.000 0.264 0.000 0.736
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.3764 0.7597 0.000 0.216 0.000 0.784
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 5 0.0613 0.9487 0.008 0.000 0.004 0.004 0.984
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.4219 0.6677 0.016 0.264 0.716 0.004 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 1 0.2471 0.7752 0.864 0.000 0.000 0.136 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 1 0.0609 0.8688 0.980 0.000 0.020 0.000 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 1 0.1205 0.8653 0.956 0.000 0.004 0.040 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 2 0.2694 0.8778 0.008 0.876 0.008 0.108 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 4 0.2280 0.8023 0.120 0.000 0.000 0.880 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 3 0.1788 0.8425 0.008 0.004 0.932 0.056 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 3 0.2563 0.8164 0.000 0.120 0.872 0.000 0.008
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 1 0.1197 0.8568 0.952 0.000 0.000 0.048 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 3 0.0963 0.8545 0.036 0.000 0.964 0.000 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 4 0.0880 0.8510 0.032 0.000 0.000 0.968 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0000 0.9675 0.000 1.000 0.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 4 0.1908 0.8239 0.092 0.000 0.000 0.908 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 1 0.1992 0.8521 0.924 0.032 0.044 0.000 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 5 0.1341 0.9270 0.000 0.000 0.056 0.000 0.944
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 1 0.4278 0.1499 0.548 0.000 0.000 0.452 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 1 0.0794 0.8658 0.972 0.000 0.000 0.028 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 2 0.2237 0.8988 0.008 0.904 0.084 0.004 0.000
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.1831 0.8391 0.076 0.000 0.920 0.004 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0162 0.9681 0.000 0.996 0.004 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 5 0.0162 0.9519 0.000 0.004 0.000 0.000 0.996
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0290 0.9648 0.008 0.992 0.000 0.000 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.0324 0.9678 0.004 0.992 0.004 0.000 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 1 0.1740 0.8548 0.932 0.000 0.056 0.012 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 1 0.1981 0.8493 0.924 0.048 0.028 0.000 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 5 0.0613 0.9500 0.008 0.004 0.000 0.004 0.984
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0162 0.9681 0.000 0.996 0.004 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 4 0.0510 0.8527 0.016 0.000 0.000 0.984 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 5 0.0162 0.9512 0.000 0.000 0.004 0.000 0.996
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0324 0.9678 0.004 0.992 0.004 0.000 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.2930 0.8052 0.004 0.832 0.000 0.164 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 4 0.4201 0.2951 0.000 0.000 0.408 0.592 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 1 0.0451 0.8694 0.988 0.000 0.000 0.008 0.004
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 5 0.0162 0.9519 0.000 0.004 0.000 0.000 0.996
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.1478 0.9234 0.000 0.936 0.000 0.000 0.064
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.0162 0.9657 0.000 0.996 0.000 0.000 0.004
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 1 0.1251 0.8668 0.956 0.000 0.036 0.008 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 4 0.0324 0.8506 0.004 0.000 0.004 0.992 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 5 0.3093 0.8057 0.008 0.000 0.168 0.000 0.824
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0162 0.9681 0.000 0.996 0.004 0.000 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 3 0.0404 0.8590 0.012 0.000 0.988 0.000 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.0324 0.9678 0.004 0.992 0.004 0.000 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 3 0.2930 0.7420 0.004 0.000 0.832 0.164 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0000 0.9675 0.000 1.000 0.000 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 4 0.2488 0.7959 0.124 0.000 0.004 0.872 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 2 0.3070 0.8840 0.008 0.872 0.028 0.004 0.088
#> 117673A3-2918-4702-8583-B66ADE6E4338 1 0.1281 0.8605 0.956 0.032 0.012 0.000 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 3 0.4250 0.6825 0.028 0.252 0.720 0.000 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 1 0.0671 0.8698 0.980 0.000 0.004 0.016 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 2 0.0727 0.9614 0.004 0.980 0.012 0.004 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.0324 0.9678 0.004 0.992 0.004 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 4 0.0290 0.8521 0.008 0.000 0.000 0.992 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0162 0.9681 0.000 0.996 0.004 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 4 0.0566 0.8490 0.004 0.000 0.012 0.984 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 4 0.3193 0.7849 0.112 0.004 0.032 0.852 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.2672 0.8155 0.008 0.116 0.872 0.004 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.1329 0.8575 0.008 0.032 0.956 0.004 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 1 0.4287 0.0859 0.540 0.000 0.460 0.000 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 4 0.4182 0.3133 0.000 0.000 0.400 0.600 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 2 0.0566 0.9636 0.004 0.984 0.012 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 5 0.0162 0.9519 0.000 0.004 0.000 0.000 0.996
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 3 0.4171 0.3439 0.396 0.000 0.604 0.000 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 3 0.1285 0.8528 0.004 0.004 0.956 0.000 0.036
#> 1CC36859-357A-49E0-A367-4F57D47288BA 2 0.2437 0.9103 0.032 0.904 0.060 0.004 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0000 0.9675 0.000 1.000 0.000 0.000 0.000
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 3 0.0775 0.8621 0.004 0.008 0.980 0.004 0.004
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0162 0.9681 0.000 0.996 0.004 0.000 0.000
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 5 0.1082 0.9390 0.000 0.000 0.028 0.008 0.964
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 5 0.2921 0.8286 0.148 0.000 0.004 0.004 0.844
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 3 0.0865 0.8571 0.024 0.000 0.972 0.000 0.004
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0162 0.9681 0.000 0.996 0.004 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.0854 0.8614 0.008 0.012 0.976 0.004 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 3 0.0566 0.8627 0.004 0.012 0.984 0.000 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 5 0.0000 0.9515 0.000 0.000 0.000 0.000 1.000
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 3 0.3661 0.6213 0.276 0.000 0.724 0.000 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0000 0.9675 0.000 1.000 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 5 0.1121 0.9276 0.000 0.044 0.000 0.000 0.956
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 4 0.0703 0.8524 0.024 0.000 0.000 0.976 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 3 0.0854 0.8573 0.008 0.000 0.976 0.012 0.004
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.3544 0.7437 0.008 0.200 0.788 0.004 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.0613 0.8611 0.008 0.004 0.984 0.004 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.5012 0.4784 0.032 0.364 0.600 0.004 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0324 0.9678 0.004 0.992 0.004 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 2 0.1812 0.9406 0.008 0.940 0.012 0.004 0.036
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.0960 0.8620 0.016 0.008 0.972 0.004 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 3 0.0798 0.8599 0.016 0.000 0.976 0.008 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> E6088A41-B0DC-4FBF-8D14-BE78024CF8CD 6 0.2996 0.743 0.000 0.000 0.228 0.000 0.000 0.772
#> F569915C-8F77-4D67-9730-30824DB57EE5 3 0.4555 0.788 0.000 0.260 0.680 0.044 0.016 0.000
#> E3B71CB7-673B-4741-8607-4F0A11633036 5 0.3946 0.746 0.076 0.000 0.168 0.000 0.756 0.000
#> DAF84798-FE3F-403C-B589-7F256AF752BE 5 0.1320 0.853 0.000 0.000 0.036 0.016 0.948 0.000
#> 2F38E3B1-4975-4877-9DCC-C00270602180 5 0.1405 0.857 0.024 0.000 0.024 0.004 0.948 0.000
#> C79A4C2F-02C4-4C03-A5A5-DE06802EEB57 3 0.4315 0.730 0.036 0.328 0.636 0.000 0.000 0.000
#> 92E3ED8F-5C74-4ED2-9B03-9FA5E7B491D6 1 0.4361 0.663 0.720 0.000 0.168 0.000 0.112 0.000
#> E61D60BE-3BD3-4B5E-BC12-80F3684A0959 4 0.4033 0.605 0.164 0.012 0.032 0.776 0.016 0.000
#> C9388FC7-DB1D-4416-BC2D-EA643584F1E6 4 0.4469 0.590 0.000 0.124 0.012 0.736 0.000 0.128
#> D4219360-6344-4AF3-ACEB-1701A9F1F67D 5 0.2605 0.836 0.028 0.000 0.108 0.000 0.864 0.000
#> 07D155E6-F27C-4D09-B786-8A9B71147B72 4 0.2636 0.699 0.000 0.004 0.016 0.860 0.120 0.000
#> 10C8C361-85A5-40E8-A395-B92623E6F27C 1 0.3580 0.727 0.772 0.000 0.196 0.004 0.028 0.000
#> BDF20891-7C98-4A55-BBF3-8A836BE303C6 2 0.0405 0.919 0.004 0.988 0.008 0.000 0.000 0.000
#> 694B0504-095E-431A-84F2-B4387072138E 1 0.3150 0.747 0.832 0.000 0.104 0.000 0.064 0.000
#> EDCF8E7F-8B1D-46F7-8AE0-84A1DC9647D2 5 0.1949 0.839 0.000 0.020 0.020 0.036 0.924 0.000
#> 0EA8288E-824D-4304-A053-5A833361F5C5 6 0.4918 0.635 0.000 0.000 0.124 0.232 0.000 0.644
#> C4ACCFA1-34A9-4F61-8A1F-35B3B60EA193 5 0.4230 0.458 0.324 0.000 0.024 0.004 0.648 0.000
#> 760BA639-38AC-4BC9-9647-09F6893EA8ED 5 0.2445 0.838 0.020 0.000 0.108 0.000 0.872 0.000
#> 082DFC6B-C6E6-48B9-BDE5-74FF3B3DC954 3 0.3897 0.774 0.000 0.300 0.684 0.008 0.000 0.008
#> FE349848-D7C4-4C49-B670-0E20454DDD7E 3 0.4876 0.344 0.000 0.000 0.564 0.368 0.068 0.000
#> 2E8937B3-9EA1-4528-8CEE-BF3D4137908A 2 0.0260 0.920 0.000 0.992 0.000 0.008 0.000 0.000
#> 9942D04E-2767-4E39-BA16-7762EAC3DFC4 6 0.0000 0.790 0.000 0.000 0.000 0.000 0.000 1.000
#> F9C23182-91C4-4145-AE52-526FE8EB199D 2 0.0984 0.915 0.008 0.968 0.012 0.000 0.012 0.000
#> 01024EED-7811-4E24-A067-8E0B978FFE2D 2 0.2133 0.870 0.000 0.912 0.016 0.052 0.020 0.000
#> 932C8E98-A352-409C-8744-3D49FABCE425 5 0.2554 0.800 0.004 0.000 0.028 0.092 0.876 0.000
#> 6DFD9439-C659-4936-84E5-108F717E3E4D 5 0.2633 0.818 0.000 0.044 0.028 0.040 0.888 0.000
#> 1D8B83B8-5DD7-4F45-9D59-487EC6906A8B 6 0.4864 0.328 0.000 0.064 0.384 0.000 0.000 0.552
#> 804B8503-73F6-42DE-835F-39DE2C9F13E1 2 0.0458 0.914 0.000 0.984 0.016 0.000 0.000 0.000
#> B9453C9B-84D5-43BD-85BB-1780F02F039C 1 0.0405 0.794 0.988 0.000 0.008 0.004 0.000 0.000
#> 836E6CD6-4E88-4028-9844-FA3C892C254A 6 0.2631 0.752 0.000 0.000 0.180 0.000 0.000 0.820
#> 36EDD202-A845-4CE7-95D5-A515C471262E 2 0.0748 0.918 0.000 0.976 0.004 0.004 0.016 0.000
#> 181C156A-B8F4-4F9D-93AA-6AFBD197644D 2 0.3967 0.422 0.356 0.632 0.012 0.000 0.000 0.000
#> 0580E798-6A98-4E46-AC96-9A869DFF502E 1 0.4424 0.169 0.532 0.004 0.012 0.448 0.004 0.000
#> EA1B63CF-D389-4567-8D9E-B0EF1F804598 5 0.2668 0.805 0.004 0.000 0.168 0.000 0.828 0.000
#> 5ABC3D06-B8E9-4831-B89B-7C1EC4006B47 6 0.0000 0.790 0.000 0.000 0.000 0.000 0.000 1.000
#> 1CC01E74-A7C1-4A51-9E28-1703C352BE16 2 0.3266 0.610 0.000 0.728 0.000 0.000 0.000 0.272
#> 97E55B75-DBDB-46BC-9E74-7828C070BF16 2 0.1863 0.843 0.000 0.896 0.000 0.000 0.000 0.104
#> BEB37A2D-E8B0-4674-89AC-DC0F34E0AFD6 5 0.2151 0.827 0.008 0.000 0.016 0.072 0.904 0.000
#> 558648BA-6551-4D34-97C2-AD5F677D63FB 1 0.0291 0.795 0.992 0.000 0.004 0.004 0.000 0.000
#> FF7021BB-414B-4466-BC43-372D856E6A1D 6 0.3960 0.640 0.000 0.000 0.032 0.224 0.008 0.736
#> 9C04B840-54E0-425D-BDBE-8CB6B9C2C722 2 0.0603 0.914 0.000 0.980 0.016 0.000 0.004 0.000
#> 5F50B1F4-1A02-4506-AA6D-9BF21CD8059A 4 0.1297 0.703 0.000 0.000 0.040 0.948 0.012 0.000
#> 7D4B8FF3-7977-4ABD-AE04-EED1C50B23CC 2 0.2434 0.861 0.000 0.896 0.056 0.016 0.032 0.000
#> A005AF15-B98E-4623-8AD4-1E69EFA9BC7A 4 0.3628 0.462 0.268 0.004 0.008 0.720 0.000 0.000
#> 1DF04345-5C93-4B18-B307-8D1FCA532999 2 0.0405 0.921 0.000 0.988 0.008 0.004 0.000 0.000
#> 6AF30C18-347E-41ED-A4AB-6F81F42206C5 1 0.4427 0.563 0.684 0.000 0.012 0.040 0.264 0.000
#> 88A44ED0-BDE1-401E-B0E1-D8C8D6DBAEFC 3 0.3888 0.765 0.000 0.312 0.672 0.000 0.000 0.016
#> 117673A3-2918-4702-8583-B66ADE6E4338 5 0.2611 0.811 0.000 0.080 0.028 0.012 0.880 0.000
#> 4903A571-E14D-43C9-A737-22CAFAC414CD 4 0.6995 0.526 0.008 0.144 0.148 0.516 0.184 0.000
#> C175E68E-632A-4B4A-9DDB-4895C4760F20 5 0.1141 0.855 0.000 0.000 0.052 0.000 0.948 0.000
#> FA5CA2F4-A1AD-4A79-B8F3-73A976EB2FB1 3 0.3737 0.683 0.000 0.392 0.608 0.000 0.000 0.000
#> E1F883A7-8B1F-4C1D-8A1A-9749A9C09845 2 0.1168 0.906 0.000 0.956 0.016 0.028 0.000 0.000
#> E74F0729-6000-4908-ADB0-7BDBAC0639E6 1 0.1257 0.791 0.952 0.000 0.028 0.020 0.000 0.000
#> F92135A9-1981-4C79-99A5-4243EEC5D30D 2 0.0291 0.920 0.000 0.992 0.004 0.004 0.000 0.000
#> 73C6919F-DEB4-4DA4-B4AE-4032AC8F96C5 1 0.2259 0.780 0.904 0.000 0.044 0.044 0.008 0.000
#> 33977B40-2E92-48B6-8D3A-3EBE913F6F8A 3 0.2868 0.514 0.132 0.000 0.840 0.000 0.028 0.000
#> 490BD607-2F08-4B4C-9612-F9BB5CBBC8BB 3 0.4455 0.781 0.000 0.232 0.688 0.080 0.000 0.000
#> 6C666E17-2EB8-4244-BCC1-8DD34EE4173E 3 0.4118 0.567 0.000 0.028 0.660 0.312 0.000 0.000
#> C41F3064-4483-4796-B860-82155BAA5157 4 0.3864 0.267 0.000 0.000 0.000 0.520 0.480 0.000
#> F4232B90-51B9-43EE-9971-35B3A318758F 1 0.3887 0.402 0.632 0.000 0.008 0.360 0.000 0.000
#> 291EA1F6-FC56-4429-A433-0C452A6A514C 3 0.3868 0.483 0.000 0.492 0.508 0.000 0.000 0.000
#> CC4AF04D-CB8E-40DD-B12E-7AA39B38262A 6 0.0458 0.789 0.000 0.016 0.000 0.000 0.000 0.984
#> 4DAE26B9-AB57-4763-BB9A-2ADDD5D9C007 4 0.5067 0.223 0.000 0.024 0.032 0.488 0.456 0.000
#> 676B4AEE-FB41-4C16-AA91-03E4A1DA8E26 4 0.1820 0.688 0.000 0.008 0.012 0.924 0.000 0.056
#> 1CC36859-357A-49E0-A367-4F57D47288BA 3 0.4185 0.751 0.000 0.332 0.644 0.004 0.020 0.000
#> 7CCDDFC8-6DF6-4734-96AC-19EBC464FC50 2 0.0603 0.915 0.000 0.980 0.016 0.000 0.000 0.004
#> 10F38C34-3956-48ED-AB62-3439EF00D46B 4 0.3126 0.488 0.000 0.000 0.248 0.752 0.000 0.000
#> 80B2B393-9D6E-45B4-B696-2A5C203543B7 2 0.0862 0.916 0.000 0.972 0.008 0.016 0.000 0.004
#> 5AC7B5EA-1AAC-4529-8E5A-F612E5E0719A 6 0.2094 0.772 0.000 0.000 0.020 0.080 0.000 0.900
#> 3648E98D-4B3A-41B7-BE05-595D44F0150D 6 0.5567 0.599 0.028 0.000 0.240 0.000 0.120 0.612
#> 252EC850-6026-473B-8FFC-2CF567EF42E4 4 0.1950 0.708 0.000 0.000 0.024 0.912 0.064 0.000
#> 1790E7A3-99D8-447E-BC50-51C8A4A18853 2 0.0146 0.919 0.000 0.996 0.004 0.000 0.000 0.000
#> DA61A4F8-350B-4DD1-A240-AD016520DB5B 3 0.4500 0.668 0.000 0.088 0.688 0.224 0.000 0.000
#> 767E5BAF-2515-40D7-AE7D-0B24D77A03F4 4 0.3975 0.134 0.000 0.000 0.392 0.600 0.008 0.000
#> 8CB9C903-D749-44EA-9137-7FB5A92C5932 6 0.1204 0.788 0.000 0.000 0.056 0.000 0.000 0.944
#> A9B968AE-2243-41BC-852A-3A12C1FB4892 4 0.4302 0.455 0.000 0.004 0.020 0.608 0.368 0.000
#> 3C582264-85BC-4569-A888-8EB3008E5198 2 0.0146 0.919 0.000 0.996 0.004 0.000 0.000 0.000
#> F4EAF9A2-9938-4D9B-9080-C0A7542D5704 6 0.3175 0.583 0.000 0.256 0.000 0.000 0.000 0.744
#> 328499D3-45DD-4B66-BD22-3C52BA92C2BB 1 0.0748 0.795 0.976 0.000 0.004 0.004 0.016 0.000
#> AF8AB83D-2917-4752-8C38-CF84C565B565 4 0.1297 0.691 0.012 0.000 0.040 0.948 0.000 0.000
#> 03A327CA-A9FE-42A4-844D-CA85206969FC 3 0.4588 0.785 0.000 0.248 0.676 0.072 0.004 0.000
#> 4B0BEC44-87BB-4014-9A26-3529DD63760B 3 0.4252 0.513 0.000 0.016 0.632 0.344 0.008 0.000
#> 05C7843E-DC89-4D8E-A78A-952C5DFD667B 3 0.4337 0.787 0.000 0.272 0.684 0.032 0.012 0.000
#> 29A0FFF9-13D3-4D16-BE6A-8E48A2C4E315 2 0.0622 0.918 0.000 0.980 0.008 0.012 0.000 0.000
#> 22947CC3-4F42-4F44-899C-2B7085E36C9D 3 0.3608 0.760 0.000 0.272 0.716 0.000 0.000 0.012
#> A247D92D-253A-4BEC-B450-184AF90D17D0 3 0.4544 0.614 0.000 0.056 0.660 0.280 0.004 0.000
#> 0D907A63-D23F-4684-80D9-0BA552435B86 4 0.2339 0.707 0.000 0.012 0.020 0.896 0.072 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.
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