Date: 2019-12-25 22:34:39 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 14049 rows and 148 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] 14049 148
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:pam | 6 | 1.000 | 0.974 | 0.983 | ** | 2,3,5 |
| SD:NMF | 5 | 1.000 | 0.975 | 0.988 | ** | 2,4 |
| CV:mclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:pam | 6 | 1.000 | 1.000 | 1.000 | ** | 2,3,5 |
| ATC:hclust | 6 | 1.000 | 0.988 | 0.990 | ** | 3,4 |
| ATC:pam | 6 | 1.000 | 0.989 | 0.994 | ** | 2,5 |
| ATC:mclust | 6 | 1.000 | 1.000 | 1.000 | ** | 2,5 |
| ATC:NMF | 3 | 1.000 | 0.965 | 0.981 | ** | 2 |
| CV:NMF | 6 | 0.964 | 0.942 | 0.947 | ** | 2,3,5 |
| SD:mclust | 6 | 0.961 | 0.975 | 0.954 | ** | 2,4,5 |
| MAD:mclust | 6 | 0.956 | 0.969 | 0.952 | ** | 2,5 |
| ATC:skmeans | 4 | 0.950 | 0.960 | 0.968 | ** | 2,3 |
| CV:pam | 6 | 0.940 | 0.916 | 0.933 | * | 2,3,4,5 |
| CV:hclust | 6 | 0.938 | 0.958 | 0.969 | * | |
| CV:skmeans | 6 | 0.934 | 0.867 | 0.882 | * | 5 |
| MAD:NMF | 6 | 0.927 | 0.830 | 0.899 | * | 2,4,5 |
| SD:skmeans | 6 | 0.911 | 0.926 | 0.862 | * | 5 |
| MAD:skmeans | 6 | 0.901 | 0.902 | 0.869 | * | |
| MAD:hclust | 5 | 0.841 | 0.888 | 0.903 | ||
| SD:hclust | 3 | 0.623 | 0.858 | 0.899 | ||
| ATC:kmeans | 3 | 0.610 | 0.897 | 0.874 | ||
| CV:kmeans | 4 | 0.535 | 0.716 | 0.691 | ||
| SD:kmeans | 4 | 0.475 | 0.717 | 0.745 | ||
| MAD:kmeans | 4 | 0.444 | 0.688 | 0.731 |
**: 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 1.000 1.000 1.000 0.326 0.675 0.675
#> CV:NMF 2 1.000 1.000 1.000 0.326 0.675 0.675
#> MAD:NMF 2 1.000 1.000 1.000 0.326 0.675 0.675
#> ATC:NMF 2 1.000 1.000 1.000 0.326 0.675 0.675
#> SD:skmeans 2 0.799 0.941 0.959 0.468 0.520 0.520
#> CV:skmeans 2 0.535 0.705 0.844 0.475 0.520 0.520
#> MAD:skmeans 2 0.640 0.738 0.900 0.490 0.502 0.502
#> ATC:skmeans 2 0.904 0.932 0.964 0.498 0.501 0.501
#> SD:mclust 2 1.000 1.000 1.000 0.326 0.675 0.675
#> CV:mclust 2 1.000 1.000 1.000 0.326 0.675 0.675
#> MAD:mclust 2 1.000 1.000 1.000 0.326 0.675 0.675
#> ATC:mclust 2 1.000 1.000 1.000 0.326 0.675 0.675
#> SD:kmeans 2 0.198 0.790 0.824 0.366 0.675 0.675
#> CV:kmeans 2 0.153 0.710 0.757 0.381 0.675 0.675
#> MAD:kmeans 2 0.175 0.710 0.793 0.383 0.675 0.675
#> ATC:kmeans 2 0.300 0.701 0.773 0.365 0.579 0.579
#> SD:pam 2 1.000 1.000 1.000 0.326 0.675 0.675
#> CV:pam 2 1.000 1.000 1.000 0.326 0.675 0.675
#> MAD:pam 2 1.000 1.000 1.000 0.326 0.675 0.675
#> ATC:pam 2 1.000 1.000 1.000 0.326 0.675 0.675
#> SD:hclust 2 0.579 0.755 0.869 0.246 0.828 0.828
#> CV:hclust 2 0.198 0.748 0.784 0.384 0.520 0.520
#> MAD:hclust 2 0.259 0.815 0.791 0.389 0.515 0.515
#> ATC:hclust 2 0.579 0.783 0.881 0.234 0.828 0.828
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.716 0.824 0.894 0.891 0.686 0.534
#> CV:NMF 3 1.000 0.962 0.984 0.778 0.757 0.640
#> MAD:NMF 3 0.762 0.923 0.947 0.943 0.686 0.534
#> ATC:NMF 3 1.000 0.965 0.981 0.869 0.710 0.570
#> SD:skmeans 3 0.672 0.801 0.874 0.368 0.647 0.428
#> CV:skmeans 3 0.724 0.869 0.923 0.316 0.609 0.403
#> MAD:skmeans 3 0.701 0.867 0.904 0.313 0.759 0.563
#> ATC:skmeans 3 1.000 1.000 1.000 0.286 0.859 0.719
#> SD:mclust 3 0.768 0.926 0.955 0.799 0.757 0.640
#> CV:mclust 3 0.616 0.782 0.856 0.862 0.757 0.640
#> MAD:mclust 3 0.809 0.844 0.908 0.836 0.757 0.640
#> ATC:mclust 3 0.585 0.885 0.870 0.830 0.585 0.418
#> SD:kmeans 3 0.401 0.490 0.712 0.569 0.680 0.526
#> CV:kmeans 3 0.243 0.675 0.756 0.510 0.724 0.592
#> MAD:kmeans 3 0.312 0.635 0.695 0.498 0.686 0.534
#> ATC:kmeans 3 0.610 0.897 0.874 0.585 0.686 0.499
#> SD:pam 3 1.000 0.985 0.993 0.759 0.757 0.640
#> CV:pam 3 1.000 0.971 0.987 0.771 0.757 0.640
#> MAD:pam 3 1.000 0.978 0.990 0.765 0.757 0.640
#> ATC:pam 3 0.835 0.842 0.941 0.964 0.681 0.527
#> SD:hclust 3 0.623 0.858 0.899 1.084 0.713 0.653
#> CV:hclust 3 0.569 0.775 0.846 0.486 0.923 0.852
#> MAD:hclust 3 0.419 0.887 0.836 0.548 0.840 0.689
#> ATC:hclust 3 1.000 1.000 1.000 0.962 0.713 0.653
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 1.000 0.957 0.974 0.1708 0.724 0.397
#> CV:NMF 4 0.769 0.833 0.873 0.2560 0.846 0.642
#> MAD:NMF 4 0.974 0.926 0.965 0.1393 0.742 0.426
#> ATC:NMF 4 0.865 0.825 0.929 0.1765 0.796 0.526
#> SD:skmeans 4 0.745 0.843 0.893 0.1413 0.724 0.397
#> CV:skmeans 4 0.759 0.672 0.710 0.1720 0.807 0.554
#> MAD:skmeans 4 0.724 0.659 0.804 0.1407 0.726 0.383
#> ATC:skmeans 4 0.950 0.960 0.968 0.0838 0.950 0.862
#> SD:mclust 4 1.000 0.998 0.999 0.2324 0.846 0.642
#> CV:mclust 4 0.741 0.825 0.877 0.1953 0.840 0.630
#> MAD:mclust 4 0.763 0.695 0.841 0.2109 0.822 0.588
#> ATC:mclust 4 0.765 0.903 0.923 0.1880 0.905 0.731
#> SD:kmeans 4 0.475 0.717 0.745 0.1813 0.739 0.416
#> CV:kmeans 4 0.535 0.716 0.691 0.2111 0.807 0.554
#> MAD:kmeans 4 0.444 0.688 0.731 0.1976 0.724 0.397
#> ATC:kmeans 4 0.609 0.804 0.815 0.1539 0.890 0.714
#> SD:pam 4 0.839 0.910 0.918 0.2589 0.840 0.630
#> CV:pam 4 0.962 0.960 0.980 0.2710 0.840 0.630
#> MAD:pam 4 0.735 0.678 0.869 0.2602 0.822 0.588
#> ATC:pam 4 0.876 0.893 0.956 0.1018 0.852 0.629
#> SD:hclust 4 0.739 0.728 0.868 0.3705 0.796 0.623
#> CV:hclust 4 0.831 0.859 0.910 0.2609 0.835 0.626
#> MAD:hclust 4 0.765 0.853 0.864 0.1776 0.943 0.840
#> ATC:hclust 4 1.000 1.000 1.000 0.5273 0.757 0.551
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 1.000 0.975 0.988 0.1141 0.917 0.702
#> CV:NMF 5 1.000 1.000 1.000 0.1061 0.917 0.702
#> MAD:NMF 5 0.932 0.898 0.951 0.1101 0.902 0.659
#> ATC:NMF 5 0.872 0.886 0.928 0.0802 0.866 0.573
#> SD:skmeans 5 0.904 0.959 0.962 0.1004 0.917 0.702
#> CV:skmeans 5 0.977 0.965 0.965 0.0953 0.923 0.716
#> MAD:skmeans 5 0.865 0.864 0.862 0.0941 0.899 0.634
#> ATC:skmeans 5 0.854 0.898 0.855 0.1006 0.898 0.672
#> SD:mclust 5 1.000 0.990 0.990 0.1136 0.917 0.702
#> CV:mclust 5 0.811 0.774 0.808 0.0945 0.941 0.782
#> MAD:mclust 5 1.000 0.990 0.993 0.1100 0.899 0.634
#> ATC:mclust 5 0.905 0.960 0.963 0.1247 0.917 0.702
#> SD:kmeans 5 0.626 0.666 0.685 0.1081 1.000 1.000
#> CV:kmeans 5 0.659 0.786 0.720 0.0718 0.929 0.737
#> MAD:kmeans 5 0.621 0.665 0.713 0.0917 1.000 1.000
#> ATC:kmeans 5 0.632 0.797 0.739 0.1069 0.874 0.603
#> SD:pam 5 0.963 0.928 0.968 0.1040 0.857 0.536
#> CV:pam 5 1.000 0.973 0.985 0.0898 0.929 0.737
#> MAD:pam 5 1.000 0.987 0.992 0.0996 0.855 0.518
#> ATC:pam 5 1.000 0.983 0.993 0.1177 0.854 0.551
#> SD:hclust 5 0.816 0.814 0.860 0.1091 0.879 0.640
#> CV:hclust 5 0.899 0.957 0.877 0.0861 0.917 0.702
#> MAD:hclust 5 0.841 0.888 0.903 0.1103 0.917 0.722
#> ATC:hclust 5 0.879 0.889 0.863 0.0825 0.961 0.870
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.978 0.905 0.902 0.0220 1.000 1.000
#> CV:NMF 6 0.964 0.942 0.947 0.0199 0.986 0.931
#> MAD:NMF 6 0.927 0.830 0.899 0.0285 0.972 0.861
#> ATC:NMF 6 0.876 0.788 0.868 0.0510 0.944 0.757
#> SD:skmeans 6 0.911 0.926 0.862 0.0262 0.979 0.894
#> CV:skmeans 6 0.934 0.867 0.882 0.0274 0.986 0.931
#> MAD:skmeans 6 0.901 0.902 0.869 0.0274 0.979 0.894
#> ATC:skmeans 6 0.818 0.913 0.873 0.0545 0.981 0.908
#> SD:mclust 6 0.961 0.975 0.954 0.0232 0.982 0.907
#> CV:mclust 6 0.802 0.783 0.777 0.0258 0.958 0.800
#> MAD:mclust 6 0.956 0.969 0.952 0.0231 0.982 0.907
#> ATC:mclust 6 1.000 1.000 1.000 0.0325 0.982 0.907
#> SD:kmeans 6 0.682 0.595 0.640 0.0522 0.895 0.659
#> CV:kmeans 6 0.702 0.746 0.745 0.0623 1.000 1.000
#> MAD:kmeans 6 0.718 0.688 0.710 0.0606 0.917 0.702
#> ATC:kmeans 6 0.665 0.768 0.743 0.0566 0.957 0.816
#> SD:pam 6 1.000 0.974 0.983 0.0411 0.959 0.800
#> CV:pam 6 0.940 0.916 0.933 0.0344 0.970 0.849
#> MAD:pam 6 1.000 1.000 1.000 0.0403 0.959 0.800
#> ATC:pam 6 1.000 0.989 0.994 0.0403 0.970 0.856
#> SD:hclust 6 0.899 0.782 0.868 0.0532 0.961 0.821
#> CV:hclust 6 0.938 0.958 0.969 0.0451 0.982 0.907
#> MAD:hclust 6 0.899 0.904 0.926 0.0438 0.961 0.821
#> ATC:hclust 6 1.000 0.988 0.990 0.0838 0.917 0.681
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 14049 rows and 148 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 0.579 0.755 0.869 0.2464 0.828 0.828
#> 3 3 0.623 0.858 0.899 1.0844 0.713 0.653
#> 4 4 0.739 0.728 0.868 0.3705 0.796 0.623
#> 5 5 0.816 0.814 0.860 0.1091 0.879 0.640
#> 6 6 0.899 0.782 0.868 0.0532 0.961 0.821
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
#> ERR978107 2 0.000 0.821 0.00 1.00
#> ERR978108 2 0.000 0.821 0.00 1.00
#> ERR978109 2 0.000 0.821 0.00 1.00
#> ERR978110 2 0.000 0.821 0.00 1.00
#> ERR978111 2 0.000 0.821 0.00 1.00
#> ERR978112 2 0.000 0.821 0.00 1.00
#> ERR978113 2 0.000 0.821 0.00 1.00
#> ERR978114 2 0.000 0.821 0.00 1.00
#> ERR978115 2 0.000 0.821 0.00 1.00
#> ERR978116 2 0.000 0.821 0.00 1.00
#> ERR978117 2 0.000 0.821 0.00 1.00
#> ERR978118 2 0.000 0.821 0.00 1.00
#> ERR978119 2 0.000 0.821 0.00 1.00
#> ERR978120 2 0.000 0.821 0.00 1.00
#> ERR978121 2 0.000 0.821 0.00 1.00
#> ERR978122 2 0.000 0.821 0.00 1.00
#> ERR978123 2 0.242 0.803 0.04 0.96
#> ERR978124 2 0.242 0.803 0.04 0.96
#> ERR978125 2 0.242 0.803 0.04 0.96
#> ERR978126 2 0.242 0.803 0.04 0.96
#> ERR978127 2 0.242 0.803 0.04 0.96
#> ERR978128 2 0.242 0.803 0.04 0.96
#> ERR978129 2 0.242 0.803 0.04 0.96
#> ERR978130 2 0.242 0.803 0.04 0.96
#> ERR978131 2 0.242 0.803 0.04 0.96
#> ERR978132 2 0.242 0.803 0.04 0.96
#> ERR978133 2 0.242 0.803 0.04 0.96
#> ERR978134 2 0.242 0.803 0.04 0.96
#> ERR978135 2 0.242 0.803 0.04 0.96
#> ERR978136 2 0.242 0.803 0.04 0.96
#> ERR978137 2 0.242 0.803 0.04 0.96
#> ERR978138 2 0.242 0.803 0.04 0.96
#> ERR978139 2 0.242 0.803 0.04 0.96
#> ERR978140 2 0.242 0.803 0.04 0.96
#> ERR978141 2 0.242 0.803 0.04 0.96
#> ERR978142 2 0.242 0.803 0.04 0.96
#> ERR978143 2 0.242 0.803 0.04 0.96
#> ERR978144 2 0.242 0.803 0.04 0.96
#> ERR978145 2 0.242 0.803 0.04 0.96
#> ERR978146 2 0.242 0.803 0.04 0.96
#> ERR978147 2 0.242 0.803 0.04 0.96
#> ERR978148 2 0.242 0.803 0.04 0.96
#> ERR978149 2 0.242 0.803 0.04 0.96
#> ERR978150 2 0.242 0.803 0.04 0.96
#> ERR978151 2 0.242 0.803 0.04 0.96
#> ERR978152 2 0.242 0.803 0.04 0.96
#> ERR978153 2 0.971 0.440 0.40 0.60
#> ERR978154 2 0.971 0.440 0.40 0.60
#> ERR978155 2 0.971 0.440 0.40 0.60
#> ERR978156 2 0.971 0.440 0.40 0.60
#> ERR978157 2 0.971 0.440 0.40 0.60
#> ERR978158 2 0.971 0.440 0.40 0.60
#> ERR978159 2 0.971 0.440 0.40 0.60
#> ERR978160 2 0.971 0.440 0.40 0.60
#> ERR978161 2 0.971 0.440 0.40 0.60
#> ERR978162 2 0.971 0.440 0.40 0.60
#> ERR978163 2 0.971 0.440 0.40 0.60
#> ERR978164 2 0.971 0.440 0.40 0.60
#> ERR978165 2 0.971 0.440 0.40 0.60
#> ERR978166 2 0.971 0.440 0.40 0.60
#> ERR978167 2 0.971 0.440 0.40 0.60
#> ERR978168 2 0.971 0.440 0.40 0.60
#> ERR978169 1 0.971 1.000 0.60 0.40
#> ERR978170 1 0.971 1.000 0.60 0.40
#> ERR978171 1 0.971 1.000 0.60 0.40
#> ERR978172 1 0.971 1.000 0.60 0.40
#> ERR978173 1 0.971 1.000 0.60 0.40
#> ERR978174 1 0.971 1.000 0.60 0.40
#> ERR978175 1 0.971 1.000 0.60 0.40
#> ERR978176 1 0.971 1.000 0.60 0.40
#> ERR978177 1 0.971 1.000 0.60 0.40
#> ERR978178 1 0.971 1.000 0.60 0.40
#> ERR978179 1 0.971 1.000 0.60 0.40
#> ERR978180 1 0.971 1.000 0.60 0.40
#> ERR978181 1 0.971 1.000 0.60 0.40
#> ERR978182 1 0.971 1.000 0.60 0.40
#> ERR978183 2 0.000 0.821 0.00 1.00
#> ERR978184 2 0.000 0.821 0.00 1.00
#> ERR978185 2 0.000 0.821 0.00 1.00
#> ERR978186 2 0.000 0.821 0.00 1.00
#> ERR978187 2 0.000 0.821 0.00 1.00
#> ERR978188 2 0.000 0.821 0.00 1.00
#> ERR978189 2 0.000 0.821 0.00 1.00
#> ERR978190 2 0.000 0.821 0.00 1.00
#> ERR978191 2 0.000 0.821 0.00 1.00
#> ERR978192 2 0.000 0.821 0.00 1.00
#> ERR978193 2 0.000 0.821 0.00 1.00
#> ERR978194 2 0.000 0.821 0.00 1.00
#> ERR978195 2 0.000 0.821 0.00 1.00
#> ERR978196 2 0.000 0.821 0.00 1.00
#> ERR978197 2 0.000 0.821 0.00 1.00
#> ERR978198 2 0.000 0.821 0.00 1.00
#> ERR978199 2 0.000 0.821 0.00 1.00
#> ERR978200 2 0.000 0.821 0.00 1.00
#> ERR978201 2 0.000 0.821 0.00 1.00
#> ERR978202 2 0.000 0.821 0.00 1.00
#> ERR978203 2 0.000 0.821 0.00 1.00
#> ERR978204 2 0.000 0.821 0.00 1.00
#> ERR978205 2 0.000 0.821 0.00 1.00
#> ERR978206 2 0.000 0.821 0.00 1.00
#> ERR978207 2 0.000 0.821 0.00 1.00
#> ERR978208 2 0.000 0.821 0.00 1.00
#> ERR978209 2 0.000 0.821 0.00 1.00
#> ERR978210 2 0.000 0.821 0.00 1.00
#> ERR978211 2 0.000 0.821 0.00 1.00
#> ERR978212 2 0.000 0.821 0.00 1.00
#> ERR978213 2 0.000 0.821 0.00 1.00
#> ERR978214 2 0.000 0.821 0.00 1.00
#> ERR978215 2 0.000 0.821 0.00 1.00
#> ERR978216 2 0.000 0.821 0.00 1.00
#> ERR978217 2 0.000 0.821 0.00 1.00
#> ERR978218 2 0.000 0.821 0.00 1.00
#> ERR978219 2 0.000 0.821 0.00 1.00
#> ERR978220 2 0.000 0.821 0.00 1.00
#> ERR978221 2 0.000 0.821 0.00 1.00
#> ERR978222 2 0.000 0.821 0.00 1.00
#> ERR978223 2 0.000 0.821 0.00 1.00
#> ERR978224 2 0.000 0.821 0.00 1.00
#> ERR978225 2 0.000 0.821 0.00 1.00
#> ERR978226 2 0.000 0.821 0.00 1.00
#> ERR978227 2 0.971 0.440 0.40 0.60
#> ERR978228 2 0.971 0.440 0.40 0.60
#> ERR978229 2 0.971 0.440 0.40 0.60
#> ERR978230 2 0.971 0.440 0.40 0.60
#> ERR978231 2 0.971 0.440 0.40 0.60
#> ERR978232 2 0.971 0.440 0.40 0.60
#> ERR978233 2 0.971 0.440 0.40 0.60
#> ERR978234 2 0.971 0.440 0.40 0.60
#> ERR978235 2 0.971 0.440 0.40 0.60
#> ERR978236 2 0.971 0.440 0.40 0.60
#> ERR978237 2 0.971 0.440 0.40 0.60
#> ERR978238 2 0.971 0.440 0.40 0.60
#> ERR978239 2 0.971 0.440 0.40 0.60
#> ERR978240 2 0.971 0.440 0.40 0.60
#> ERR978241 2 0.242 0.803 0.04 0.96
#> ERR978242 2 0.242 0.803 0.04 0.96
#> ERR978243 2 0.242 0.803 0.04 0.96
#> ERR978244 2 0.242 0.803 0.04 0.96
#> ERR978245 2 0.242 0.803 0.04 0.96
#> ERR978246 2 0.242 0.803 0.04 0.96
#> ERR978247 2 0.242 0.803 0.04 0.96
#> ERR978248 2 0.242 0.803 0.04 0.96
#> ERR978249 2 0.242 0.803 0.04 0.96
#> ERR978250 2 0.242 0.803 0.04 0.96
#> ERR978251 2 0.242 0.803 0.04 0.96
#> ERR978252 2 0.242 0.803 0.04 0.96
#> ERR978253 2 0.242 0.803 0.04 0.96
#> ERR978254 2 0.242 0.803 0.04 0.96
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.394 0.798 0.000 0.844 0.156
#> ERR978108 2 0.394 0.798 0.000 0.844 0.156
#> ERR978109 2 0.394 0.798 0.000 0.844 0.156
#> ERR978110 2 0.394 0.798 0.000 0.844 0.156
#> ERR978111 2 0.394 0.798 0.000 0.844 0.156
#> ERR978112 2 0.394 0.798 0.000 0.844 0.156
#> ERR978113 2 0.394 0.798 0.000 0.844 0.156
#> ERR978114 2 0.394 0.798 0.000 0.844 0.156
#> ERR978115 2 0.394 0.798 0.000 0.844 0.156
#> ERR978116 2 0.394 0.798 0.000 0.844 0.156
#> ERR978117 2 0.394 0.798 0.000 0.844 0.156
#> ERR978118 2 0.394 0.798 0.000 0.844 0.156
#> ERR978119 2 0.394 0.798 0.000 0.844 0.156
#> ERR978120 2 0.394 0.798 0.000 0.844 0.156
#> ERR978121 2 0.394 0.798 0.000 0.844 0.156
#> ERR978122 2 0.394 0.798 0.000 0.844 0.156
#> ERR978123 2 0.337 0.839 0.024 0.904 0.072
#> ERR978124 2 0.337 0.839 0.024 0.904 0.072
#> ERR978125 2 0.337 0.839 0.024 0.904 0.072
#> ERR978126 2 0.337 0.839 0.024 0.904 0.072
#> ERR978127 2 0.337 0.839 0.024 0.904 0.072
#> ERR978128 2 0.337 0.839 0.024 0.904 0.072
#> ERR978129 2 0.337 0.839 0.024 0.904 0.072
#> ERR978130 2 0.337 0.839 0.024 0.904 0.072
#> ERR978131 2 0.337 0.839 0.024 0.904 0.072
#> ERR978132 2 0.337 0.839 0.024 0.904 0.072
#> ERR978133 2 0.337 0.839 0.024 0.904 0.072
#> ERR978134 2 0.337 0.839 0.024 0.904 0.072
#> ERR978135 2 0.337 0.839 0.024 0.904 0.072
#> ERR978136 2 0.337 0.839 0.024 0.904 0.072
#> ERR978137 2 0.337 0.839 0.024 0.904 0.072
#> ERR978138 2 0.255 0.851 0.024 0.936 0.040
#> ERR978139 2 0.255 0.851 0.024 0.936 0.040
#> ERR978140 2 0.255 0.851 0.024 0.936 0.040
#> ERR978141 2 0.255 0.851 0.024 0.936 0.040
#> ERR978142 2 0.255 0.851 0.024 0.936 0.040
#> ERR978143 2 0.255 0.851 0.024 0.936 0.040
#> ERR978144 2 0.255 0.851 0.024 0.936 0.040
#> ERR978145 2 0.255 0.851 0.024 0.936 0.040
#> ERR978146 2 0.255 0.851 0.024 0.936 0.040
#> ERR978147 2 0.255 0.851 0.024 0.936 0.040
#> ERR978148 2 0.255 0.851 0.024 0.936 0.040
#> ERR978149 2 0.255 0.851 0.024 0.936 0.040
#> ERR978150 2 0.255 0.851 0.024 0.936 0.040
#> ERR978151 2 0.255 0.851 0.024 0.936 0.040
#> ERR978152 2 0.255 0.851 0.024 0.936 0.040
#> ERR978153 1 0.000 1.000 1.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1.000 0.000 0.000
#> ERR978169 3 0.394 1.000 0.000 0.156 0.844
#> ERR978170 3 0.394 1.000 0.000 0.156 0.844
#> ERR978171 3 0.394 1.000 0.000 0.156 0.844
#> ERR978172 3 0.394 1.000 0.000 0.156 0.844
#> ERR978173 3 0.394 1.000 0.000 0.156 0.844
#> ERR978174 3 0.394 1.000 0.000 0.156 0.844
#> ERR978175 3 0.394 1.000 0.000 0.156 0.844
#> ERR978176 3 0.394 1.000 0.000 0.156 0.844
#> ERR978177 3 0.394 1.000 0.000 0.156 0.844
#> ERR978178 3 0.394 1.000 0.000 0.156 0.844
#> ERR978179 3 0.394 1.000 0.000 0.156 0.844
#> ERR978180 3 0.394 1.000 0.000 0.156 0.844
#> ERR978181 3 0.394 1.000 0.000 0.156 0.844
#> ERR978182 3 0.394 1.000 0.000 0.156 0.844
#> ERR978183 2 0.394 0.798 0.000 0.844 0.156
#> ERR978184 2 0.394 0.798 0.000 0.844 0.156
#> ERR978185 2 0.394 0.798 0.000 0.844 0.156
#> ERR978186 2 0.394 0.798 0.000 0.844 0.156
#> ERR978187 2 0.394 0.798 0.000 0.844 0.156
#> ERR978188 2 0.394 0.798 0.000 0.844 0.156
#> ERR978189 2 0.394 0.798 0.000 0.844 0.156
#> ERR978190 2 0.394 0.798 0.000 0.844 0.156
#> ERR978191 2 0.394 0.798 0.000 0.844 0.156
#> ERR978192 2 0.394 0.798 0.000 0.844 0.156
#> ERR978193 2 0.394 0.798 0.000 0.844 0.156
#> ERR978194 2 0.394 0.798 0.000 0.844 0.156
#> ERR978195 2 0.394 0.798 0.000 0.844 0.156
#> ERR978196 2 0.394 0.798 0.000 0.844 0.156
#> ERR978197 2 0.129 0.857 0.000 0.968 0.032
#> ERR978198 2 0.129 0.857 0.000 0.968 0.032
#> ERR978199 2 0.129 0.857 0.000 0.968 0.032
#> ERR978200 2 0.129 0.857 0.000 0.968 0.032
#> ERR978201 2 0.129 0.857 0.000 0.968 0.032
#> ERR978202 2 0.129 0.857 0.000 0.968 0.032
#> ERR978203 2 0.129 0.857 0.000 0.968 0.032
#> ERR978204 2 0.129 0.857 0.000 0.968 0.032
#> ERR978205 2 0.129 0.857 0.000 0.968 0.032
#> ERR978206 2 0.129 0.857 0.000 0.968 0.032
#> ERR978207 2 0.129 0.857 0.000 0.968 0.032
#> ERR978208 2 0.129 0.857 0.000 0.968 0.032
#> ERR978209 2 0.129 0.857 0.000 0.968 0.032
#> ERR978210 2 0.129 0.857 0.000 0.968 0.032
#> ERR978211 2 0.129 0.857 0.000 0.968 0.032
#> ERR978212 2 0.000 0.858 0.000 1.000 0.000
#> ERR978213 2 0.000 0.858 0.000 1.000 0.000
#> ERR978214 2 0.000 0.858 0.000 1.000 0.000
#> ERR978215 2 0.000 0.858 0.000 1.000 0.000
#> ERR978216 2 0.000 0.858 0.000 1.000 0.000
#> ERR978217 2 0.000 0.858 0.000 1.000 0.000
#> ERR978218 2 0.000 0.858 0.000 1.000 0.000
#> ERR978219 2 0.000 0.858 0.000 1.000 0.000
#> ERR978220 2 0.000 0.858 0.000 1.000 0.000
#> ERR978221 2 0.000 0.858 0.000 1.000 0.000
#> ERR978222 2 0.000 0.858 0.000 1.000 0.000
#> ERR978223 2 0.000 0.858 0.000 1.000 0.000
#> ERR978224 2 0.000 0.858 0.000 1.000 0.000
#> ERR978225 2 0.000 0.858 0.000 1.000 0.000
#> ERR978226 2 0.000 0.858 0.000 1.000 0.000
#> ERR978227 1 0.000 1.000 1.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1.000 0.000 0.000
#> ERR978241 2 0.639 0.572 0.024 0.692 0.284
#> ERR978242 2 0.639 0.572 0.024 0.692 0.284
#> ERR978243 2 0.639 0.572 0.024 0.692 0.284
#> ERR978244 2 0.639 0.572 0.024 0.692 0.284
#> ERR978245 2 0.639 0.572 0.024 0.692 0.284
#> ERR978246 2 0.639 0.572 0.024 0.692 0.284
#> ERR978247 2 0.639 0.572 0.024 0.692 0.284
#> ERR978248 2 0.639 0.572 0.024 0.692 0.284
#> ERR978249 2 0.639 0.572 0.024 0.692 0.284
#> ERR978250 2 0.639 0.572 0.024 0.692 0.284
#> ERR978251 2 0.639 0.572 0.024 0.692 0.284
#> ERR978252 2 0.639 0.572 0.024 0.692 0.284
#> ERR978253 2 0.639 0.572 0.024 0.692 0.284
#> ERR978254 2 0.639 0.572 0.024 0.692 0.284
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978108 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978109 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978110 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978111 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978112 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978113 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978114 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978115 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978116 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978117 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978118 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978119 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978120 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978121 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978122 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978123 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978124 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978125 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978126 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978127 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978128 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978129 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978130 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978131 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978132 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978133 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978134 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978135 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978136 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978137 3 0.000 0.645 0 0.000 1.000 0.0
#> ERR978138 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978139 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978140 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978141 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978142 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978143 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978144 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978145 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978146 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978147 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978148 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978149 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978150 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978151 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978152 3 0.102 0.651 0 0.032 0.968 0.0
#> ERR978153 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978154 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978155 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978156 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978157 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978158 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978159 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978160 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978161 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978162 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978163 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978164 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978165 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978166 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978167 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978168 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978169 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978170 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978171 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978172 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978173 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978174 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978175 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978176 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978177 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978178 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978179 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978180 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978181 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978182 4 0.000 1.000 0 0.000 0.000 1.0
#> ERR978183 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978184 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978185 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978186 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978187 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978188 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978189 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978190 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978191 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978192 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978193 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978194 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978195 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978196 2 0.000 1.000 0 1.000 0.000 0.0
#> ERR978197 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978198 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978199 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978200 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978201 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978202 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978203 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978204 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978205 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978206 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978207 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978208 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978209 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978210 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978211 3 0.492 0.430 0 0.424 0.576 0.0
#> ERR978212 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978213 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978214 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978215 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978216 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978217 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978218 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978219 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978220 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978221 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978222 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978223 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978224 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978225 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978226 3 0.499 0.386 0 0.472 0.528 0.0
#> ERR978227 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978228 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978229 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978230 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978231 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978232 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978233 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978234 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978235 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978236 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978237 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978238 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978239 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978240 1 0.000 1.000 1 0.000 0.000 0.0
#> ERR978241 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978242 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978243 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978244 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978245 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978246 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978247 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978248 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978249 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978250 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978251 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978252 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978253 3 0.485 0.145 0 0.000 0.600 0.4
#> ERR978254 3 0.485 0.145 0 0.000 0.600 0.4
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978123 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978124 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978125 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978126 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978127 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978128 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978129 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978130 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978131 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978132 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978133 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978134 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978135 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978136 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978137 3 0.431 0.608 0 0.000 0.508 0.000 0.492
#> ERR978138 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978139 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978140 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978141 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978142 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978143 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978144 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978145 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978146 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978147 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978148 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978149 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978150 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978151 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978152 3 0.337 0.634 0 0.000 0.768 0.000 0.232
#> ERR978153 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978169 4 0.307 0.902 0 0.000 0.196 0.804 0.000
#> ERR978170 4 0.307 0.902 0 0.000 0.196 0.804 0.000
#> ERR978171 4 0.307 0.902 0 0.000 0.196 0.804 0.000
#> ERR978172 4 0.307 0.902 0 0.000 0.196 0.804 0.000
#> ERR978173 4 0.307 0.902 0 0.000 0.196 0.804 0.000
#> ERR978174 4 0.307 0.902 0 0.000 0.196 0.804 0.000
#> ERR978175 4 0.307 0.902 0 0.000 0.196 0.804 0.000
#> ERR978176 4 0.000 0.902 0 0.000 0.000 1.000 0.000
#> ERR978177 4 0.000 0.902 0 0.000 0.000 1.000 0.000
#> ERR978178 4 0.000 0.902 0 0.000 0.000 1.000 0.000
#> ERR978179 4 0.000 0.902 0 0.000 0.000 1.000 0.000
#> ERR978180 4 0.000 0.902 0 0.000 0.000 1.000 0.000
#> ERR978181 4 0.000 0.902 0 0.000 0.000 1.000 0.000
#> ERR978182 4 0.000 0.902 0 0.000 0.000 1.000 0.000
#> ERR978183 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978197 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978198 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978199 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978200 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978201 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978202 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978203 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978204 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978205 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978206 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978207 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978208 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978209 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978210 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978211 5 0.000 0.765 0 0.000 0.000 0.000 1.000
#> ERR978212 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978213 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978214 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978215 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978216 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978217 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978218 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978219 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978220 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978221 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978222 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978223 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978224 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978225 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978226 5 0.425 0.765 0 0.016 0.296 0.000 0.688
#> ERR978227 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978241 3 0.342 0.443 0 0.000 0.788 0.204 0.008
#> ERR978242 3 0.342 0.443 0 0.000 0.788 0.204 0.008
#> ERR978243 3 0.342 0.443 0 0.000 0.788 0.204 0.008
#> ERR978244 3 0.342 0.443 0 0.000 0.788 0.204 0.008
#> ERR978245 3 0.342 0.443 0 0.000 0.788 0.204 0.008
#> ERR978246 3 0.342 0.443 0 0.000 0.788 0.204 0.008
#> ERR978247 3 0.342 0.443 0 0.000 0.788 0.204 0.008
#> ERR978248 3 0.445 0.443 0 0.000 0.592 0.400 0.008
#> ERR978249 3 0.445 0.443 0 0.000 0.592 0.400 0.008
#> ERR978250 3 0.445 0.443 0 0.000 0.592 0.400 0.008
#> ERR978251 3 0.445 0.443 0 0.000 0.592 0.400 0.008
#> ERR978252 3 0.445 0.443 0 0.000 0.592 0.400 0.008
#> ERR978253 3 0.445 0.443 0 0.000 0.592 0.400 0.008
#> ERR978254 3 0.445 0.443 0 0.000 0.592 0.400 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978108 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978109 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978110 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978111 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978112 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978113 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978114 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978115 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978116 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978117 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978118 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978119 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978120 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978121 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978122 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978123 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978124 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978125 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978126 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978127 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978128 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978129 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978130 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978131 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978132 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978133 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978134 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978135 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978136 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978137 3 0.000 0.630 0 0 1.000 0.0 0.000 0.0
#> ERR978138 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978139 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978140 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978141 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978142 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978143 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978144 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978145 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978146 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978147 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978148 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978149 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978150 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978151 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978152 3 0.382 0.627 0 0 0.564 0.0 0.436 0.0
#> ERR978153 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978154 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978155 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978156 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978157 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978158 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978159 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978160 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978161 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978162 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978163 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978164 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978165 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978166 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978167 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978168 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978169 4 0.000 0.674 0 0 0.000 1.0 0.000 0.0
#> ERR978170 4 0.000 0.674 0 0 0.000 1.0 0.000 0.0
#> ERR978171 4 0.000 0.674 0 0 0.000 1.0 0.000 0.0
#> ERR978172 4 0.000 0.674 0 0 0.000 1.0 0.000 0.0
#> ERR978173 4 0.000 0.674 0 0 0.000 1.0 0.000 0.0
#> ERR978174 4 0.000 0.674 0 0 0.000 1.0 0.000 0.0
#> ERR978175 4 0.000 0.674 0 0 0.000 1.0 0.000 0.0
#> ERR978176 4 0.376 0.674 0 0 0.000 0.6 0.000 0.4
#> ERR978177 4 0.376 0.674 0 0 0.000 0.6 0.000 0.4
#> ERR978178 4 0.376 0.674 0 0 0.000 0.6 0.000 0.4
#> ERR978179 4 0.376 0.674 0 0 0.000 0.6 0.000 0.4
#> ERR978180 4 0.376 0.674 0 0 0.000 0.6 0.000 0.4
#> ERR978181 4 0.376 0.674 0 0 0.000 0.6 0.000 0.4
#> ERR978182 4 0.376 0.674 0 0 0.000 0.6 0.000 0.4
#> ERR978183 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978184 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978185 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978186 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978187 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978188 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978189 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978190 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978191 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978192 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978193 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978194 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978195 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978196 2 0.000 1.000 0 1 0.000 0.0 0.000 0.0
#> ERR978197 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978198 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978199 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978200 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978201 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978202 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978203 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978204 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978205 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978206 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978207 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978208 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978209 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978210 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978211 5 0.387 0.590 0 0 0.492 0.0 0.508 0.0
#> ERR978212 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978213 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978214 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978215 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978216 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978217 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978218 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978219 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978220 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978221 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978222 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978223 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978224 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978225 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978226 5 0.000 0.613 0 0 0.000 0.0 1.000 0.0
#> ERR978227 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978228 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978229 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978230 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978231 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978232 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978233 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978234 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978235 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978236 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978237 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978238 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978239 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978240 1 0.000 1.000 1 0 0.000 0.0 0.000 0.0
#> ERR978241 6 0.376 0.674 0 0 0.000 0.4 0.000 0.6
#> ERR978242 6 0.376 0.674 0 0 0.000 0.4 0.000 0.6
#> ERR978243 6 0.376 0.674 0 0 0.000 0.4 0.000 0.6
#> ERR978244 6 0.376 0.674 0 0 0.000 0.4 0.000 0.6
#> ERR978245 6 0.376 0.674 0 0 0.000 0.4 0.000 0.6
#> ERR978246 6 0.376 0.674 0 0 0.000 0.4 0.000 0.6
#> ERR978247 6 0.376 0.674 0 0 0.000 0.4 0.000 0.6
#> ERR978248 6 0.000 0.674 0 0 0.000 0.0 0.000 1.0
#> ERR978249 6 0.000 0.674 0 0 0.000 0.0 0.000 1.0
#> ERR978250 6 0.000 0.674 0 0 0.000 0.0 0.000 1.0
#> ERR978251 6 0.000 0.674 0 0 0.000 0.0 0.000 1.0
#> ERR978252 6 0.000 0.674 0 0 0.000 0.0 0.000 1.0
#> ERR978253 6 0.000 0.674 0 0 0.000 0.0 0.000 1.0
#> ERR978254 6 0.000 0.674 0 0 0.000 0.0 0.000 1.0
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 14049 rows and 148 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.198 0.790 0.824 0.3663 0.675 0.675
#> 3 3 0.401 0.490 0.712 0.5691 0.680 0.526
#> 4 4 0.475 0.717 0.745 0.1813 0.739 0.416
#> 5 5 0.626 0.666 0.685 0.1081 1.000 1.000
#> 6 6 0.682 0.595 0.640 0.0522 0.895 0.659
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
#> ERR978107 2 0.278 0.732 0.048 0.952
#> ERR978108 2 0.278 0.732 0.048 0.952
#> ERR978109 2 0.278 0.732 0.048 0.952
#> ERR978110 2 0.278 0.732 0.048 0.952
#> ERR978111 2 0.278 0.732 0.048 0.952
#> ERR978112 2 0.278 0.732 0.048 0.952
#> ERR978113 2 0.278 0.732 0.048 0.952
#> ERR978114 2 0.278 0.732 0.048 0.952
#> ERR978115 2 0.278 0.732 0.048 0.952
#> ERR978116 2 0.278 0.732 0.048 0.952
#> ERR978117 2 0.278 0.732 0.048 0.952
#> ERR978118 2 0.278 0.732 0.048 0.952
#> ERR978119 2 0.278 0.732 0.048 0.952
#> ERR978120 2 0.278 0.732 0.048 0.952
#> ERR978121 2 0.278 0.732 0.048 0.952
#> ERR978122 2 0.278 0.732 0.048 0.952
#> ERR978123 2 0.886 0.746 0.304 0.696
#> ERR978124 2 0.886 0.746 0.304 0.696
#> ERR978125 2 0.886 0.746 0.304 0.696
#> ERR978126 2 0.886 0.746 0.304 0.696
#> ERR978127 2 0.886 0.746 0.304 0.696
#> ERR978128 2 0.886 0.746 0.304 0.696
#> ERR978129 2 0.886 0.746 0.304 0.696
#> ERR978130 2 0.886 0.746 0.304 0.696
#> ERR978131 2 0.855 0.755 0.280 0.720
#> ERR978132 2 0.855 0.755 0.280 0.720
#> ERR978133 2 0.855 0.755 0.280 0.720
#> ERR978134 2 0.855 0.755 0.280 0.720
#> ERR978135 2 0.855 0.755 0.280 0.720
#> ERR978136 2 0.855 0.755 0.280 0.720
#> ERR978137 2 0.855 0.755 0.280 0.720
#> ERR978138 2 0.855 0.761 0.280 0.720
#> ERR978139 2 0.855 0.761 0.280 0.720
#> ERR978140 2 0.855 0.761 0.280 0.720
#> ERR978141 2 0.855 0.761 0.280 0.720
#> ERR978142 2 0.855 0.761 0.280 0.720
#> ERR978143 2 0.855 0.761 0.280 0.720
#> ERR978144 2 0.855 0.761 0.280 0.720
#> ERR978145 2 0.855 0.761 0.280 0.720
#> ERR978146 2 0.866 0.755 0.288 0.712
#> ERR978147 2 0.866 0.755 0.288 0.712
#> ERR978148 2 0.866 0.755 0.288 0.712
#> ERR978149 2 0.866 0.755 0.288 0.712
#> ERR978150 2 0.866 0.755 0.288 0.712
#> ERR978151 2 0.866 0.755 0.288 0.712
#> ERR978152 2 0.866 0.755 0.288 0.712
#> ERR978153 1 0.541 1.000 0.876 0.124
#> ERR978154 1 0.541 1.000 0.876 0.124
#> ERR978155 1 0.541 1.000 0.876 0.124
#> ERR978156 1 0.541 1.000 0.876 0.124
#> ERR978157 1 0.541 1.000 0.876 0.124
#> ERR978158 1 0.541 1.000 0.876 0.124
#> ERR978159 1 0.541 1.000 0.876 0.124
#> ERR978160 1 0.541 1.000 0.876 0.124
#> ERR978161 1 0.541 1.000 0.876 0.124
#> ERR978162 1 0.541 1.000 0.876 0.124
#> ERR978163 1 0.541 1.000 0.876 0.124
#> ERR978164 1 0.541 1.000 0.876 0.124
#> ERR978165 1 0.541 1.000 0.876 0.124
#> ERR978166 1 0.541 1.000 0.876 0.124
#> ERR978167 1 0.541 1.000 0.876 0.124
#> ERR978168 1 0.541 1.000 0.876 0.124
#> ERR978169 2 0.993 0.603 0.452 0.548
#> ERR978170 2 0.993 0.603 0.452 0.548
#> ERR978171 2 0.993 0.603 0.452 0.548
#> ERR978172 2 0.993 0.603 0.452 0.548
#> ERR978173 2 0.993 0.603 0.452 0.548
#> ERR978174 2 0.993 0.603 0.452 0.548
#> ERR978175 2 0.993 0.603 0.452 0.548
#> ERR978176 2 0.993 0.609 0.452 0.548
#> ERR978177 2 0.993 0.609 0.452 0.548
#> ERR978178 2 0.993 0.609 0.452 0.548
#> ERR978179 2 0.993 0.609 0.452 0.548
#> ERR978180 2 0.993 0.609 0.452 0.548
#> ERR978181 2 0.993 0.609 0.452 0.548
#> ERR978182 2 0.993 0.609 0.452 0.548
#> ERR978183 2 0.278 0.732 0.048 0.952
#> ERR978184 2 0.278 0.732 0.048 0.952
#> ERR978185 2 0.278 0.732 0.048 0.952
#> ERR978186 2 0.278 0.732 0.048 0.952
#> ERR978187 2 0.278 0.732 0.048 0.952
#> ERR978188 2 0.278 0.732 0.048 0.952
#> ERR978189 2 0.278 0.732 0.048 0.952
#> ERR978190 2 0.278 0.732 0.048 0.952
#> ERR978191 2 0.278 0.732 0.048 0.952
#> ERR978192 2 0.278 0.732 0.048 0.952
#> ERR978193 2 0.278 0.732 0.048 0.952
#> ERR978194 2 0.278 0.732 0.048 0.952
#> ERR978195 2 0.278 0.732 0.048 0.952
#> ERR978196 2 0.278 0.732 0.048 0.952
#> ERR978197 2 0.584 0.798 0.140 0.860
#> ERR978198 2 0.584 0.798 0.140 0.860
#> ERR978199 2 0.584 0.798 0.140 0.860
#> ERR978200 2 0.584 0.798 0.140 0.860
#> ERR978201 2 0.584 0.798 0.140 0.860
#> ERR978202 2 0.584 0.798 0.140 0.860
#> ERR978203 2 0.584 0.798 0.140 0.860
#> ERR978204 2 0.529 0.795 0.120 0.880
#> ERR978205 2 0.529 0.795 0.120 0.880
#> ERR978206 2 0.529 0.795 0.120 0.880
#> ERR978207 2 0.529 0.795 0.120 0.880
#> ERR978208 2 0.529 0.795 0.120 0.880
#> ERR978209 2 0.529 0.795 0.120 0.880
#> ERR978210 2 0.529 0.795 0.120 0.880
#> ERR978211 2 0.529 0.795 0.120 0.880
#> ERR978212 2 0.634 0.797 0.160 0.840
#> ERR978213 2 0.634 0.797 0.160 0.840
#> ERR978214 2 0.634 0.797 0.160 0.840
#> ERR978215 2 0.634 0.797 0.160 0.840
#> ERR978216 2 0.634 0.797 0.160 0.840
#> ERR978217 2 0.634 0.797 0.160 0.840
#> ERR978218 2 0.634 0.797 0.160 0.840
#> ERR978219 2 0.634 0.797 0.160 0.840
#> ERR978220 2 0.634 0.797 0.160 0.840
#> ERR978221 2 0.634 0.797 0.160 0.840
#> ERR978222 2 0.634 0.797 0.160 0.840
#> ERR978223 2 0.634 0.797 0.160 0.840
#> ERR978224 2 0.634 0.797 0.160 0.840
#> ERR978225 2 0.634 0.797 0.160 0.840
#> ERR978226 2 0.634 0.797 0.160 0.840
#> ERR978227 1 0.541 1.000 0.876 0.124
#> ERR978228 1 0.541 1.000 0.876 0.124
#> ERR978229 1 0.541 1.000 0.876 0.124
#> ERR978230 1 0.541 1.000 0.876 0.124
#> ERR978231 1 0.541 1.000 0.876 0.124
#> ERR978232 1 0.541 1.000 0.876 0.124
#> ERR978233 1 0.541 1.000 0.876 0.124
#> ERR978234 1 0.541 1.000 0.876 0.124
#> ERR978235 1 0.541 1.000 0.876 0.124
#> ERR978236 1 0.541 1.000 0.876 0.124
#> ERR978237 1 0.541 1.000 0.876 0.124
#> ERR978238 1 0.541 1.000 0.876 0.124
#> ERR978239 1 0.541 1.000 0.876 0.124
#> ERR978240 1 0.541 1.000 0.876 0.124
#> ERR978241 2 0.992 0.626 0.448 0.552
#> ERR978242 2 0.992 0.626 0.448 0.552
#> ERR978243 2 0.992 0.626 0.448 0.552
#> ERR978244 2 0.992 0.626 0.448 0.552
#> ERR978245 2 0.992 0.626 0.448 0.552
#> ERR978246 2 0.992 0.626 0.448 0.552
#> ERR978247 2 0.992 0.626 0.448 0.552
#> ERR978248 2 0.730 0.795 0.204 0.796
#> ERR978249 2 0.730 0.795 0.204 0.796
#> ERR978250 2 0.730 0.795 0.204 0.796
#> ERR978251 2 0.730 0.795 0.204 0.796
#> ERR978252 2 0.730 0.795 0.204 0.796
#> ERR978253 2 0.730 0.795 0.204 0.796
#> ERR978254 2 0.730 0.795 0.204 0.796
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978108 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978109 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978110 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978111 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978112 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978113 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978114 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978115 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978116 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978117 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978118 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978119 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978120 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978121 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978122 2 0.195 0.5788 0.008 0.952 0.040
#> ERR978123 3 0.691 0.4562 0.028 0.344 0.628
#> ERR978124 3 0.691 0.4562 0.028 0.344 0.628
#> ERR978125 3 0.691 0.4562 0.028 0.344 0.628
#> ERR978126 3 0.691 0.4562 0.028 0.344 0.628
#> ERR978127 3 0.691 0.4562 0.028 0.344 0.628
#> ERR978128 3 0.691 0.4562 0.028 0.344 0.628
#> ERR978129 3 0.691 0.4562 0.028 0.344 0.628
#> ERR978130 3 0.691 0.4562 0.028 0.344 0.628
#> ERR978131 3 0.693 0.4524 0.028 0.348 0.624
#> ERR978132 3 0.693 0.4524 0.028 0.348 0.624
#> ERR978133 3 0.693 0.4524 0.028 0.348 0.624
#> ERR978134 3 0.693 0.4524 0.028 0.348 0.624
#> ERR978135 3 0.693 0.4524 0.028 0.348 0.624
#> ERR978136 3 0.693 0.4524 0.028 0.348 0.624
#> ERR978137 3 0.693 0.4524 0.028 0.348 0.624
#> ERR978138 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978139 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978140 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978141 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978142 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978143 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978144 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978145 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978146 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978147 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978148 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978149 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978150 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978151 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978152 3 0.782 0.4675 0.072 0.324 0.604
#> ERR978153 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978154 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978155 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978156 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978157 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978158 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978159 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978160 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978161 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978162 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978163 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978164 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978165 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978166 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978167 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978168 1 0.350 0.9790 0.900 0.028 0.072
#> ERR978169 3 0.781 0.4637 0.184 0.144 0.672
#> ERR978170 3 0.781 0.4637 0.184 0.144 0.672
#> ERR978171 3 0.781 0.4637 0.184 0.144 0.672
#> ERR978172 3 0.781 0.4637 0.184 0.144 0.672
#> ERR978173 3 0.781 0.4637 0.184 0.144 0.672
#> ERR978174 3 0.781 0.4637 0.184 0.144 0.672
#> ERR978175 3 0.781 0.4637 0.184 0.144 0.672
#> ERR978176 3 0.801 0.4557 0.192 0.152 0.656
#> ERR978177 3 0.801 0.4557 0.192 0.152 0.656
#> ERR978178 3 0.801 0.4557 0.192 0.152 0.656
#> ERR978179 3 0.801 0.4557 0.192 0.152 0.656
#> ERR978180 3 0.801 0.4557 0.192 0.152 0.656
#> ERR978181 3 0.801 0.4557 0.192 0.152 0.656
#> ERR978182 3 0.801 0.4557 0.192 0.152 0.656
#> ERR978183 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978184 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978185 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978186 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978187 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978188 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978189 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978190 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978191 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978192 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978193 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978194 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978195 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978196 2 0.148 0.5836 0.020 0.968 0.012
#> ERR978197 3 0.707 0.1175 0.020 0.472 0.508
#> ERR978198 3 0.707 0.1175 0.020 0.472 0.508
#> ERR978199 3 0.707 0.1175 0.020 0.472 0.508
#> ERR978200 3 0.707 0.1175 0.020 0.472 0.508
#> ERR978201 3 0.707 0.1175 0.020 0.472 0.508
#> ERR978202 3 0.707 0.1175 0.020 0.472 0.508
#> ERR978203 3 0.707 0.1175 0.020 0.472 0.508
#> ERR978204 2 0.708 -0.0737 0.020 0.492 0.488
#> ERR978205 2 0.708 -0.0737 0.020 0.492 0.488
#> ERR978206 2 0.708 -0.0737 0.020 0.492 0.488
#> ERR978207 2 0.708 -0.0737 0.020 0.492 0.488
#> ERR978208 2 0.708 -0.0737 0.020 0.492 0.488
#> ERR978209 2 0.708 -0.0737 0.020 0.492 0.488
#> ERR978210 2 0.708 -0.0737 0.020 0.492 0.488
#> ERR978211 2 0.708 -0.0737 0.020 0.492 0.488
#> ERR978212 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978213 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978214 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978215 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978216 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978217 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978218 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978219 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978220 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978221 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978222 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978223 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978224 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978225 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978226 2 0.809 0.1182 0.068 0.516 0.416
#> ERR978227 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978228 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978229 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978230 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978231 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978232 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978233 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978234 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978235 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978236 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978237 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978238 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978239 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978240 1 0.175 0.9760 0.960 0.028 0.012
#> ERR978241 3 0.746 0.4797 0.180 0.124 0.696
#> ERR978242 3 0.746 0.4797 0.180 0.124 0.696
#> ERR978243 3 0.746 0.4797 0.180 0.124 0.696
#> ERR978244 3 0.746 0.4797 0.180 0.124 0.696
#> ERR978245 3 0.746 0.4797 0.180 0.124 0.696
#> ERR978246 3 0.746 0.4797 0.180 0.124 0.696
#> ERR978247 3 0.746 0.4797 0.180 0.124 0.696
#> ERR978248 2 0.879 0.0275 0.116 0.492 0.392
#> ERR978249 2 0.879 0.0275 0.116 0.492 0.392
#> ERR978250 2 0.879 0.0275 0.116 0.492 0.392
#> ERR978251 2 0.879 0.0275 0.116 0.492 0.392
#> ERR978252 2 0.879 0.0275 0.116 0.492 0.392
#> ERR978253 2 0.879 0.0275 0.116 0.492 0.392
#> ERR978254 2 0.879 0.0275 0.116 0.492 0.392
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.5788 0.903 0.004 0.716 0.176 0.104
#> ERR978108 2 0.5788 0.903 0.004 0.716 0.176 0.104
#> ERR978109 2 0.5788 0.903 0.004 0.716 0.176 0.104
#> ERR978110 2 0.5788 0.903 0.004 0.716 0.176 0.104
#> ERR978111 2 0.5788 0.903 0.004 0.716 0.176 0.104
#> ERR978112 2 0.5788 0.903 0.004 0.716 0.176 0.104
#> ERR978113 2 0.5788 0.903 0.004 0.716 0.176 0.104
#> ERR978114 2 0.5788 0.903 0.004 0.716 0.176 0.104
#> ERR978115 2 0.5674 0.903 0.004 0.724 0.176 0.096
#> ERR978116 2 0.5674 0.903 0.004 0.724 0.176 0.096
#> ERR978117 2 0.5674 0.903 0.004 0.724 0.176 0.096
#> ERR978118 2 0.5674 0.903 0.004 0.724 0.176 0.096
#> ERR978119 2 0.5674 0.903 0.004 0.724 0.176 0.096
#> ERR978120 2 0.5674 0.903 0.004 0.724 0.176 0.096
#> ERR978121 2 0.5674 0.903 0.004 0.724 0.176 0.096
#> ERR978122 2 0.5674 0.903 0.004 0.724 0.176 0.096
#> ERR978123 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978124 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978125 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978126 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978127 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978128 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978129 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978130 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978131 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978132 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978133 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978134 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978135 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978136 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978137 3 0.1762 0.584 0.012 0.016 0.952 0.020
#> ERR978138 3 0.6274 0.488 0.028 0.088 0.704 0.180
#> ERR978139 3 0.6274 0.488 0.028 0.088 0.704 0.180
#> ERR978140 3 0.6274 0.488 0.028 0.088 0.704 0.180
#> ERR978141 3 0.6274 0.488 0.028 0.088 0.704 0.180
#> ERR978142 3 0.6274 0.488 0.028 0.088 0.704 0.180
#> ERR978143 3 0.6274 0.488 0.028 0.088 0.704 0.180
#> ERR978144 3 0.6274 0.488 0.028 0.088 0.704 0.180
#> ERR978145 3 0.6274 0.488 0.028 0.088 0.704 0.180
#> ERR978146 3 0.6070 0.490 0.028 0.080 0.720 0.172
#> ERR978147 3 0.6070 0.490 0.028 0.080 0.720 0.172
#> ERR978148 3 0.6070 0.490 0.028 0.080 0.720 0.172
#> ERR978149 3 0.6070 0.490 0.028 0.080 0.720 0.172
#> ERR978150 3 0.6070 0.490 0.028 0.080 0.720 0.172
#> ERR978151 3 0.6070 0.490 0.028 0.080 0.720 0.172
#> ERR978152 3 0.6070 0.490 0.028 0.080 0.720 0.172
#> ERR978153 1 0.4548 0.921 0.804 0.044 0.008 0.144
#> ERR978154 1 0.4548 0.921 0.804 0.044 0.008 0.144
#> ERR978155 1 0.4548 0.921 0.804 0.044 0.008 0.144
#> ERR978156 1 0.4548 0.921 0.804 0.044 0.008 0.144
#> ERR978157 1 0.4548 0.921 0.804 0.044 0.008 0.144
#> ERR978158 1 0.4548 0.921 0.804 0.044 0.008 0.144
#> ERR978159 1 0.4548 0.921 0.804 0.044 0.008 0.144
#> ERR978160 1 0.4548 0.921 0.804 0.044 0.008 0.144
#> ERR978161 1 0.4597 0.920 0.800 0.044 0.008 0.148
#> ERR978162 1 0.4597 0.920 0.800 0.044 0.008 0.148
#> ERR978163 1 0.4597 0.920 0.800 0.044 0.008 0.148
#> ERR978164 1 0.4597 0.920 0.800 0.044 0.008 0.148
#> ERR978165 1 0.4597 0.920 0.800 0.044 0.008 0.148
#> ERR978166 1 0.4597 0.920 0.800 0.044 0.008 0.148
#> ERR978167 1 0.4597 0.920 0.800 0.044 0.008 0.148
#> ERR978168 1 0.4597 0.920 0.800 0.044 0.008 0.148
#> ERR978169 4 0.7346 0.794 0.068 0.060 0.280 0.592
#> ERR978170 4 0.7346 0.794 0.068 0.060 0.280 0.592
#> ERR978171 4 0.7346 0.794 0.068 0.060 0.280 0.592
#> ERR978172 4 0.7346 0.794 0.068 0.060 0.280 0.592
#> ERR978173 4 0.7346 0.794 0.068 0.060 0.280 0.592
#> ERR978174 4 0.7346 0.794 0.068 0.060 0.280 0.592
#> ERR978175 4 0.7346 0.794 0.068 0.060 0.280 0.592
#> ERR978176 4 0.7419 0.793 0.064 0.076 0.260 0.600
#> ERR978177 4 0.7419 0.793 0.064 0.076 0.260 0.600
#> ERR978178 4 0.7419 0.793 0.064 0.076 0.260 0.600
#> ERR978179 4 0.7419 0.793 0.064 0.076 0.260 0.600
#> ERR978180 4 0.7419 0.793 0.064 0.076 0.260 0.600
#> ERR978181 4 0.7419 0.793 0.064 0.076 0.260 0.600
#> ERR978182 4 0.7419 0.793 0.064 0.076 0.260 0.600
#> ERR978183 2 0.3172 0.888 0.004 0.872 0.112 0.012
#> ERR978184 2 0.3172 0.888 0.004 0.872 0.112 0.012
#> ERR978185 2 0.3172 0.888 0.004 0.872 0.112 0.012
#> ERR978186 2 0.3172 0.888 0.004 0.872 0.112 0.012
#> ERR978187 2 0.3172 0.888 0.004 0.872 0.112 0.012
#> ERR978188 2 0.3172 0.888 0.004 0.872 0.112 0.012
#> ERR978189 2 0.3172 0.888 0.004 0.872 0.112 0.012
#> ERR978190 2 0.3043 0.890 0.008 0.876 0.112 0.004
#> ERR978191 2 0.3043 0.890 0.008 0.876 0.112 0.004
#> ERR978192 2 0.3043 0.890 0.008 0.876 0.112 0.004
#> ERR978193 2 0.3043 0.890 0.008 0.876 0.112 0.004
#> ERR978194 2 0.3043 0.890 0.008 0.876 0.112 0.004
#> ERR978195 2 0.3043 0.890 0.008 0.876 0.112 0.004
#> ERR978196 2 0.3043 0.890 0.008 0.876 0.112 0.004
#> ERR978197 3 0.4662 0.630 0.000 0.112 0.796 0.092
#> ERR978198 3 0.4662 0.630 0.000 0.112 0.796 0.092
#> ERR978199 3 0.4662 0.630 0.000 0.112 0.796 0.092
#> ERR978200 3 0.4662 0.630 0.000 0.112 0.796 0.092
#> ERR978201 3 0.4662 0.630 0.000 0.112 0.796 0.092
#> ERR978202 3 0.4662 0.630 0.000 0.112 0.796 0.092
#> ERR978203 3 0.4662 0.630 0.000 0.112 0.796 0.092
#> ERR978204 3 0.4931 0.626 0.000 0.132 0.776 0.092
#> ERR978205 3 0.4931 0.626 0.000 0.132 0.776 0.092
#> ERR978206 3 0.4931 0.626 0.000 0.132 0.776 0.092
#> ERR978207 3 0.4931 0.626 0.000 0.132 0.776 0.092
#> ERR978208 3 0.4931 0.626 0.000 0.132 0.776 0.092
#> ERR978209 3 0.4931 0.626 0.000 0.132 0.776 0.092
#> ERR978210 3 0.4931 0.626 0.000 0.132 0.776 0.092
#> ERR978211 3 0.4931 0.626 0.000 0.132 0.776 0.092
#> ERR978212 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978213 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978214 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978215 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978216 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978217 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978218 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978219 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978220 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978221 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978222 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978223 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978224 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978225 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978226 3 0.8140 0.500 0.020 0.304 0.460 0.216
#> ERR978227 1 0.0804 0.908 0.980 0.012 0.008 0.000
#> ERR978228 1 0.0804 0.908 0.980 0.012 0.008 0.000
#> ERR978229 1 0.0804 0.908 0.980 0.012 0.008 0.000
#> ERR978230 1 0.0804 0.908 0.980 0.012 0.008 0.000
#> ERR978231 1 0.0804 0.908 0.980 0.012 0.008 0.000
#> ERR978232 1 0.0804 0.908 0.980 0.012 0.008 0.000
#> ERR978233 1 0.0804 0.908 0.980 0.012 0.008 0.000
#> ERR978234 1 0.0804 0.908 0.980 0.000 0.008 0.012
#> ERR978235 1 0.0804 0.908 0.980 0.000 0.008 0.012
#> ERR978236 1 0.0804 0.908 0.980 0.000 0.008 0.012
#> ERR978237 1 0.0804 0.908 0.980 0.000 0.008 0.012
#> ERR978238 1 0.0804 0.908 0.980 0.000 0.008 0.012
#> ERR978239 1 0.0804 0.908 0.980 0.000 0.008 0.012
#> ERR978240 1 0.0804 0.908 0.980 0.000 0.008 0.012
#> ERR978241 4 0.7798 0.770 0.060 0.088 0.316 0.536
#> ERR978242 4 0.7798 0.770 0.060 0.088 0.316 0.536
#> ERR978243 4 0.7798 0.770 0.060 0.088 0.316 0.536
#> ERR978244 4 0.7798 0.770 0.060 0.088 0.316 0.536
#> ERR978245 4 0.7798 0.770 0.060 0.088 0.316 0.536
#> ERR978246 4 0.7798 0.770 0.060 0.088 0.316 0.536
#> ERR978247 4 0.7798 0.770 0.060 0.088 0.316 0.536
#> ERR978248 4 0.8831 0.333 0.044 0.292 0.292 0.372
#> ERR978249 4 0.8831 0.333 0.044 0.292 0.292 0.372
#> ERR978250 4 0.8831 0.333 0.044 0.292 0.292 0.372
#> ERR978251 4 0.8831 0.333 0.044 0.292 0.292 0.372
#> ERR978252 4 0.8831 0.333 0.044 0.292 0.292 0.372
#> ERR978253 4 0.8831 0.333 0.044 0.292 0.292 0.372
#> ERR978254 4 0.8831 0.333 0.044 0.292 0.292 0.372
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.5089 0.880 0.008 0.748 0.052 0.036 NA
#> ERR978108 2 0.5089 0.880 0.008 0.748 0.052 0.036 NA
#> ERR978109 2 0.5089 0.880 0.008 0.748 0.052 0.036 NA
#> ERR978110 2 0.5089 0.880 0.008 0.748 0.052 0.036 NA
#> ERR978111 2 0.5089 0.880 0.008 0.748 0.052 0.036 NA
#> ERR978112 2 0.5089 0.880 0.008 0.748 0.052 0.036 NA
#> ERR978113 2 0.5089 0.880 0.008 0.748 0.052 0.036 NA
#> ERR978114 2 0.5089 0.880 0.008 0.748 0.052 0.036 NA
#> ERR978115 2 0.5383 0.880 0.008 0.724 0.052 0.044 NA
#> ERR978116 2 0.5383 0.880 0.008 0.724 0.052 0.044 NA
#> ERR978117 2 0.5383 0.880 0.008 0.724 0.052 0.044 NA
#> ERR978118 2 0.5383 0.880 0.008 0.724 0.052 0.044 NA
#> ERR978119 2 0.5383 0.880 0.008 0.724 0.052 0.044 NA
#> ERR978120 2 0.5383 0.880 0.008 0.724 0.052 0.044 NA
#> ERR978121 2 0.5383 0.880 0.008 0.724 0.052 0.044 NA
#> ERR978122 2 0.5383 0.880 0.008 0.724 0.052 0.044 NA
#> ERR978123 3 0.6746 0.503 0.004 0.044 0.548 0.104 NA
#> ERR978124 3 0.6746 0.503 0.004 0.044 0.548 0.104 NA
#> ERR978125 3 0.6746 0.503 0.004 0.044 0.548 0.104 NA
#> ERR978126 3 0.6746 0.503 0.004 0.044 0.548 0.104 NA
#> ERR978127 3 0.6746 0.503 0.004 0.044 0.548 0.104 NA
#> ERR978128 3 0.6746 0.503 0.004 0.044 0.548 0.104 NA
#> ERR978129 3 0.6746 0.503 0.004 0.044 0.548 0.104 NA
#> ERR978130 3 0.6746 0.503 0.004 0.044 0.548 0.104 NA
#> ERR978131 3 0.6795 0.505 0.004 0.048 0.540 0.100 NA
#> ERR978132 3 0.6795 0.505 0.004 0.048 0.540 0.100 NA
#> ERR978133 3 0.6795 0.505 0.004 0.048 0.540 0.100 NA
#> ERR978134 3 0.6795 0.505 0.004 0.048 0.540 0.100 NA
#> ERR978135 3 0.6795 0.505 0.004 0.048 0.540 0.100 NA
#> ERR978136 3 0.6795 0.505 0.004 0.048 0.540 0.100 NA
#> ERR978137 3 0.6795 0.505 0.004 0.048 0.540 0.100 NA
#> ERR978138 3 0.4928 0.366 0.008 0.036 0.684 0.268 NA
#> ERR978139 3 0.4928 0.366 0.008 0.036 0.684 0.268 NA
#> ERR978140 3 0.4928 0.366 0.008 0.036 0.684 0.268 NA
#> ERR978141 3 0.4928 0.366 0.008 0.036 0.684 0.268 NA
#> ERR978142 3 0.4928 0.366 0.008 0.036 0.684 0.268 NA
#> ERR978143 3 0.4928 0.366 0.008 0.036 0.684 0.268 NA
#> ERR978144 3 0.4928 0.366 0.008 0.036 0.684 0.268 NA
#> ERR978145 3 0.4928 0.366 0.008 0.036 0.684 0.268 NA
#> ERR978146 3 0.5359 0.366 0.008 0.036 0.664 0.272 NA
#> ERR978147 3 0.5359 0.366 0.008 0.036 0.664 0.272 NA
#> ERR978148 3 0.5359 0.366 0.008 0.036 0.664 0.272 NA
#> ERR978149 3 0.5359 0.366 0.008 0.036 0.664 0.272 NA
#> ERR978150 3 0.5359 0.366 0.008 0.036 0.664 0.272 NA
#> ERR978151 3 0.5359 0.366 0.008 0.036 0.664 0.272 NA
#> ERR978152 3 0.5359 0.366 0.008 0.036 0.664 0.272 NA
#> ERR978153 1 0.4314 0.904 0.772 0.008 0.020 0.016 NA
#> ERR978154 1 0.4314 0.904 0.772 0.008 0.020 0.016 NA
#> ERR978155 1 0.4314 0.904 0.772 0.008 0.020 0.016 NA
#> ERR978156 1 0.4314 0.904 0.772 0.008 0.020 0.016 NA
#> ERR978157 1 0.4314 0.904 0.772 0.008 0.020 0.016 NA
#> ERR978158 1 0.4314 0.904 0.772 0.008 0.020 0.016 NA
#> ERR978159 1 0.4314 0.904 0.772 0.008 0.020 0.016 NA
#> ERR978160 1 0.4314 0.904 0.772 0.008 0.020 0.016 NA
#> ERR978161 1 0.3659 0.903 0.768 0.000 0.012 0.000 NA
#> ERR978162 1 0.3659 0.903 0.768 0.000 0.012 0.000 NA
#> ERR978163 1 0.3659 0.903 0.768 0.000 0.012 0.000 NA
#> ERR978164 1 0.3659 0.903 0.768 0.000 0.012 0.000 NA
#> ERR978165 1 0.3659 0.903 0.768 0.000 0.012 0.000 NA
#> ERR978166 1 0.3659 0.903 0.768 0.000 0.012 0.000 NA
#> ERR978167 1 0.3659 0.903 0.768 0.000 0.012 0.000 NA
#> ERR978168 1 0.3659 0.903 0.768 0.000 0.012 0.000 NA
#> ERR978169 4 0.2151 0.778 0.020 0.016 0.040 0.924 NA
#> ERR978170 4 0.2151 0.778 0.020 0.016 0.040 0.924 NA
#> ERR978171 4 0.2151 0.778 0.020 0.016 0.040 0.924 NA
#> ERR978172 4 0.2151 0.778 0.020 0.016 0.040 0.924 NA
#> ERR978173 4 0.2151 0.778 0.020 0.016 0.040 0.924 NA
#> ERR978174 4 0.2151 0.778 0.020 0.016 0.040 0.924 NA
#> ERR978175 4 0.2151 0.778 0.020 0.016 0.040 0.924 NA
#> ERR978176 4 0.3327 0.778 0.024 0.016 0.032 0.876 NA
#> ERR978177 4 0.3327 0.778 0.024 0.016 0.032 0.876 NA
#> ERR978178 4 0.3327 0.778 0.024 0.016 0.032 0.876 NA
#> ERR978179 4 0.3327 0.778 0.024 0.016 0.032 0.876 NA
#> ERR978180 4 0.3327 0.778 0.024 0.016 0.032 0.876 NA
#> ERR978181 4 0.3327 0.778 0.024 0.016 0.032 0.876 NA
#> ERR978182 4 0.3327 0.778 0.024 0.016 0.032 0.876 NA
#> ERR978183 2 0.1522 0.862 0.012 0.944 0.044 0.000 NA
#> ERR978184 2 0.1522 0.862 0.012 0.944 0.044 0.000 NA
#> ERR978185 2 0.1522 0.862 0.012 0.944 0.044 0.000 NA
#> ERR978186 2 0.1522 0.862 0.012 0.944 0.044 0.000 NA
#> ERR978187 2 0.1522 0.862 0.012 0.944 0.044 0.000 NA
#> ERR978188 2 0.1522 0.862 0.012 0.944 0.044 0.000 NA
#> ERR978189 2 0.1522 0.862 0.012 0.944 0.044 0.000 NA
#> ERR978190 2 0.2297 0.862 0.008 0.920 0.044 0.008 NA
#> ERR978191 2 0.2297 0.862 0.008 0.920 0.044 0.008 NA
#> ERR978192 2 0.2297 0.862 0.008 0.920 0.044 0.008 NA
#> ERR978193 2 0.2297 0.862 0.008 0.920 0.044 0.008 NA
#> ERR978194 2 0.2297 0.862 0.008 0.920 0.044 0.008 NA
#> ERR978195 2 0.2297 0.862 0.008 0.920 0.044 0.008 NA
#> ERR978196 2 0.2297 0.862 0.008 0.920 0.044 0.008 NA
#> ERR978197 3 0.6150 0.544 0.000 0.072 0.508 0.024 NA
#> ERR978198 3 0.6150 0.544 0.000 0.072 0.508 0.024 NA
#> ERR978199 3 0.6150 0.544 0.000 0.072 0.508 0.024 NA
#> ERR978200 3 0.6150 0.544 0.000 0.072 0.508 0.024 NA
#> ERR978201 3 0.6150 0.544 0.000 0.072 0.508 0.024 NA
#> ERR978202 3 0.6150 0.544 0.000 0.072 0.508 0.024 NA
#> ERR978203 3 0.6150 0.544 0.000 0.072 0.508 0.024 NA
#> ERR978204 3 0.6236 0.539 0.000 0.088 0.508 0.020 NA
#> ERR978205 3 0.6236 0.539 0.000 0.088 0.508 0.020 NA
#> ERR978206 3 0.6236 0.539 0.000 0.088 0.508 0.020 NA
#> ERR978207 3 0.6236 0.539 0.000 0.088 0.508 0.020 NA
#> ERR978208 3 0.6236 0.539 0.000 0.088 0.508 0.020 NA
#> ERR978209 3 0.6236 0.539 0.000 0.088 0.508 0.020 NA
#> ERR978210 3 0.6236 0.539 0.000 0.088 0.508 0.020 NA
#> ERR978211 3 0.6236 0.539 0.000 0.088 0.508 0.020 NA
#> ERR978212 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978213 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978214 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978215 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978216 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978217 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978218 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978219 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978220 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978221 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978222 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978223 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978224 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978225 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978226 3 0.6935 0.384 0.008 0.136 0.616 0.108 NA
#> ERR978227 1 0.0898 0.894 0.972 0.000 0.020 0.008 NA
#> ERR978228 1 0.0898 0.894 0.972 0.000 0.020 0.008 NA
#> ERR978229 1 0.0898 0.894 0.972 0.000 0.020 0.008 NA
#> ERR978230 1 0.0898 0.894 0.972 0.000 0.020 0.008 NA
#> ERR978231 1 0.0898 0.894 0.972 0.000 0.020 0.008 NA
#> ERR978232 1 0.0898 0.894 0.972 0.000 0.020 0.008 NA
#> ERR978233 1 0.0898 0.894 0.972 0.000 0.020 0.008 NA
#> ERR978234 1 0.0912 0.894 0.972 0.000 0.012 0.000 NA
#> ERR978235 1 0.0912 0.894 0.972 0.000 0.012 0.000 NA
#> ERR978236 1 0.0912 0.894 0.972 0.000 0.012 0.000 NA
#> ERR978237 1 0.0912 0.894 0.972 0.000 0.012 0.000 NA
#> ERR978238 1 0.0912 0.894 0.972 0.000 0.012 0.000 NA
#> ERR978239 1 0.0912 0.894 0.972 0.000 0.012 0.000 NA
#> ERR978240 1 0.0912 0.894 0.972 0.000 0.012 0.000 NA
#> ERR978241 4 0.4476 0.756 0.024 0.020 0.120 0.800 NA
#> ERR978242 4 0.4476 0.756 0.024 0.020 0.120 0.800 NA
#> ERR978243 4 0.4476 0.756 0.024 0.020 0.120 0.800 NA
#> ERR978244 4 0.4476 0.756 0.024 0.020 0.120 0.800 NA
#> ERR978245 4 0.4476 0.756 0.024 0.020 0.120 0.800 NA
#> ERR978246 4 0.4476 0.756 0.024 0.020 0.120 0.800 NA
#> ERR978247 4 0.4476 0.756 0.024 0.020 0.120 0.800 NA
#> ERR978248 4 0.8710 0.332 0.028 0.156 0.300 0.368 NA
#> ERR978249 4 0.8710 0.332 0.028 0.156 0.300 0.368 NA
#> ERR978250 4 0.8710 0.332 0.028 0.156 0.300 0.368 NA
#> ERR978251 4 0.8710 0.332 0.028 0.156 0.300 0.368 NA
#> ERR978252 4 0.8710 0.332 0.028 0.156 0.300 0.368 NA
#> ERR978253 4 0.8710 0.332 0.028 0.156 0.300 0.368 NA
#> ERR978254 4 0.8710 0.332 0.028 0.156 0.300 0.368 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.1811 0.822 0.000 0.936 0.012 0.012 0.020 0.020
#> ERR978108 2 0.1811 0.822 0.000 0.936 0.012 0.012 0.020 0.020
#> ERR978109 2 0.1811 0.822 0.000 0.936 0.012 0.012 0.020 0.020
#> ERR978110 2 0.1811 0.822 0.000 0.936 0.012 0.012 0.020 0.020
#> ERR978111 2 0.1811 0.822 0.000 0.936 0.012 0.012 0.020 0.020
#> ERR978112 2 0.1811 0.822 0.000 0.936 0.012 0.012 0.020 0.020
#> ERR978113 2 0.1811 0.822 0.000 0.936 0.012 0.012 0.020 0.020
#> ERR978114 2 0.1811 0.822 0.000 0.936 0.012 0.012 0.020 0.020
#> ERR978115 2 0.0951 0.822 0.000 0.968 0.004 0.008 0.020 0.000
#> ERR978116 2 0.0951 0.822 0.000 0.968 0.004 0.008 0.020 0.000
#> ERR978117 2 0.0951 0.822 0.000 0.968 0.004 0.008 0.020 0.000
#> ERR978118 2 0.0951 0.822 0.000 0.968 0.004 0.008 0.020 0.000
#> ERR978119 2 0.0951 0.822 0.000 0.968 0.004 0.008 0.020 0.000
#> ERR978120 2 0.0951 0.822 0.000 0.968 0.004 0.008 0.020 0.000
#> ERR978121 2 0.0951 0.822 0.000 0.968 0.004 0.008 0.020 0.000
#> ERR978122 2 0.0951 0.822 0.000 0.968 0.004 0.008 0.020 0.000
#> ERR978123 5 0.7015 -0.195 0.000 0.032 0.364 0.064 0.432 0.108
#> ERR978124 5 0.7015 -0.195 0.000 0.032 0.364 0.064 0.432 0.108
#> ERR978125 5 0.7015 -0.195 0.000 0.032 0.364 0.064 0.432 0.108
#> ERR978126 5 0.7015 -0.195 0.000 0.032 0.364 0.064 0.432 0.108
#> ERR978127 5 0.7015 -0.195 0.000 0.032 0.364 0.064 0.432 0.108
#> ERR978128 5 0.7015 -0.195 0.000 0.032 0.364 0.064 0.432 0.108
#> ERR978129 5 0.7015 -0.195 0.000 0.032 0.364 0.064 0.432 0.108
#> ERR978130 5 0.7015 -0.195 0.000 0.032 0.364 0.064 0.432 0.108
#> ERR978131 5 0.6981 -0.205 0.000 0.032 0.376 0.060 0.424 0.108
#> ERR978132 5 0.6981 -0.205 0.000 0.032 0.376 0.060 0.424 0.108
#> ERR978133 5 0.6981 -0.205 0.000 0.032 0.376 0.060 0.424 0.108
#> ERR978134 5 0.6981 -0.205 0.000 0.032 0.376 0.060 0.424 0.108
#> ERR978135 5 0.6981 -0.205 0.000 0.032 0.376 0.060 0.424 0.108
#> ERR978136 5 0.6981 -0.205 0.000 0.032 0.376 0.060 0.424 0.108
#> ERR978137 5 0.6981 -0.205 0.000 0.032 0.376 0.060 0.424 0.108
#> ERR978138 5 0.2747 0.382 0.004 0.028 0.004 0.096 0.868 0.000
#> ERR978139 5 0.2747 0.382 0.004 0.028 0.004 0.096 0.868 0.000
#> ERR978140 5 0.2747 0.382 0.004 0.028 0.004 0.096 0.868 0.000
#> ERR978141 5 0.2747 0.382 0.004 0.028 0.004 0.096 0.868 0.000
#> ERR978142 5 0.2747 0.382 0.004 0.028 0.004 0.096 0.868 0.000
#> ERR978143 5 0.2747 0.382 0.004 0.028 0.004 0.096 0.868 0.000
#> ERR978144 5 0.2747 0.382 0.004 0.028 0.004 0.096 0.868 0.000
#> ERR978145 5 0.2747 0.382 0.004 0.028 0.004 0.096 0.868 0.000
#> ERR978146 5 0.3211 0.378 0.004 0.028 0.012 0.096 0.852 0.008
#> ERR978147 5 0.3211 0.378 0.004 0.028 0.012 0.096 0.852 0.008
#> ERR978148 5 0.3211 0.378 0.004 0.028 0.012 0.096 0.852 0.008
#> ERR978149 5 0.3211 0.378 0.004 0.028 0.012 0.096 0.852 0.008
#> ERR978150 5 0.3211 0.378 0.004 0.028 0.012 0.096 0.852 0.008
#> ERR978151 5 0.3211 0.378 0.004 0.028 0.012 0.096 0.852 0.008
#> ERR978152 5 0.3211 0.378 0.004 0.028 0.012 0.096 0.852 0.008
#> ERR978153 1 0.3942 0.840 0.624 0.004 0.000 0.000 0.004 0.368
#> ERR978154 1 0.3942 0.840 0.624 0.004 0.000 0.000 0.004 0.368
#> ERR978155 1 0.3942 0.840 0.624 0.004 0.000 0.000 0.004 0.368
#> ERR978156 1 0.3942 0.840 0.624 0.004 0.000 0.000 0.004 0.368
#> ERR978157 1 0.3942 0.840 0.624 0.004 0.000 0.000 0.004 0.368
#> ERR978158 1 0.3942 0.840 0.624 0.004 0.000 0.000 0.004 0.368
#> ERR978159 1 0.3942 0.840 0.624 0.004 0.000 0.000 0.004 0.368
#> ERR978160 1 0.3942 0.840 0.624 0.004 0.000 0.000 0.004 0.368
#> ERR978161 1 0.5169 0.840 0.624 0.004 0.036 0.024 0.008 0.304
#> ERR978162 1 0.5169 0.840 0.624 0.004 0.036 0.024 0.008 0.304
#> ERR978163 1 0.5157 0.840 0.624 0.004 0.040 0.020 0.008 0.304
#> ERR978164 1 0.5157 0.840 0.624 0.004 0.040 0.020 0.008 0.304
#> ERR978165 1 0.5157 0.840 0.624 0.004 0.040 0.020 0.008 0.304
#> ERR978166 1 0.5157 0.840 0.624 0.004 0.040 0.020 0.008 0.304
#> ERR978167 1 0.5175 0.840 0.624 0.004 0.032 0.028 0.008 0.304
#> ERR978168 1 0.5175 0.840 0.624 0.004 0.032 0.028 0.008 0.304
#> ERR978169 4 0.2856 0.886 0.012 0.004 0.004 0.844 0.136 0.000
#> ERR978170 4 0.2856 0.886 0.012 0.004 0.004 0.844 0.136 0.000
#> ERR978171 4 0.2856 0.886 0.012 0.004 0.004 0.844 0.136 0.000
#> ERR978172 4 0.2856 0.886 0.012 0.004 0.004 0.844 0.136 0.000
#> ERR978173 4 0.2856 0.886 0.012 0.004 0.004 0.844 0.136 0.000
#> ERR978174 4 0.2856 0.886 0.012 0.004 0.004 0.844 0.136 0.000
#> ERR978175 4 0.2856 0.886 0.012 0.004 0.004 0.844 0.136 0.000
#> ERR978176 4 0.4942 0.872 0.016 0.020 0.020 0.740 0.152 0.052
#> ERR978177 4 0.4942 0.872 0.016 0.020 0.020 0.740 0.152 0.052
#> ERR978178 4 0.4942 0.872 0.016 0.020 0.020 0.740 0.152 0.052
#> ERR978179 4 0.4942 0.872 0.016 0.020 0.020 0.740 0.152 0.052
#> ERR978180 4 0.4942 0.872 0.016 0.020 0.020 0.740 0.152 0.052
#> ERR978181 4 0.4942 0.872 0.016 0.020 0.020 0.740 0.152 0.052
#> ERR978182 4 0.4942 0.872 0.016 0.020 0.020 0.740 0.152 0.052
#> ERR978183 2 0.5630 0.795 0.000 0.616 0.048 0.036 0.024 0.276
#> ERR978184 2 0.5630 0.795 0.000 0.616 0.048 0.036 0.024 0.276
#> ERR978185 2 0.5630 0.795 0.000 0.616 0.048 0.036 0.024 0.276
#> ERR978186 2 0.5630 0.795 0.000 0.616 0.048 0.036 0.024 0.276
#> ERR978187 2 0.5630 0.795 0.000 0.616 0.048 0.036 0.024 0.276
#> ERR978188 2 0.5630 0.795 0.000 0.616 0.048 0.036 0.024 0.276
#> ERR978189 2 0.5630 0.795 0.000 0.616 0.048 0.036 0.024 0.276
#> ERR978190 2 0.5685 0.795 0.000 0.636 0.056 0.044 0.024 0.240
#> ERR978191 2 0.5685 0.795 0.000 0.636 0.056 0.044 0.024 0.240
#> ERR978192 2 0.5685 0.795 0.000 0.636 0.056 0.044 0.024 0.240
#> ERR978193 2 0.5685 0.795 0.000 0.636 0.056 0.044 0.024 0.240
#> ERR978194 2 0.5685 0.795 0.000 0.636 0.056 0.044 0.024 0.240
#> ERR978195 2 0.5685 0.795 0.000 0.636 0.056 0.044 0.024 0.240
#> ERR978196 2 0.5689 0.795 0.000 0.636 0.052 0.048 0.024 0.240
#> ERR978197 3 0.5197 0.940 0.000 0.048 0.640 0.020 0.276 0.016
#> ERR978198 3 0.5197 0.940 0.000 0.048 0.640 0.020 0.276 0.016
#> ERR978199 3 0.5197 0.940 0.000 0.048 0.640 0.020 0.276 0.016
#> ERR978200 3 0.5197 0.940 0.000 0.048 0.640 0.020 0.276 0.016
#> ERR978201 3 0.5197 0.940 0.000 0.048 0.640 0.020 0.276 0.016
#> ERR978202 3 0.5197 0.940 0.000 0.048 0.640 0.020 0.276 0.016
#> ERR978203 3 0.5197 0.940 0.000 0.048 0.640 0.020 0.276 0.016
#> ERR978204 3 0.4796 0.948 0.000 0.052 0.660 0.008 0.272 0.008
#> ERR978205 3 0.4796 0.948 0.000 0.052 0.660 0.008 0.272 0.008
#> ERR978206 3 0.4796 0.948 0.000 0.052 0.660 0.008 0.272 0.008
#> ERR978207 3 0.4796 0.948 0.000 0.052 0.660 0.008 0.272 0.008
#> ERR978208 3 0.4796 0.948 0.000 0.052 0.660 0.008 0.272 0.008
#> ERR978209 3 0.4796 0.948 0.000 0.052 0.660 0.008 0.272 0.008
#> ERR978210 3 0.4796 0.948 0.000 0.052 0.660 0.008 0.272 0.008
#> ERR978211 3 0.4796 0.948 0.000 0.052 0.660 0.008 0.272 0.008
#> ERR978212 5 0.6532 0.320 0.004 0.084 0.224 0.008 0.568 0.112
#> ERR978213 5 0.6532 0.320 0.004 0.084 0.224 0.008 0.568 0.112
#> ERR978214 5 0.6532 0.320 0.004 0.084 0.224 0.008 0.568 0.112
#> ERR978215 5 0.6532 0.320 0.004 0.084 0.224 0.008 0.568 0.112
#> ERR978216 5 0.6532 0.320 0.004 0.084 0.224 0.008 0.568 0.112
#> ERR978217 5 0.6532 0.320 0.004 0.084 0.224 0.008 0.568 0.112
#> ERR978218 5 0.6532 0.320 0.004 0.084 0.224 0.008 0.568 0.112
#> ERR978219 5 0.6516 0.320 0.004 0.084 0.228 0.008 0.568 0.108
#> ERR978220 5 0.6516 0.320 0.004 0.084 0.228 0.008 0.568 0.108
#> ERR978221 5 0.6516 0.320 0.004 0.084 0.228 0.008 0.568 0.108
#> ERR978222 5 0.6516 0.320 0.004 0.084 0.228 0.008 0.568 0.108
#> ERR978223 5 0.6516 0.320 0.004 0.084 0.228 0.008 0.568 0.108
#> ERR978224 5 0.6516 0.320 0.004 0.084 0.228 0.008 0.568 0.108
#> ERR978225 5 0.6516 0.320 0.004 0.084 0.228 0.008 0.568 0.108
#> ERR978226 5 0.6516 0.320 0.004 0.084 0.228 0.008 0.568 0.108
#> ERR978227 1 0.1129 0.824 0.964 0.004 0.008 0.012 0.012 0.000
#> ERR978228 1 0.1129 0.824 0.964 0.004 0.008 0.012 0.012 0.000
#> ERR978229 1 0.1129 0.824 0.964 0.004 0.008 0.012 0.012 0.000
#> ERR978230 1 0.1129 0.824 0.964 0.004 0.008 0.012 0.012 0.000
#> ERR978231 1 0.1129 0.824 0.964 0.004 0.008 0.012 0.012 0.000
#> ERR978232 1 0.1129 0.824 0.964 0.004 0.008 0.012 0.012 0.000
#> ERR978233 1 0.1129 0.824 0.964 0.004 0.008 0.012 0.012 0.000
#> ERR978234 1 0.0436 0.824 0.988 0.004 0.000 0.004 0.004 0.000
#> ERR978235 1 0.0436 0.824 0.988 0.004 0.000 0.004 0.004 0.000
#> ERR978236 1 0.0436 0.824 0.988 0.004 0.000 0.004 0.004 0.000
#> ERR978237 1 0.0436 0.824 0.988 0.004 0.000 0.004 0.004 0.000
#> ERR978238 1 0.0436 0.824 0.988 0.004 0.000 0.004 0.004 0.000
#> ERR978239 1 0.0436 0.824 0.988 0.004 0.000 0.004 0.004 0.000
#> ERR978240 1 0.0436 0.824 0.988 0.004 0.004 0.000 0.004 0.000
#> ERR978241 4 0.6113 0.830 0.016 0.012 0.084 0.632 0.204 0.052
#> ERR978242 4 0.6113 0.830 0.016 0.012 0.084 0.632 0.204 0.052
#> ERR978243 4 0.6113 0.830 0.016 0.012 0.084 0.632 0.204 0.052
#> ERR978244 4 0.6113 0.830 0.016 0.012 0.084 0.632 0.204 0.052
#> ERR978245 4 0.6113 0.830 0.016 0.012 0.084 0.632 0.204 0.052
#> ERR978246 4 0.6113 0.830 0.016 0.012 0.084 0.632 0.204 0.052
#> ERR978247 4 0.6113 0.830 0.016 0.012 0.084 0.632 0.204 0.052
#> ERR978248 5 0.8833 -0.143 0.004 0.152 0.132 0.256 0.288 0.168
#> ERR978249 5 0.8833 -0.143 0.004 0.152 0.132 0.256 0.288 0.168
#> ERR978250 5 0.8833 -0.143 0.004 0.152 0.132 0.256 0.288 0.168
#> ERR978251 5 0.8833 -0.143 0.004 0.152 0.132 0.256 0.288 0.168
#> ERR978252 5 0.8833 -0.143 0.004 0.152 0.132 0.256 0.288 0.168
#> ERR978253 5 0.8833 -0.143 0.004 0.152 0.132 0.256 0.288 0.168
#> ERR978254 5 0.8833 -0.143 0.004 0.152 0.132 0.256 0.288 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["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 14049 rows and 148 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 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 0.799 0.941 0.959 0.4682 0.520 0.520
#> 3 3 0.672 0.801 0.874 0.3681 0.647 0.428
#> 4 4 0.745 0.843 0.893 0.1413 0.724 0.397
#> 5 5 0.904 0.959 0.962 0.1004 0.917 0.702
#> 6 6 0.911 0.926 0.862 0.0262 0.979 0.894
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 5
There is also optional best \(k\) = 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0.000 0.970 0.000 1.000
#> ERR978108 2 0.000 0.970 0.000 1.000
#> ERR978109 2 0.000 0.970 0.000 1.000
#> ERR978110 2 0.000 0.970 0.000 1.000
#> ERR978111 2 0.000 0.970 0.000 1.000
#> ERR978112 2 0.000 0.970 0.000 1.000
#> ERR978113 2 0.000 0.970 0.000 1.000
#> ERR978114 2 0.000 0.970 0.000 1.000
#> ERR978115 2 0.000 0.970 0.000 1.000
#> ERR978116 2 0.000 0.970 0.000 1.000
#> ERR978117 2 0.000 0.970 0.000 1.000
#> ERR978118 2 0.000 0.970 0.000 1.000
#> ERR978119 2 0.000 0.970 0.000 1.000
#> ERR978120 2 0.000 0.970 0.000 1.000
#> ERR978121 2 0.000 0.970 0.000 1.000
#> ERR978122 2 0.000 0.970 0.000 1.000
#> ERR978123 2 0.518 0.898 0.116 0.884
#> ERR978124 2 0.518 0.898 0.116 0.884
#> ERR978125 2 0.518 0.898 0.116 0.884
#> ERR978126 2 0.518 0.898 0.116 0.884
#> ERR978127 2 0.518 0.898 0.116 0.884
#> ERR978128 2 0.518 0.898 0.116 0.884
#> ERR978129 2 0.518 0.898 0.116 0.884
#> ERR978130 2 0.518 0.898 0.116 0.884
#> ERR978131 2 0.518 0.898 0.116 0.884
#> ERR978132 2 0.518 0.898 0.116 0.884
#> ERR978133 2 0.518 0.898 0.116 0.884
#> ERR978134 2 0.518 0.898 0.116 0.884
#> ERR978135 2 0.518 0.898 0.116 0.884
#> ERR978136 2 0.518 0.898 0.116 0.884
#> ERR978137 2 0.518 0.898 0.116 0.884
#> ERR978138 2 0.295 0.947 0.052 0.948
#> ERR978139 2 0.295 0.947 0.052 0.948
#> ERR978140 2 0.295 0.947 0.052 0.948
#> ERR978141 2 0.295 0.947 0.052 0.948
#> ERR978142 2 0.295 0.947 0.052 0.948
#> ERR978143 2 0.295 0.947 0.052 0.948
#> ERR978144 2 0.295 0.947 0.052 0.948
#> ERR978145 2 0.295 0.947 0.052 0.948
#> ERR978146 2 0.295 0.947 0.052 0.948
#> ERR978147 2 0.295 0.947 0.052 0.948
#> ERR978148 2 0.295 0.947 0.052 0.948
#> ERR978149 2 0.295 0.947 0.052 0.948
#> ERR978150 2 0.295 0.947 0.052 0.948
#> ERR978151 2 0.295 0.947 0.052 0.948
#> ERR978152 2 0.295 0.947 0.052 0.948
#> ERR978153 1 0.000 0.938 1.000 0.000
#> ERR978154 1 0.000 0.938 1.000 0.000
#> ERR978155 1 0.000 0.938 1.000 0.000
#> ERR978156 1 0.000 0.938 1.000 0.000
#> ERR978157 1 0.000 0.938 1.000 0.000
#> ERR978158 1 0.000 0.938 1.000 0.000
#> ERR978159 1 0.000 0.938 1.000 0.000
#> ERR978160 1 0.000 0.938 1.000 0.000
#> ERR978161 1 0.000 0.938 1.000 0.000
#> ERR978162 1 0.000 0.938 1.000 0.000
#> ERR978163 1 0.000 0.938 1.000 0.000
#> ERR978164 1 0.000 0.938 1.000 0.000
#> ERR978165 1 0.000 0.938 1.000 0.000
#> ERR978166 1 0.000 0.938 1.000 0.000
#> ERR978167 1 0.000 0.938 1.000 0.000
#> ERR978168 1 0.000 0.938 1.000 0.000
#> ERR978169 1 0.456 0.926 0.904 0.096
#> ERR978170 1 0.456 0.926 0.904 0.096
#> ERR978171 1 0.456 0.926 0.904 0.096
#> ERR978172 1 0.456 0.926 0.904 0.096
#> ERR978173 1 0.456 0.926 0.904 0.096
#> ERR978174 1 0.456 0.926 0.904 0.096
#> ERR978175 1 0.456 0.926 0.904 0.096
#> ERR978176 1 0.456 0.926 0.904 0.096
#> ERR978177 1 0.456 0.926 0.904 0.096
#> ERR978178 1 0.456 0.926 0.904 0.096
#> ERR978179 1 0.456 0.926 0.904 0.096
#> ERR978180 1 0.456 0.926 0.904 0.096
#> ERR978181 1 0.456 0.926 0.904 0.096
#> ERR978182 1 0.456 0.926 0.904 0.096
#> ERR978183 2 0.000 0.970 0.000 1.000
#> ERR978184 2 0.000 0.970 0.000 1.000
#> ERR978185 2 0.000 0.970 0.000 1.000
#> ERR978186 2 0.000 0.970 0.000 1.000
#> ERR978187 2 0.000 0.970 0.000 1.000
#> ERR978188 2 0.000 0.970 0.000 1.000
#> ERR978189 2 0.000 0.970 0.000 1.000
#> ERR978190 2 0.000 0.970 0.000 1.000
#> ERR978191 2 0.000 0.970 0.000 1.000
#> ERR978192 2 0.000 0.970 0.000 1.000
#> ERR978193 2 0.000 0.970 0.000 1.000
#> ERR978194 2 0.000 0.970 0.000 1.000
#> ERR978195 2 0.000 0.970 0.000 1.000
#> ERR978196 2 0.000 0.970 0.000 1.000
#> ERR978197 2 0.000 0.970 0.000 1.000
#> ERR978198 2 0.000 0.970 0.000 1.000
#> ERR978199 2 0.000 0.970 0.000 1.000
#> ERR978200 2 0.000 0.970 0.000 1.000
#> ERR978201 2 0.000 0.970 0.000 1.000
#> ERR978202 2 0.000 0.970 0.000 1.000
#> ERR978203 2 0.000 0.970 0.000 1.000
#> ERR978204 2 0.000 0.970 0.000 1.000
#> ERR978205 2 0.000 0.970 0.000 1.000
#> ERR978206 2 0.000 0.970 0.000 1.000
#> ERR978207 2 0.000 0.970 0.000 1.000
#> ERR978208 2 0.000 0.970 0.000 1.000
#> ERR978209 2 0.000 0.970 0.000 1.000
#> ERR978210 2 0.000 0.970 0.000 1.000
#> ERR978211 2 0.000 0.970 0.000 1.000
#> ERR978212 2 0.000 0.970 0.000 1.000
#> ERR978213 2 0.000 0.970 0.000 1.000
#> ERR978214 2 0.000 0.970 0.000 1.000
#> ERR978215 2 0.000 0.970 0.000 1.000
#> ERR978216 2 0.000 0.970 0.000 1.000
#> ERR978217 2 0.000 0.970 0.000 1.000
#> ERR978218 2 0.000 0.970 0.000 1.000
#> ERR978219 2 0.000 0.970 0.000 1.000
#> ERR978220 2 0.000 0.970 0.000 1.000
#> ERR978221 2 0.000 0.970 0.000 1.000
#> ERR978222 2 0.000 0.970 0.000 1.000
#> ERR978223 2 0.000 0.970 0.000 1.000
#> ERR978224 2 0.000 0.970 0.000 1.000
#> ERR978225 2 0.000 0.970 0.000 1.000
#> ERR978226 2 0.000 0.970 0.000 1.000
#> ERR978227 1 0.000 0.938 1.000 0.000
#> ERR978228 1 0.000 0.938 1.000 0.000
#> ERR978229 1 0.000 0.938 1.000 0.000
#> ERR978230 1 0.000 0.938 1.000 0.000
#> ERR978231 1 0.000 0.938 1.000 0.000
#> ERR978232 1 0.000 0.938 1.000 0.000
#> ERR978233 1 0.000 0.938 1.000 0.000
#> ERR978234 1 0.000 0.938 1.000 0.000
#> ERR978235 1 0.000 0.938 1.000 0.000
#> ERR978236 1 0.000 0.938 1.000 0.000
#> ERR978237 1 0.000 0.938 1.000 0.000
#> ERR978238 1 0.000 0.938 1.000 0.000
#> ERR978239 1 0.000 0.938 1.000 0.000
#> ERR978240 1 0.000 0.938 1.000 0.000
#> ERR978241 1 0.456 0.926 0.904 0.096
#> ERR978242 1 0.456 0.926 0.904 0.096
#> ERR978243 1 0.456 0.926 0.904 0.096
#> ERR978244 1 0.456 0.926 0.904 0.096
#> ERR978245 1 0.456 0.926 0.904 0.096
#> ERR978246 1 0.456 0.926 0.904 0.096
#> ERR978247 1 0.456 0.926 0.904 0.096
#> ERR978248 1 0.745 0.829 0.788 0.212
#> ERR978249 1 0.745 0.829 0.788 0.212
#> ERR978250 1 0.745 0.829 0.788 0.212
#> ERR978251 1 0.745 0.829 0.788 0.212
#> ERR978252 1 0.745 0.829 0.788 0.212
#> ERR978253 1 0.745 0.829 0.788 0.212
#> ERR978254 1 0.745 0.829 0.788 0.212
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.000 0.786 0.000 1.000 0.000
#> ERR978108 2 0.000 0.786 0.000 1.000 0.000
#> ERR978109 2 0.000 0.786 0.000 1.000 0.000
#> ERR978110 2 0.000 0.786 0.000 1.000 0.000
#> ERR978111 2 0.000 0.786 0.000 1.000 0.000
#> ERR978112 2 0.000 0.786 0.000 1.000 0.000
#> ERR978113 2 0.000 0.786 0.000 1.000 0.000
#> ERR978114 2 0.000 0.786 0.000 1.000 0.000
#> ERR978115 2 0.000 0.786 0.000 1.000 0.000
#> ERR978116 2 0.000 0.786 0.000 1.000 0.000
#> ERR978117 2 0.000 0.786 0.000 1.000 0.000
#> ERR978118 2 0.000 0.786 0.000 1.000 0.000
#> ERR978119 2 0.000 0.786 0.000 1.000 0.000
#> ERR978120 2 0.000 0.786 0.000 1.000 0.000
#> ERR978121 2 0.000 0.786 0.000 1.000 0.000
#> ERR978122 2 0.000 0.786 0.000 1.000 0.000
#> ERR978123 3 0.103 0.826 0.000 0.024 0.976
#> ERR978124 3 0.103 0.826 0.000 0.024 0.976
#> ERR978125 3 0.103 0.826 0.000 0.024 0.976
#> ERR978126 3 0.103 0.826 0.000 0.024 0.976
#> ERR978127 3 0.103 0.826 0.000 0.024 0.976
#> ERR978128 3 0.103 0.826 0.000 0.024 0.976
#> ERR978129 3 0.103 0.826 0.000 0.024 0.976
#> ERR978130 3 0.103 0.826 0.000 0.024 0.976
#> ERR978131 3 0.103 0.826 0.000 0.024 0.976
#> ERR978132 3 0.103 0.826 0.000 0.024 0.976
#> ERR978133 3 0.103 0.826 0.000 0.024 0.976
#> ERR978134 3 0.103 0.826 0.000 0.024 0.976
#> ERR978135 3 0.103 0.826 0.000 0.024 0.976
#> ERR978136 3 0.103 0.826 0.000 0.024 0.976
#> ERR978137 3 0.103 0.826 0.000 0.024 0.976
#> ERR978138 3 0.000 0.829 0.000 0.000 1.000
#> ERR978139 3 0.000 0.829 0.000 0.000 1.000
#> ERR978140 3 0.000 0.829 0.000 0.000 1.000
#> ERR978141 3 0.000 0.829 0.000 0.000 1.000
#> ERR978142 3 0.000 0.829 0.000 0.000 1.000
#> ERR978143 3 0.000 0.829 0.000 0.000 1.000
#> ERR978144 3 0.000 0.829 0.000 0.000 1.000
#> ERR978145 3 0.000 0.829 0.000 0.000 1.000
#> ERR978146 3 0.000 0.829 0.000 0.000 1.000
#> ERR978147 3 0.000 0.829 0.000 0.000 1.000
#> ERR978148 3 0.000 0.829 0.000 0.000 1.000
#> ERR978149 3 0.000 0.829 0.000 0.000 1.000
#> ERR978150 3 0.000 0.829 0.000 0.000 1.000
#> ERR978151 3 0.000 0.829 0.000 0.000 1.000
#> ERR978152 3 0.000 0.829 0.000 0.000 1.000
#> ERR978153 1 0.000 1.000 1.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1.000 0.000 0.000
#> ERR978169 3 0.687 0.760 0.080 0.196 0.724
#> ERR978170 3 0.687 0.760 0.080 0.196 0.724
#> ERR978171 3 0.687 0.760 0.080 0.196 0.724
#> ERR978172 3 0.687 0.760 0.080 0.196 0.724
#> ERR978173 3 0.687 0.760 0.080 0.196 0.724
#> ERR978174 3 0.687 0.760 0.080 0.196 0.724
#> ERR978175 3 0.687 0.760 0.080 0.196 0.724
#> ERR978176 3 0.719 0.732 0.080 0.224 0.696
#> ERR978177 3 0.719 0.732 0.080 0.224 0.696
#> ERR978178 3 0.719 0.732 0.080 0.224 0.696
#> ERR978179 3 0.719 0.732 0.080 0.224 0.696
#> ERR978180 3 0.719 0.732 0.080 0.224 0.696
#> ERR978181 3 0.719 0.732 0.080 0.224 0.696
#> ERR978182 3 0.719 0.732 0.080 0.224 0.696
#> ERR978183 2 0.000 0.786 0.000 1.000 0.000
#> ERR978184 2 0.000 0.786 0.000 1.000 0.000
#> ERR978185 2 0.000 0.786 0.000 1.000 0.000
#> ERR978186 2 0.000 0.786 0.000 1.000 0.000
#> ERR978187 2 0.000 0.786 0.000 1.000 0.000
#> ERR978188 2 0.000 0.786 0.000 1.000 0.000
#> ERR978189 2 0.000 0.786 0.000 1.000 0.000
#> ERR978190 2 0.000 0.786 0.000 1.000 0.000
#> ERR978191 2 0.000 0.786 0.000 1.000 0.000
#> ERR978192 2 0.000 0.786 0.000 1.000 0.000
#> ERR978193 2 0.000 0.786 0.000 1.000 0.000
#> ERR978194 2 0.000 0.786 0.000 1.000 0.000
#> ERR978195 2 0.000 0.786 0.000 1.000 0.000
#> ERR978196 2 0.000 0.786 0.000 1.000 0.000
#> ERR978197 2 0.626 0.571 0.000 0.552 0.448
#> ERR978198 2 0.626 0.571 0.000 0.552 0.448
#> ERR978199 2 0.626 0.571 0.000 0.552 0.448
#> ERR978200 2 0.626 0.571 0.000 0.552 0.448
#> ERR978201 2 0.626 0.571 0.000 0.552 0.448
#> ERR978202 2 0.626 0.571 0.000 0.552 0.448
#> ERR978203 2 0.626 0.571 0.000 0.552 0.448
#> ERR978204 2 0.624 0.583 0.000 0.560 0.440
#> ERR978205 2 0.624 0.583 0.000 0.560 0.440
#> ERR978206 2 0.624 0.583 0.000 0.560 0.440
#> ERR978207 2 0.624 0.583 0.000 0.560 0.440
#> ERR978208 2 0.624 0.583 0.000 0.560 0.440
#> ERR978209 2 0.624 0.583 0.000 0.560 0.440
#> ERR978210 2 0.624 0.583 0.000 0.560 0.440
#> ERR978211 2 0.624 0.583 0.000 0.560 0.440
#> ERR978212 2 0.588 0.692 0.000 0.652 0.348
#> ERR978213 2 0.588 0.692 0.000 0.652 0.348
#> ERR978214 2 0.588 0.692 0.000 0.652 0.348
#> ERR978215 2 0.588 0.692 0.000 0.652 0.348
#> ERR978216 2 0.588 0.692 0.000 0.652 0.348
#> ERR978217 2 0.588 0.692 0.000 0.652 0.348
#> ERR978218 2 0.588 0.692 0.000 0.652 0.348
#> ERR978219 2 0.588 0.692 0.000 0.652 0.348
#> ERR978220 2 0.588 0.692 0.000 0.652 0.348
#> ERR978221 2 0.588 0.692 0.000 0.652 0.348
#> ERR978222 2 0.588 0.692 0.000 0.652 0.348
#> ERR978223 2 0.588 0.692 0.000 0.652 0.348
#> ERR978224 2 0.588 0.692 0.000 0.652 0.348
#> ERR978225 2 0.588 0.692 0.000 0.652 0.348
#> ERR978226 2 0.588 0.692 0.000 0.652 0.348
#> ERR978227 1 0.000 1.000 1.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1.000 0.000 0.000
#> ERR978241 3 0.687 0.760 0.080 0.196 0.724
#> ERR978242 3 0.687 0.760 0.080 0.196 0.724
#> ERR978243 3 0.687 0.760 0.080 0.196 0.724
#> ERR978244 3 0.687 0.760 0.080 0.196 0.724
#> ERR978245 3 0.687 0.760 0.080 0.196 0.724
#> ERR978246 3 0.687 0.760 0.080 0.196 0.724
#> ERR978247 3 0.687 0.760 0.080 0.196 0.724
#> ERR978248 2 0.234 0.766 0.012 0.940 0.048
#> ERR978249 2 0.234 0.766 0.012 0.940 0.048
#> ERR978250 2 0.234 0.766 0.012 0.940 0.048
#> ERR978251 2 0.234 0.766 0.012 0.940 0.048
#> ERR978252 2 0.234 0.766 0.012 0.940 0.048
#> ERR978253 2 0.234 0.766 0.012 0.940 0.048
#> ERR978254 2 0.234 0.766 0.012 0.940 0.048
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978123 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978124 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978125 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978126 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978127 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978128 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978129 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978130 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978131 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978132 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978133 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978134 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978135 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978136 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978137 3 0.2149 0.742 0.000 0.000 0.912 0.088
#> ERR978138 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978139 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978140 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978141 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978142 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978143 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978144 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978145 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978146 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978147 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978148 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978149 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978150 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978151 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978152 3 0.4961 0.565 0.000 0.000 0.552 0.448
#> ERR978153 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978169 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978170 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978171 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978172 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978173 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978174 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978175 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978176 4 0.0336 0.921 0.000 0.008 0.000 0.992
#> ERR978177 4 0.0336 0.921 0.000 0.008 0.000 0.992
#> ERR978178 4 0.0336 0.921 0.000 0.008 0.000 0.992
#> ERR978179 4 0.0336 0.921 0.000 0.008 0.000 0.992
#> ERR978180 4 0.0336 0.921 0.000 0.008 0.000 0.992
#> ERR978181 4 0.0336 0.921 0.000 0.008 0.000 0.992
#> ERR978182 4 0.0336 0.921 0.000 0.008 0.000 0.992
#> ERR978183 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978184 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978185 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978186 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978187 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978188 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978189 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978190 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978191 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978192 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978193 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978194 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978195 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978196 2 0.0000 1.000 0.000 1.000 0.000 0.000
#> ERR978197 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978198 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978199 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978200 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978201 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978202 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978203 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978204 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978205 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978206 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978207 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978208 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978209 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978210 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978211 3 0.0336 0.742 0.000 0.008 0.992 0.000
#> ERR978212 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978213 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978214 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978215 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978216 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978217 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978218 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978219 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978220 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978221 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978222 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978223 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978224 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978225 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978226 3 0.6685 0.624 0.000 0.160 0.616 0.224
#> ERR978227 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> ERR978241 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978242 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978243 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978244 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978245 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978246 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978247 4 0.0524 0.922 0.004 0.008 0.000 0.988
#> ERR978248 4 0.4937 0.763 0.000 0.172 0.064 0.764
#> ERR978249 4 0.4937 0.763 0.000 0.172 0.064 0.764
#> ERR978250 4 0.4937 0.763 0.000 0.172 0.064 0.764
#> ERR978251 4 0.4937 0.763 0.000 0.172 0.064 0.764
#> ERR978252 4 0.4937 0.763 0.000 0.172 0.064 0.764
#> ERR978253 4 0.4937 0.763 0.000 0.172 0.064 0.764
#> ERR978254 4 0.4937 0.763 0.000 0.172 0.064 0.764
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978108 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978109 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978110 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978111 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978112 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978113 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978114 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978115 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978116 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978117 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978118 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978119 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978120 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978121 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978122 2 0.0162 0.998 0 0.996 0.004 0.000 0.000
#> ERR978123 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978124 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978125 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978126 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978127 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978128 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978129 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978130 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978131 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978132 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978133 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978134 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978135 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978136 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978137 3 0.0324 0.936 0 0.004 0.992 0.004 0.000
#> ERR978138 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978139 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978140 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978141 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978142 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978143 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978144 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978145 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978146 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978147 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978148 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978149 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978150 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978151 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978152 5 0.3719 0.901 0 0.000 0.116 0.068 0.816
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978170 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978171 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978172 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978173 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978174 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978175 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978176 4 0.0290 0.973 0 0.000 0.008 0.992 0.000
#> ERR978177 4 0.0290 0.973 0 0.000 0.008 0.992 0.000
#> ERR978178 4 0.0290 0.973 0 0.000 0.008 0.992 0.000
#> ERR978179 4 0.0290 0.973 0 0.000 0.008 0.992 0.000
#> ERR978180 4 0.0290 0.973 0 0.000 0.008 0.992 0.000
#> ERR978181 4 0.0290 0.973 0 0.000 0.008 0.992 0.000
#> ERR978182 4 0.0290 0.973 0 0.000 0.008 0.992 0.000
#> ERR978183 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978184 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978185 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978186 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978187 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978188 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978189 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978190 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978191 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978192 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978193 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978194 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978195 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978196 2 0.0000 0.997 0 1.000 0.000 0.000 0.000
#> ERR978197 3 0.2304 0.935 0 0.008 0.892 0.000 0.100
#> ERR978198 3 0.2304 0.935 0 0.008 0.892 0.000 0.100
#> ERR978199 3 0.2304 0.935 0 0.008 0.892 0.000 0.100
#> ERR978200 3 0.2304 0.935 0 0.008 0.892 0.000 0.100
#> ERR978201 3 0.2304 0.935 0 0.008 0.892 0.000 0.100
#> ERR978202 3 0.2304 0.935 0 0.008 0.892 0.000 0.100
#> ERR978203 3 0.2304 0.935 0 0.008 0.892 0.000 0.100
#> ERR978204 3 0.2416 0.934 0 0.012 0.888 0.000 0.100
#> ERR978205 3 0.2416 0.934 0 0.012 0.888 0.000 0.100
#> ERR978206 3 0.2416 0.934 0 0.012 0.888 0.000 0.100
#> ERR978207 3 0.2416 0.934 0 0.012 0.888 0.000 0.100
#> ERR978208 3 0.2416 0.934 0 0.012 0.888 0.000 0.100
#> ERR978209 3 0.2416 0.934 0 0.012 0.888 0.000 0.100
#> ERR978210 3 0.2416 0.934 0 0.012 0.888 0.000 0.100
#> ERR978211 3 0.2416 0.934 0 0.012 0.888 0.000 0.100
#> ERR978212 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978213 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978214 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978215 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978216 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978217 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978218 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978219 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978220 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978221 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978222 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978223 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978224 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978225 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978226 5 0.0451 0.902 0 0.004 0.008 0.000 0.988
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978241 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978242 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978243 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978244 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978245 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978246 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978247 4 0.0162 0.973 0 0.000 0.004 0.996 0.000
#> ERR978248 4 0.2414 0.920 0 0.012 0.008 0.900 0.080
#> ERR978249 4 0.2414 0.920 0 0.012 0.008 0.900 0.080
#> ERR978250 4 0.2414 0.920 0 0.012 0.008 0.900 0.080
#> ERR978251 4 0.2414 0.920 0 0.012 0.008 0.900 0.080
#> ERR978252 4 0.2414 0.920 0 0.012 0.008 0.900 0.080
#> ERR978253 4 0.2414 0.920 0 0.012 0.008 0.900 0.080
#> ERR978254 4 0.2414 0.920 0 0.012 0.008 0.900 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 0.989 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 0.989 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 0.989 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 0.989 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 0.989 0.000 1.000 0.000 0.000 0.000 0.000
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#> ERR978153 1 0.0000 0.995 1.000 0.000 0.000 0.000 0.000 0.000
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#> ERR978176 4 0.2100 0.874 0.000 0.000 0.000 0.884 0.004 0.112
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#> ERR978183 2 0.0777 0.988 0.000 0.972 0.000 0.000 0.004 0.024
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#> ERR978197 3 0.3509 0.807 0.000 0.000 0.744 0.000 0.016 0.240
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#> ERR978212 6 0.4524 1.000 0.000 0.000 0.036 0.000 0.404 0.560
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#> ERR978227 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
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#> ERR978240 1 0.0363 0.995 0.988 0.000 0.000 0.000 0.000 0.012
#> ERR978241 4 0.0692 0.882 0.000 0.000 0.000 0.976 0.004 0.020
#> ERR978242 4 0.0692 0.882 0.000 0.000 0.000 0.976 0.004 0.020
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#> ERR978248 4 0.4002 0.712 0.000 0.008 0.000 0.588 0.000 0.404
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#> ERR978254 4 0.4002 0.712 0.000 0.008 0.000 0.588 0.000 0.404
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 14049 rows and 148 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 1.000 1.000 1.000 0.3261 0.675 0.675
#> 3 3 1.000 0.985 0.993 0.7586 0.757 0.640
#> 4 4 0.839 0.910 0.918 0.2589 0.840 0.630
#> 5 5 0.963 0.928 0.968 0.1040 0.857 0.536
#> 6 6 1.000 0.974 0.983 0.0411 0.959 0.800
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 5
There is also optional best \(k\) = 2 3 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
#> ERR978109 2 0 1 0 1
#> ERR978110 2 0 1 0 1
#> ERR978111 2 0 1 0 1
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#> ERR978210 2 0 1 0 1
#> ERR978211 2 0 1 0 1
#> ERR978212 2 0 1 0 1
#> ERR978213 2 0 1 0 1
#> ERR978214 2 0 1 0 1
#> ERR978215 2 0 1 0 1
#> ERR978216 2 0 1 0 1
#> ERR978217 2 0 1 0 1
#> ERR978218 2 0 1 0 1
#> ERR978219 2 0 1 0 1
#> ERR978220 2 0 1 0 1
#> ERR978221 2 0 1 0 1
#> ERR978222 2 0 1 0 1
#> ERR978223 2 0 1 0 1
#> ERR978224 2 0 1 0 1
#> ERR978225 2 0 1 0 1
#> ERR978226 2 0 1 0 1
#> ERR978227 1 0 1 1 0
#> ERR978228 1 0 1 1 0
#> ERR978229 1 0 1 1 0
#> ERR978230 1 0 1 1 0
#> ERR978231 1 0 1 1 0
#> ERR978232 1 0 1 1 0
#> ERR978233 1 0 1 1 0
#> ERR978234 1 0 1 1 0
#> ERR978235 1 0 1 1 0
#> ERR978236 1 0 1 1 0
#> ERR978237 1 0 1 1 0
#> ERR978238 1 0 1 1 0
#> ERR978239 1 0 1 1 0
#> ERR978240 1 0 1 1 0
#> ERR978241 2 0 1 0 1
#> ERR978242 2 0 1 0 1
#> ERR978243 2 0 1 0 1
#> ERR978244 2 0 1 0 1
#> ERR978245 2 0 1 0 1
#> ERR978246 2 0 1 0 1
#> ERR978247 2 0 1 0 1
#> ERR978248 2 0 1 0 1
#> ERR978249 2 0 1 0 1
#> ERR978250 2 0 1 0 1
#> ERR978251 2 0 1 0 1
#> ERR978252 2 0 1 0 1
#> ERR978253 2 0 1 0 1
#> ERR978254 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.0000 1.000 0 1.000 0.000
#> ERR978108 2 0.0000 1.000 0 1.000 0.000
#> ERR978109 2 0.0000 1.000 0 1.000 0.000
#> ERR978110 2 0.0000 1.000 0 1.000 0.000
#> ERR978111 2 0.0000 1.000 0 1.000 0.000
#> ERR978112 2 0.0000 1.000 0 1.000 0.000
#> ERR978113 2 0.0000 1.000 0 1.000 0.000
#> ERR978114 2 0.0000 1.000 0 1.000 0.000
#> ERR978115 2 0.0000 1.000 0 1.000 0.000
#> ERR978116 2 0.0000 1.000 0 1.000 0.000
#> ERR978117 2 0.0000 1.000 0 1.000 0.000
#> ERR978118 2 0.0000 1.000 0 1.000 0.000
#> ERR978119 2 0.0000 1.000 0 1.000 0.000
#> ERR978120 2 0.0000 1.000 0 1.000 0.000
#> ERR978121 2 0.0000 1.000 0 1.000 0.000
#> ERR978122 2 0.0000 1.000 0 1.000 0.000
#> ERR978123 3 0.0000 0.988 0 0.000 1.000
#> ERR978124 3 0.0000 0.988 0 0.000 1.000
#> ERR978125 3 0.0000 0.988 0 0.000 1.000
#> ERR978126 3 0.0000 0.988 0 0.000 1.000
#> ERR978127 3 0.0000 0.988 0 0.000 1.000
#> ERR978128 3 0.0000 0.988 0 0.000 1.000
#> ERR978129 3 0.0000 0.988 0 0.000 1.000
#> ERR978130 3 0.0000 0.988 0 0.000 1.000
#> ERR978131 3 0.0000 0.988 0 0.000 1.000
#> ERR978132 3 0.0000 0.988 0 0.000 1.000
#> ERR978133 3 0.0000 0.988 0 0.000 1.000
#> ERR978134 3 0.0000 0.988 0 0.000 1.000
#> ERR978135 3 0.0000 0.988 0 0.000 1.000
#> ERR978136 3 0.0000 0.988 0 0.000 1.000
#> ERR978137 3 0.0000 0.988 0 0.000 1.000
#> ERR978138 3 0.0000 0.988 0 0.000 1.000
#> ERR978139 3 0.0000 0.988 0 0.000 1.000
#> ERR978140 3 0.0000 0.988 0 0.000 1.000
#> ERR978141 3 0.0000 0.988 0 0.000 1.000
#> ERR978142 3 0.0000 0.988 0 0.000 1.000
#> ERR978143 3 0.0000 0.988 0 0.000 1.000
#> ERR978144 3 0.0000 0.988 0 0.000 1.000
#> ERR978145 3 0.0000 0.988 0 0.000 1.000
#> ERR978146 3 0.0000 0.988 0 0.000 1.000
#> ERR978147 3 0.0000 0.988 0 0.000 1.000
#> ERR978148 3 0.0000 0.988 0 0.000 1.000
#> ERR978149 3 0.0000 0.988 0 0.000 1.000
#> ERR978150 3 0.0000 0.988 0 0.000 1.000
#> ERR978151 3 0.0000 0.988 0 0.000 1.000
#> ERR978152 3 0.0000 0.988 0 0.000 1.000
#> ERR978153 1 0.0000 1.000 1 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000
#> ERR978169 3 0.0000 0.988 0 0.000 1.000
#> ERR978170 3 0.0000 0.988 0 0.000 1.000
#> ERR978171 3 0.0000 0.988 0 0.000 1.000
#> ERR978172 3 0.0000 0.988 0 0.000 1.000
#> ERR978173 3 0.0000 0.988 0 0.000 1.000
#> ERR978174 3 0.0000 0.988 0 0.000 1.000
#> ERR978175 3 0.0000 0.988 0 0.000 1.000
#> ERR978176 3 0.0000 0.988 0 0.000 1.000
#> ERR978177 3 0.0000 0.988 0 0.000 1.000
#> ERR978178 3 0.0000 0.988 0 0.000 1.000
#> ERR978179 3 0.0000 0.988 0 0.000 1.000
#> ERR978180 3 0.0000 0.988 0 0.000 1.000
#> ERR978181 3 0.0000 0.988 0 0.000 1.000
#> ERR978182 3 0.0000 0.988 0 0.000 1.000
#> ERR978183 2 0.0000 1.000 0 1.000 0.000
#> ERR978184 2 0.0000 1.000 0 1.000 0.000
#> ERR978185 2 0.0000 1.000 0 1.000 0.000
#> ERR978186 2 0.0000 1.000 0 1.000 0.000
#> ERR978187 2 0.0000 1.000 0 1.000 0.000
#> ERR978188 2 0.0000 1.000 0 1.000 0.000
#> ERR978189 2 0.0000 1.000 0 1.000 0.000
#> ERR978190 2 0.0000 1.000 0 1.000 0.000
#> ERR978191 2 0.0000 1.000 0 1.000 0.000
#> ERR978192 2 0.0000 1.000 0 1.000 0.000
#> ERR978193 2 0.0000 1.000 0 1.000 0.000
#> ERR978194 2 0.0000 1.000 0 1.000 0.000
#> ERR978195 2 0.0000 1.000 0 1.000 0.000
#> ERR978196 2 0.0000 1.000 0 1.000 0.000
#> ERR978197 3 0.0000 0.988 0 0.000 1.000
#> ERR978198 3 0.0000 0.988 0 0.000 1.000
#> ERR978199 3 0.0000 0.988 0 0.000 1.000
#> ERR978200 3 0.0000 0.988 0 0.000 1.000
#> ERR978201 3 0.0000 0.988 0 0.000 1.000
#> ERR978202 3 0.0000 0.988 0 0.000 1.000
#> ERR978203 3 0.0000 0.988 0 0.000 1.000
#> ERR978204 3 0.0000 0.988 0 0.000 1.000
#> ERR978205 3 0.0000 0.988 0 0.000 1.000
#> ERR978206 3 0.0000 0.988 0 0.000 1.000
#> ERR978207 3 0.0000 0.988 0 0.000 1.000
#> ERR978208 3 0.0000 0.988 0 0.000 1.000
#> ERR978209 3 0.0000 0.988 0 0.000 1.000
#> ERR978210 3 0.0000 0.988 0 0.000 1.000
#> ERR978211 3 0.0000 0.988 0 0.000 1.000
#> ERR978212 3 0.0000 0.988 0 0.000 1.000
#> ERR978213 3 0.0000 0.988 0 0.000 1.000
#> ERR978214 3 0.0000 0.988 0 0.000 1.000
#> ERR978215 3 0.0000 0.988 0 0.000 1.000
#> ERR978216 3 0.0000 0.988 0 0.000 1.000
#> ERR978217 3 0.0000 0.988 0 0.000 1.000
#> ERR978218 3 0.0000 0.988 0 0.000 1.000
#> ERR978219 3 0.0000 0.988 0 0.000 1.000
#> ERR978220 3 0.0000 0.988 0 0.000 1.000
#> ERR978221 3 0.0000 0.988 0 0.000 1.000
#> ERR978222 3 0.0000 0.988 0 0.000 1.000
#> ERR978223 3 0.0000 0.988 0 0.000 1.000
#> ERR978224 3 0.0000 0.988 0 0.000 1.000
#> ERR978225 3 0.0000 0.988 0 0.000 1.000
#> ERR978226 3 0.0000 0.988 0 0.000 1.000
#> ERR978227 1 0.0000 1.000 1 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000
#> ERR978241 3 0.0000 0.988 0 0.000 1.000
#> ERR978242 3 0.0000 0.988 0 0.000 1.000
#> ERR978243 3 0.0000 0.988 0 0.000 1.000
#> ERR978244 3 0.0000 0.988 0 0.000 1.000
#> ERR978245 3 0.0000 0.988 0 0.000 1.000
#> ERR978246 3 0.0000 0.988 0 0.000 1.000
#> ERR978247 3 0.0000 0.988 0 0.000 1.000
#> ERR978248 3 0.5138 0.679 0 0.252 0.748
#> ERR978249 3 0.3941 0.822 0 0.156 0.844
#> ERR978250 3 0.2066 0.932 0 0.060 0.940
#> ERR978251 3 0.0747 0.974 0 0.016 0.984
#> ERR978252 3 0.2711 0.903 0 0.088 0.912
#> ERR978253 3 0.3816 0.832 0 0.148 0.852
#> ERR978254 3 0.5465 0.614 0 0.288 0.712
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978123 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978124 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978125 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978126 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978127 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978128 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978129 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978130 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978131 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978132 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978133 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978134 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978135 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978136 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978137 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978138 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978139 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978140 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978141 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978142 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978143 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978144 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978145 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978146 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978147 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978148 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978149 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978150 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978151 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978152 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978153 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978169 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978170 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978171 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978172 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978173 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978174 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978175 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978176 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978177 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978178 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978179 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978180 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978181 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978182 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978183 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.00 0.000
#> ERR978197 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978198 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978199 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978200 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978201 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978202 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978203 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978204 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978205 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978206 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978207 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978208 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978209 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978210 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978211 3 0.452 1.000 0 0.000 0.68 0.320
#> ERR978212 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978213 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978214 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978215 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978216 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978217 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978218 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978219 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978220 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978221 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978222 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978223 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978224 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978225 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978226 4 0.000 0.799 0 0.000 0.00 1.000
#> ERR978227 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.00 0.000
#> ERR978241 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978242 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978243 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978244 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978245 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978246 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978247 4 0.452 0.752 0 0.000 0.32 0.680
#> ERR978248 4 0.436 0.617 0 0.292 0.00 0.708
#> ERR978249 4 0.357 0.700 0 0.196 0.00 0.804
#> ERR978250 4 0.187 0.768 0 0.072 0.00 0.928
#> ERR978251 4 0.121 0.784 0 0.040 0.00 0.960
#> ERR978252 4 0.287 0.736 0 0.136 0.00 0.864
#> ERR978253 4 0.331 0.714 0 0.172 0.00 0.828
#> ERR978254 4 0.456 0.560 0 0.328 0.00 0.672
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978108 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978109 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978110 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978111 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978112 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978113 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978114 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978115 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978116 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978117 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978118 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978119 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978120 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978121 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978122 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978123 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978124 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978125 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978126 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978127 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978128 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978129 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978130 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978131 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978132 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978133 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978134 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978135 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978136 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978137 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978138 5 0.000 0.944 0 0 0.000 0 1.000
#> ERR978139 5 0.000 0.944 0 0 0.000 0 1.000
#> ERR978140 5 0.000 0.944 0 0 0.000 0 1.000
#> ERR978141 5 0.000 0.944 0 0 0.000 0 1.000
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#> ERR978151 3 0.409 0.520 0 0 0.632 0 0.368
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#> ERR978198 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978199 3 0.000 0.880 0 0 1.000 0 0.000
#> ERR978200 3 0.000 0.880 0 0 1.000 0 0.000
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#> ERR978204 5 0.314 0.757 0 0 0.204 0 0.796
#> ERR978205 5 0.324 0.744 0 0 0.216 0 0.784
#> ERR978206 5 0.327 0.739 0 0 0.220 0 0.780
#> ERR978207 5 0.324 0.744 0 0 0.216 0 0.784
#> ERR978208 5 0.321 0.749 0 0 0.212 0 0.788
#> ERR978209 5 0.331 0.733 0 0 0.224 0 0.776
#> ERR978210 5 0.324 0.744 0 0 0.216 0 0.784
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#> ERR978212 5 0.000 0.944 0 0 0.000 0 1.000
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#> ERR978241 4 0.000 1.000 0 0 0.000 1 0.000
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#> ERR978248 5 0.000 0.944 0 0 0.000 0 1.000
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#> ERR978254 5 0.000 0.944 0 0 0.000 0 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
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#> ERR978169 4 0.0000 0.991 0 0 0.000 1.000 0.000 0.000
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#> ERR978176 4 0.0713 0.983 0 0 0.000 0.972 0.028 0.000
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#> ERR978204 5 0.2941 0.785 0 0 0.220 0.000 0.780 0.000
#> ERR978205 5 0.3023 0.774 0 0 0.232 0.000 0.768 0.000
#> ERR978206 5 0.3023 0.774 0 0 0.232 0.000 0.768 0.000
#> ERR978207 5 0.3023 0.774 0 0 0.232 0.000 0.768 0.000
#> ERR978208 5 0.2996 0.778 0 0 0.228 0.000 0.772 0.000
#> ERR978209 5 0.3101 0.759 0 0 0.244 0.000 0.756 0.000
#> ERR978210 5 0.3050 0.769 0 0 0.236 0.000 0.764 0.000
#> ERR978211 5 0.2996 0.778 0 0 0.228 0.000 0.772 0.000
#> ERR978212 5 0.0713 0.921 0 0 0.000 0.000 0.972 0.028
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#> ERR978226 5 0.0713 0.921 0 0 0.000 0.000 0.972 0.028
#> ERR978227 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978241 4 0.0000 0.991 0 0 0.000 1.000 0.000 0.000
#> ERR978242 4 0.0000 0.991 0 0 0.000 1.000 0.000 0.000
#> ERR978243 4 0.0000 0.991 0 0 0.000 1.000 0.000 0.000
#> ERR978244 4 0.0000 0.991 0 0 0.000 1.000 0.000 0.000
#> ERR978245 4 0.0000 0.991 0 0 0.000 1.000 0.000 0.000
#> ERR978246 4 0.0000 0.991 0 0 0.000 1.000 0.000 0.000
#> ERR978247 4 0.0000 0.991 0 0 0.000 1.000 0.000 0.000
#> ERR978248 5 0.0000 0.915 0 0 0.000 0.000 1.000 0.000
#> ERR978249 5 0.0000 0.915 0 0 0.000 0.000 1.000 0.000
#> ERR978250 5 0.0000 0.915 0 0 0.000 0.000 1.000 0.000
#> ERR978251 5 0.0000 0.915 0 0 0.000 0.000 1.000 0.000
#> ERR978252 5 0.0000 0.915 0 0 0.000 0.000 1.000 0.000
#> ERR978253 5 0.0000 0.915 0 0 0.000 0.000 1.000 0.000
#> ERR978254 5 0.0000 0.915 0 0 0.000 0.000 1.000 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", "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 14049 rows and 148 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.3261 0.675 0.675
#> 3 3 0.768 0.926 0.955 0.7992 0.757 0.640
#> 4 4 1.000 0.998 0.999 0.2324 0.846 0.642
#> 5 5 1.000 0.990 0.990 0.1136 0.917 0.702
#> 6 6 0.961 0.975 0.954 0.0232 0.982 0.907
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 4 5
There is also optional best \(k\) = 2 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
#> ERR978109 2 0 1 0 1
#> ERR978110 2 0 1 0 1
#> ERR978111 2 0 1 0 1
#> ERR978112 2 0 1 0 1
#> ERR978113 2 0 1 0 1
#> ERR978114 2 0 1 0 1
#> ERR978115 2 0 1 0 1
#> ERR978116 2 0 1 0 1
#> ERR978117 2 0 1 0 1
#> ERR978118 2 0 1 0 1
#> ERR978119 2 0 1 0 1
#> ERR978120 2 0 1 0 1
#> ERR978121 2 0 1 0 1
#> ERR978122 2 0 1 0 1
#> ERR978123 2 0 1 0 1
#> ERR978124 2 0 1 0 1
#> ERR978125 2 0 1 0 1
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#> ERR978127 2 0 1 0 1
#> ERR978128 2 0 1 0 1
#> ERR978129 2 0 1 0 1
#> ERR978130 2 0 1 0 1
#> ERR978131 2 0 1 0 1
#> ERR978132 2 0 1 0 1
#> ERR978133 2 0 1 0 1
#> ERR978134 2 0 1 0 1
#> ERR978135 2 0 1 0 1
#> ERR978136 2 0 1 0 1
#> ERR978137 2 0 1 0 1
#> ERR978138 2 0 1 0 1
#> ERR978139 2 0 1 0 1
#> ERR978140 2 0 1 0 1
#> ERR978141 2 0 1 0 1
#> ERR978142 2 0 1 0 1
#> ERR978143 2 0 1 0 1
#> ERR978144 2 0 1 0 1
#> ERR978145 2 0 1 0 1
#> ERR978146 2 0 1 0 1
#> ERR978147 2 0 1 0 1
#> ERR978148 2 0 1 0 1
#> ERR978149 2 0 1 0 1
#> ERR978150 2 0 1 0 1
#> ERR978151 2 0 1 0 1
#> ERR978152 2 0 1 0 1
#> ERR978153 1 0 1 1 0
#> ERR978154 1 0 1 1 0
#> ERR978155 1 0 1 1 0
#> ERR978156 1 0 1 1 0
#> ERR978157 1 0 1 1 0
#> ERR978158 1 0 1 1 0
#> ERR978159 1 0 1 1 0
#> ERR978160 1 0 1 1 0
#> ERR978161 1 0 1 1 0
#> ERR978162 1 0 1 1 0
#> ERR978163 1 0 1 1 0
#> ERR978164 1 0 1 1 0
#> ERR978165 1 0 1 1 0
#> ERR978166 1 0 1 1 0
#> ERR978167 1 0 1 1 0
#> ERR978168 1 0 1 1 0
#> ERR978169 2 0 1 0 1
#> ERR978170 2 0 1 0 1
#> ERR978171 2 0 1 0 1
#> ERR978172 2 0 1 0 1
#> ERR978173 2 0 1 0 1
#> ERR978174 2 0 1 0 1
#> ERR978175 2 0 1 0 1
#> ERR978176 2 0 1 0 1
#> ERR978177 2 0 1 0 1
#> ERR978178 2 0 1 0 1
#> ERR978179 2 0 1 0 1
#> ERR978180 2 0 1 0 1
#> ERR978181 2 0 1 0 1
#> ERR978182 2 0 1 0 1
#> ERR978183 2 0 1 0 1
#> ERR978184 2 0 1 0 1
#> ERR978185 2 0 1 0 1
#> ERR978186 2 0 1 0 1
#> ERR978187 2 0 1 0 1
#> ERR978188 2 0 1 0 1
#> ERR978189 2 0 1 0 1
#> ERR978190 2 0 1 0 1
#> ERR978191 2 0 1 0 1
#> ERR978192 2 0 1 0 1
#> ERR978193 2 0 1 0 1
#> ERR978194 2 0 1 0 1
#> ERR978195 2 0 1 0 1
#> ERR978196 2 0 1 0 1
#> ERR978197 2 0 1 0 1
#> ERR978198 2 0 1 0 1
#> ERR978199 2 0 1 0 1
#> ERR978200 2 0 1 0 1
#> ERR978201 2 0 1 0 1
#> ERR978202 2 0 1 0 1
#> ERR978203 2 0 1 0 1
#> ERR978204 2 0 1 0 1
#> ERR978205 2 0 1 0 1
#> ERR978206 2 0 1 0 1
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#> ERR978214 2 0 1 0 1
#> ERR978215 2 0 1 0 1
#> ERR978216 2 0 1 0 1
#> ERR978217 2 0 1 0 1
#> ERR978218 2 0 1 0 1
#> ERR978219 2 0 1 0 1
#> ERR978220 2 0 1 0 1
#> ERR978221 2 0 1 0 1
#> ERR978222 2 0 1 0 1
#> ERR978223 2 0 1 0 1
#> ERR978224 2 0 1 0 1
#> ERR978225 2 0 1 0 1
#> ERR978226 2 0 1 0 1
#> ERR978227 1 0 1 1 0
#> ERR978228 1 0 1 1 0
#> ERR978229 1 0 1 1 0
#> ERR978230 1 0 1 1 0
#> ERR978231 1 0 1 1 0
#> ERR978232 1 0 1 1 0
#> ERR978233 1 0 1 1 0
#> ERR978234 1 0 1 1 0
#> ERR978235 1 0 1 1 0
#> ERR978236 1 0 1 1 0
#> ERR978237 1 0 1 1 0
#> ERR978238 1 0 1 1 0
#> ERR978239 1 0 1 1 0
#> ERR978240 1 0 1 1 0
#> ERR978241 2 0 1 0 1
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#> ERR978252 2 0 1 0 1
#> ERR978253 2 0 1 0 1
#> ERR978254 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.000 1.000 0 1.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000
#> ERR978123 3 0.000 0.917 0 0.000 1.000
#> ERR978124 3 0.000 0.917 0 0.000 1.000
#> ERR978125 3 0.000 0.917 0 0.000 1.000
#> ERR978126 3 0.000 0.917 0 0.000 1.000
#> ERR978127 3 0.000 0.917 0 0.000 1.000
#> ERR978128 3 0.000 0.917 0 0.000 1.000
#> ERR978129 3 0.000 0.917 0 0.000 1.000
#> ERR978130 3 0.000 0.917 0 0.000 1.000
#> ERR978131 3 0.000 0.917 0 0.000 1.000
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#> ERR978136 3 0.000 0.917 0 0.000 1.000
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#> ERR978138 3 0.000 0.917 0 0.000 1.000
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#> ERR978153 1 0.000 1.000 1 0.000 0.000
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#> ERR978166 1 0.000 1.000 1 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000
#> ERR978169 3 0.497 0.787 0 0.236 0.764
#> ERR978170 3 0.497 0.787 0 0.236 0.764
#> ERR978171 3 0.497 0.787 0 0.236 0.764
#> ERR978172 3 0.497 0.787 0 0.236 0.764
#> ERR978173 3 0.497 0.787 0 0.236 0.764
#> ERR978174 3 0.497 0.787 0 0.236 0.764
#> ERR978175 3 0.497 0.787 0 0.236 0.764
#> ERR978176 3 0.497 0.787 0 0.236 0.764
#> ERR978177 3 0.497 0.787 0 0.236 0.764
#> ERR978178 3 0.497 0.787 0 0.236 0.764
#> ERR978179 3 0.497 0.787 0 0.236 0.764
#> ERR978180 3 0.497 0.787 0 0.236 0.764
#> ERR978181 3 0.497 0.787 0 0.236 0.764
#> ERR978182 3 0.497 0.787 0 0.236 0.764
#> ERR978183 2 0.000 1.000 0 1.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000
#> ERR978197 3 0.000 0.917 0 0.000 1.000
#> ERR978198 3 0.000 0.917 0 0.000 1.000
#> ERR978199 3 0.000 0.917 0 0.000 1.000
#> ERR978200 3 0.000 0.917 0 0.000 1.000
#> ERR978201 3 0.000 0.917 0 0.000 1.000
#> ERR978202 3 0.000 0.917 0 0.000 1.000
#> ERR978203 3 0.000 0.917 0 0.000 1.000
#> ERR978204 3 0.000 0.917 0 0.000 1.000
#> ERR978205 3 0.000 0.917 0 0.000 1.000
#> ERR978206 3 0.000 0.917 0 0.000 1.000
#> ERR978207 3 0.000 0.917 0 0.000 1.000
#> ERR978208 3 0.000 0.917 0 0.000 1.000
#> ERR978209 3 0.000 0.917 0 0.000 1.000
#> ERR978210 3 0.000 0.917 0 0.000 1.000
#> ERR978211 3 0.000 0.917 0 0.000 1.000
#> ERR978212 3 0.000 0.917 0 0.000 1.000
#> ERR978213 3 0.000 0.917 0 0.000 1.000
#> ERR978214 3 0.000 0.917 0 0.000 1.000
#> ERR978215 3 0.000 0.917 0 0.000 1.000
#> ERR978216 3 0.000 0.917 0 0.000 1.000
#> ERR978217 3 0.000 0.917 0 0.000 1.000
#> ERR978218 3 0.000 0.917 0 0.000 1.000
#> ERR978219 3 0.000 0.917 0 0.000 1.000
#> ERR978220 3 0.000 0.917 0 0.000 1.000
#> ERR978221 3 0.000 0.917 0 0.000 1.000
#> ERR978222 3 0.000 0.917 0 0.000 1.000
#> ERR978223 3 0.000 0.917 0 0.000 1.000
#> ERR978224 3 0.000 0.917 0 0.000 1.000
#> ERR978225 3 0.000 0.917 0 0.000 1.000
#> ERR978226 3 0.000 0.917 0 0.000 1.000
#> ERR978227 1 0.000 1.000 1 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000
#> ERR978241 3 0.497 0.787 0 0.236 0.764
#> ERR978242 3 0.497 0.787 0 0.236 0.764
#> ERR978243 3 0.497 0.787 0 0.236 0.764
#> ERR978244 3 0.497 0.787 0 0.236 0.764
#> ERR978245 3 0.497 0.787 0 0.236 0.764
#> ERR978246 3 0.497 0.787 0 0.236 0.764
#> ERR978247 3 0.497 0.787 0 0.236 0.764
#> ERR978248 3 0.497 0.787 0 0.236 0.764
#> ERR978249 3 0.497 0.787 0 0.236 0.764
#> ERR978250 3 0.497 0.787 0 0.236 0.764
#> ERR978251 3 0.497 0.787 0 0.236 0.764
#> ERR978252 3 0.497 0.787 0 0.236 0.764
#> ERR978253 3 0.497 0.787 0 0.236 0.764
#> ERR978254 3 0.497 0.787 0 0.236 0.764
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978123 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978124 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978125 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978126 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978127 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978128 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978129 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978130 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978131 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978132 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978133 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978134 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978135 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978136 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978137 3 0.0469 0.991 0 0 0.988 0.012
#> ERR978138 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978139 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978140 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978141 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978142 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978143 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978144 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978145 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978146 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978147 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978148 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978149 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978150 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978151 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978152 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978153 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978169 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978170 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978171 4 0.0000 1.000 0 0 0.000 1.000
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#> ERR978183 2 0.0000 1.000 0 1 0.000 0.000
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#> ERR978191 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978192 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978193 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978194 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978195 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978196 2 0.0000 1.000 0 1 0.000 0.000
#> ERR978197 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978198 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978199 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978200 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978201 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978202 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978203 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978204 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978205 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978206 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978207 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978208 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978209 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978210 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978211 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978212 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978213 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978214 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978215 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978216 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978217 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978218 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978219 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978220 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978221 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978222 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978223 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978224 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978225 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978226 3 0.0000 0.997 0 0 1.000 0.000
#> ERR978227 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0 0.000 0.000
#> ERR978241 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978242 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978243 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978244 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978245 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978246 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978247 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978248 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978249 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978250 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978251 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978252 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978253 4 0.0000 1.000 0 0 0.000 1.000
#> ERR978254 4 0.0000 1.000 0 0 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978108 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978109 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978110 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978111 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978112 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978113 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978114 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978115 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978116 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978117 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978118 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978119 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978120 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978121 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978122 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978123 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978124 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978125 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978126 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978127 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978128 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978129 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978130 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978131 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978132 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978133 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978134 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978135 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978136 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978137 3 0.1121 0.975 0 0 0.956 0 0.044
#> ERR978138 3 0.0510 0.969 0 0 0.984 0 0.016
#> ERR978139 3 0.0510 0.969 0 0 0.984 0 0.016
#> ERR978140 3 0.0510 0.969 0 0 0.984 0 0.016
#> ERR978141 3 0.0510 0.969 0 0 0.984 0 0.016
#> ERR978142 3 0.0510 0.969 0 0 0.984 0 0.016
#> ERR978143 3 0.0510 0.969 0 0 0.984 0 0.016
#> ERR978144 3 0.0510 0.969 0 0 0.984 0 0.016
#> ERR978145 3 0.0510 0.969 0 0 0.984 0 0.016
#> ERR978146 3 0.0000 0.972 0 0 1.000 0 0.000
#> ERR978147 3 0.0000 0.972 0 0 1.000 0 0.000
#> ERR978148 3 0.0000 0.972 0 0 1.000 0 0.000
#> ERR978149 3 0.0000 0.972 0 0 1.000 0 0.000
#> ERR978150 3 0.0000 0.972 0 0 1.000 0 0.000
#> ERR978151 3 0.0000 0.972 0 0 1.000 0 0.000
#> ERR978152 3 0.0000 0.972 0 0 1.000 0 0.000
#> ERR978153 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978154 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978155 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978156 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978157 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978158 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978159 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978160 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978161 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978162 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978163 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978164 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978165 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978166 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978167 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978168 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978169 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978170 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978171 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978172 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978173 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978174 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978175 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978176 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978177 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978178 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978179 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978180 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978181 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978182 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978183 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978184 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978185 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978186 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978187 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978188 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978189 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978190 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978191 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978192 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978193 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978194 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978195 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978196 2 0.0000 1.000 0 1 0.000 0 0.000
#> ERR978197 5 0.0290 0.975 0 0 0.008 0 0.992
#> ERR978198 5 0.0290 0.975 0 0 0.008 0 0.992
#> ERR978199 5 0.0290 0.975 0 0 0.008 0 0.992
#> ERR978200 5 0.0290 0.975 0 0 0.008 0 0.992
#> ERR978201 5 0.0290 0.975 0 0 0.008 0 0.992
#> ERR978202 5 0.0290 0.975 0 0 0.008 0 0.992
#> ERR978203 5 0.0290 0.975 0 0 0.008 0 0.992
#> ERR978204 5 0.0162 0.976 0 0 0.004 0 0.996
#> ERR978205 5 0.0162 0.976 0 0 0.004 0 0.996
#> ERR978206 5 0.0162 0.976 0 0 0.004 0 0.996
#> ERR978207 5 0.0162 0.976 0 0 0.004 0 0.996
#> ERR978208 5 0.0162 0.976 0 0 0.004 0 0.996
#> ERR978209 5 0.0162 0.976 0 0 0.004 0 0.996
#> ERR978210 5 0.0162 0.976 0 0 0.004 0 0.996
#> ERR978211 5 0.0162 0.976 0 0 0.004 0 0.996
#> ERR978212 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978213 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978214 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978215 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978216 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978217 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978218 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978219 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978220 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978221 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978222 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978223 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978224 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978225 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978226 5 0.1043 0.977 0 0 0.040 0 0.960
#> ERR978227 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978228 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978229 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978230 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978231 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978232 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978233 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978234 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978235 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978236 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978237 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978238 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978239 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978240 1 0.0000 1.000 1 0 0.000 0 0.000
#> ERR978241 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978242 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978243 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978244 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978245 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978246 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978247 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978248 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978249 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978250 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978251 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978252 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978253 4 0.0000 1.000 0 0 0.000 1 0.000
#> ERR978254 4 0.0000 1.000 0 0 0.000 1 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 0.940 0 1.000 0.000 0.000 0.000 0.000
#> ERR978123 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978124 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978125 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978126 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978127 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978128 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978129 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978130 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978131 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978132 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978133 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978134 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978135 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978136 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978137 3 0.1204 0.970 0 0.000 0.944 0.056 0.000 0.000
#> ERR978138 3 0.0458 0.965 0 0.000 0.984 0.000 0.016 0.000
#> ERR978139 3 0.0458 0.965 0 0.000 0.984 0.000 0.016 0.000
#> ERR978140 3 0.0458 0.965 0 0.000 0.984 0.000 0.016 0.000
#> ERR978141 3 0.0458 0.965 0 0.000 0.984 0.000 0.016 0.000
#> ERR978142 3 0.0458 0.965 0 0.000 0.984 0.000 0.016 0.000
#> ERR978143 3 0.0458 0.965 0 0.000 0.984 0.000 0.016 0.000
#> ERR978144 3 0.0458 0.965 0 0.000 0.984 0.000 0.016 0.000
#> ERR978145 3 0.0458 0.965 0 0.000 0.984 0.000 0.016 0.000
#> ERR978146 3 0.0000 0.966 0 0.000 1.000 0.000 0.000 0.000
#> ERR978147 3 0.0000 0.966 0 0.000 1.000 0.000 0.000 0.000
#> ERR978148 3 0.0000 0.966 0 0.000 1.000 0.000 0.000 0.000
#> ERR978149 3 0.0000 0.966 0 0.000 1.000 0.000 0.000 0.000
#> ERR978150 3 0.0000 0.966 0 0.000 1.000 0.000 0.000 0.000
#> ERR978151 3 0.0000 0.966 0 0.000 1.000 0.000 0.000 0.000
#> ERR978152 3 0.0000 0.966 0 0.000 1.000 0.000 0.000 0.000
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978169 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978170 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978171 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978172 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978173 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978174 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978175 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978176 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978177 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978178 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978179 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978180 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978181 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978182 4 0.2854 1.000 0 0.000 0.000 0.792 0.000 0.208
#> ERR978183 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978184 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978185 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978186 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978187 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978188 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978189 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978190 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978191 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978192 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978193 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978194 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978195 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978196 2 0.2300 0.931 0 0.856 0.000 0.144 0.000 0.000
#> ERR978197 5 0.1349 0.969 0 0.000 0.004 0.056 0.940 0.000
#> ERR978198 5 0.1349 0.969 0 0.000 0.004 0.056 0.940 0.000
#> ERR978199 5 0.1349 0.969 0 0.000 0.004 0.056 0.940 0.000
#> ERR978200 5 0.1349 0.969 0 0.000 0.004 0.056 0.940 0.000
#> ERR978201 5 0.1349 0.969 0 0.000 0.004 0.056 0.940 0.000
#> ERR978202 5 0.1349 0.969 0 0.000 0.004 0.056 0.940 0.000
#> ERR978203 5 0.1349 0.969 0 0.000 0.004 0.056 0.940 0.000
#> ERR978204 5 0.1204 0.971 0 0.000 0.000 0.056 0.944 0.000
#> ERR978205 5 0.1204 0.971 0 0.000 0.000 0.056 0.944 0.000
#> ERR978206 5 0.1204 0.971 0 0.000 0.000 0.056 0.944 0.000
#> ERR978207 5 0.1204 0.971 0 0.000 0.000 0.056 0.944 0.000
#> ERR978208 5 0.1204 0.971 0 0.000 0.000 0.056 0.944 0.000
#> ERR978209 5 0.1204 0.971 0 0.000 0.000 0.056 0.944 0.000
#> ERR978210 5 0.1204 0.971 0 0.000 0.000 0.056 0.944 0.000
#> ERR978211 5 0.1204 0.971 0 0.000 0.000 0.056 0.944 0.000
#> ERR978212 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978213 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978214 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978215 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978216 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978217 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978218 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978219 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978220 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978221 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978222 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978223 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978224 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978225 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978226 5 0.0000 0.971 0 0.000 0.000 0.000 1.000 0.000
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978241 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978242 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978243 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978244 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978245 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978246 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978247 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978248 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978249 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978250 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978251 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978252 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978253 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
#> ERR978254 6 0.0000 1.000 0 0.000 0.000 0.000 0.000 1.000
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 14049 rows and 148 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.326 0.675 0.675
#> 3 3 0.716 0.824 0.894 0.891 0.686 0.534
#> 4 4 1.000 0.957 0.974 0.171 0.724 0.397
#> 5 5 1.000 0.975 0.988 0.114 0.917 0.702
#> 6 6 0.978 0.905 0.902 0.022 1.000 1.000
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2 4
There is also optional best \(k\) = 2 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
#> ERR978109 2 0 1 0 1
#> ERR978110 2 0 1 0 1
#> ERR978111 2 0 1 0 1
#> ERR978112 2 0 1 0 1
#> ERR978113 2 0 1 0 1
#> ERR978114 2 0 1 0 1
#> ERR978115 2 0 1 0 1
#> ERR978116 2 0 1 0 1
#> ERR978117 2 0 1 0 1
#> ERR978118 2 0 1 0 1
#> ERR978119 2 0 1 0 1
#> ERR978120 2 0 1 0 1
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#> ERR978253 2 0 1 0 1
#> ERR978254 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.000 0.742 0 1.000 0.000
#> ERR978108 2 0.000 0.742 0 1.000 0.000
#> ERR978109 2 0.000 0.742 0 1.000 0.000
#> ERR978110 2 0.000 0.742 0 1.000 0.000
#> ERR978111 2 0.000 0.742 0 1.000 0.000
#> ERR978112 2 0.000 0.742 0 1.000 0.000
#> ERR978113 2 0.000 0.742 0 1.000 0.000
#> ERR978114 2 0.000 0.742 0 1.000 0.000
#> ERR978115 2 0.000 0.742 0 1.000 0.000
#> ERR978116 2 0.000 0.742 0 1.000 0.000
#> ERR978117 2 0.000 0.742 0 1.000 0.000
#> ERR978118 2 0.000 0.742 0 1.000 0.000
#> ERR978119 2 0.000 0.742 0 1.000 0.000
#> ERR978120 2 0.000 0.742 0 1.000 0.000
#> ERR978121 2 0.000 0.742 0 1.000 0.000
#> ERR978122 2 0.000 0.742 0 1.000 0.000
#> ERR978123 3 0.000 0.962 0 0.000 1.000
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#> ERR978129 3 0.000 0.962 0 0.000 1.000
#> ERR978130 3 0.000 0.962 0 0.000 1.000
#> ERR978131 3 0.581 0.208 0 0.336 0.664
#> ERR978132 3 0.465 0.618 0 0.208 0.792
#> ERR978133 3 0.319 0.811 0 0.112 0.888
#> ERR978134 3 0.280 0.843 0 0.092 0.908
#> ERR978135 3 0.271 0.849 0 0.088 0.912
#> ERR978136 3 0.450 0.646 0 0.196 0.804
#> ERR978137 3 0.543 0.402 0 0.284 0.716
#> ERR978138 3 0.000 0.962 0 0.000 1.000
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#> ERR978196 2 0.000 0.742 0 1.000 0.000
#> ERR978197 2 0.614 0.629 0 0.596 0.404
#> ERR978198 2 0.621 0.590 0 0.572 0.428
#> ERR978199 2 0.623 0.575 0 0.564 0.436
#> ERR978200 2 0.624 0.567 0 0.560 0.440
#> ERR978201 2 0.621 0.590 0 0.572 0.428
#> ERR978202 2 0.618 0.610 0 0.584 0.416
#> ERR978203 2 0.614 0.629 0 0.596 0.404
#> ERR978204 2 0.610 0.644 0 0.608 0.392
#> ERR978205 2 0.611 0.639 0 0.604 0.396
#> ERR978206 2 0.613 0.634 0 0.600 0.400
#> ERR978207 2 0.613 0.634 0 0.600 0.400
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#> ERR978211 2 0.610 0.644 0 0.608 0.392
#> ERR978212 2 0.599 0.663 0 0.632 0.368
#> ERR978213 2 0.604 0.655 0 0.620 0.380
#> ERR978214 2 0.604 0.655 0 0.620 0.380
#> ERR978215 2 0.608 0.648 0 0.612 0.388
#> ERR978216 2 0.603 0.658 0 0.624 0.376
#> ERR978217 2 0.603 0.658 0 0.624 0.376
#> ERR978218 2 0.590 0.670 0 0.648 0.352
#> ERR978219 2 0.599 0.662 0 0.632 0.368
#> ERR978220 2 0.608 0.648 0 0.612 0.388
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#> ERR978222 2 0.610 0.644 0 0.608 0.392
#> ERR978223 2 0.606 0.651 0 0.616 0.384
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#> ERR978225 2 0.601 0.660 0 0.628 0.372
#> ERR978226 2 0.595 0.666 0 0.640 0.360
#> ERR978227 1 0.000 1.000 1 0.000 0.000
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#> ERR978246 3 0.000 0.962 0 0.000 1.000
#> ERR978247 3 0.000 0.962 0 0.000 1.000
#> ERR978248 2 0.514 0.683 0 0.748 0.252
#> ERR978249 2 0.601 0.569 0 0.628 0.372
#> ERR978250 2 0.625 0.427 0 0.556 0.444
#> ERR978251 2 0.626 0.416 0 0.552 0.448
#> ERR978252 2 0.621 0.467 0 0.572 0.428
#> ERR978253 2 0.595 0.579 0 0.640 0.360
#> ERR978254 2 0.540 0.673 0 0.720 0.280
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978123 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978124 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978125 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978126 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978127 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978128 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978129 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978130 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978131 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978132 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978133 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978134 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978135 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978136 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978137 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978138 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978139 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978140 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978141 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978142 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978143 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978144 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978145 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978146 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978147 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978148 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978149 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978150 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978151 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978152 3 0.0000 0.976 0 0.000 1.000 0.000
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978170 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978171 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978172 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978173 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978174 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978175 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978176 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978177 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978178 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978179 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978180 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978181 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978182 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978183 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978184 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978185 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978186 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978187 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978188 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978189 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978190 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978191 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978192 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978193 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978194 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978195 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978196 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978197 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978198 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978199 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978200 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978201 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978202 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978203 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978204 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978205 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978206 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978207 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978208 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978209 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978210 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978211 3 0.1211 0.977 0 0.000 0.960 0.040
#> ERR978212 3 0.0657 0.970 0 0.012 0.984 0.004
#> ERR978213 3 0.0524 0.972 0 0.008 0.988 0.004
#> ERR978214 3 0.0524 0.972 0 0.004 0.988 0.008
#> ERR978215 3 0.0804 0.968 0 0.008 0.980 0.012
#> ERR978216 3 0.0657 0.970 0 0.012 0.984 0.004
#> ERR978217 3 0.0657 0.970 0 0.012 0.984 0.004
#> ERR978218 3 0.0657 0.970 0 0.012 0.984 0.004
#> ERR978219 3 0.0188 0.975 0 0.000 0.996 0.004
#> ERR978220 3 0.0188 0.975 0 0.000 0.996 0.004
#> ERR978221 3 0.0188 0.975 0 0.000 0.996 0.004
#> ERR978222 3 0.0188 0.975 0 0.000 0.996 0.004
#> ERR978223 3 0.0188 0.975 0 0.000 0.996 0.004
#> ERR978224 3 0.0188 0.975 0 0.000 0.996 0.004
#> ERR978225 3 0.0188 0.975 0 0.000 0.996 0.004
#> ERR978226 3 0.0188 0.975 0 0.000 0.996 0.004
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978241 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978242 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978243 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978244 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978245 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978246 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978247 4 0.0000 0.907 0 0.000 0.000 1.000
#> ERR978248 4 0.5872 0.465 0 0.384 0.040 0.576
#> ERR978249 4 0.5639 0.581 0 0.324 0.040 0.636
#> ERR978250 4 0.5442 0.634 0 0.288 0.040 0.672
#> ERR978251 4 0.5168 0.682 0 0.248 0.040 0.712
#> ERR978252 4 0.5489 0.623 0 0.296 0.040 0.664
#> ERR978253 4 0.5658 0.574 0 0.328 0.040 0.632
#> ERR978254 4 0.5894 0.447 0 0.392 0.040 0.568
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978123 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978124 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978125 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978126 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978127 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978128 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978129 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978130 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978131 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978132 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978133 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978134 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978135 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978136 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978137 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978138 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978139 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978140 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978141 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978142 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978143 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978144 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978145 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978146 5 0.120 0.955 0 0.000 0.048 0.000 0.952
#> ERR978147 5 0.134 0.948 0 0.000 0.056 0.000 0.944
#> ERR978148 5 0.127 0.952 0 0.000 0.052 0.000 0.948
#> ERR978149 5 0.120 0.955 0 0.000 0.048 0.000 0.952
#> ERR978150 5 0.127 0.952 0 0.000 0.052 0.000 0.948
#> ERR978151 5 0.154 0.937 0 0.000 0.068 0.000 0.932
#> ERR978152 5 0.154 0.937 0 0.000 0.068 0.000 0.932
#> ERR978153 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978169 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978170 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978171 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978172 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978173 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978174 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978175 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978176 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978177 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978178 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978179 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978180 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978181 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978182 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978183 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978197 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978198 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978199 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978200 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978201 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978202 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978203 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978204 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978205 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978206 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978207 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978208 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978209 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978210 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978211 3 0.000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978212 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978213 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978214 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978215 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978216 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978217 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978218 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978219 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978220 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978221 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978222 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978223 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978224 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978225 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978226 5 0.000 0.985 0 0.000 0.000 0.000 1.000
#> ERR978227 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978241 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978242 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978243 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978244 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978245 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978246 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978247 4 0.000 0.948 0 0.000 0.000 1.000 0.000
#> ERR978248 4 0.446 0.390 0 0.008 0.000 0.588 0.404
#> ERR978249 4 0.340 0.744 0 0.004 0.000 0.780 0.216
#> ERR978250 4 0.127 0.915 0 0.000 0.000 0.948 0.052
#> ERR978251 4 0.088 0.929 0 0.000 0.000 0.968 0.032
#> ERR978252 4 0.164 0.904 0 0.004 0.000 0.932 0.064
#> ERR978253 4 0.364 0.699 0 0.004 0.000 0.748 0.248
#> ERR978254 4 0.444 0.482 0 0.012 0.000 0.624 0.364
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978108 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978109 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978110 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978111 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978112 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978113 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978114 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978115 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978116 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978117 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978118 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978119 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978120 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978121 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978122 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978123 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978124 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978125 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978126 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978127 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978128 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978129 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978130 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978131 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978132 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978133 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978134 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978135 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978136 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978137 3 0.0000 0.993 0 0 1.000 0.000 0.000 NA
#> ERR978138 5 0.0000 0.744 0 0 0.000 0.000 1.000 NA
#> ERR978139 5 0.0405 0.744 0 0 0.000 0.004 0.988 NA
#> ERR978140 5 0.0260 0.742 0 0 0.000 0.008 0.992 NA
#> ERR978141 5 0.0260 0.742 0 0 0.000 0.008 0.992 NA
#> ERR978142 5 0.0260 0.742 0 0 0.000 0.008 0.992 NA
#> ERR978143 5 0.0260 0.742 0 0 0.000 0.008 0.992 NA
#> ERR978144 5 0.0260 0.745 0 0 0.000 0.000 0.992 NA
#> ERR978145 5 0.0260 0.745 0 0 0.000 0.000 0.992 NA
#> ERR978146 5 0.1262 0.731 0 0 0.008 0.020 0.956 NA
#> ERR978147 5 0.1262 0.731 0 0 0.008 0.020 0.956 NA
#> ERR978148 5 0.1262 0.731 0 0 0.008 0.020 0.956 NA
#> ERR978149 5 0.1262 0.731 0 0 0.008 0.020 0.956 NA
#> ERR978150 5 0.1262 0.731 0 0 0.008 0.020 0.956 NA
#> ERR978151 5 0.1262 0.731 0 0 0.008 0.020 0.956 NA
#> ERR978152 5 0.1262 0.731 0 0 0.008 0.020 0.956 NA
#> ERR978153 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978154 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978155 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978156 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978157 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978158 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978159 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978160 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978161 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978162 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978163 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978164 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978165 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978166 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978167 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978168 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978169 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978170 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978171 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978172 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978173 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978174 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978175 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978176 4 0.3887 0.859 0 0 0.000 0.632 0.008 NA
#> ERR978177 4 0.3887 0.859 0 0 0.000 0.632 0.008 NA
#> ERR978178 4 0.3887 0.859 0 0 0.000 0.632 0.008 NA
#> ERR978179 4 0.3887 0.859 0 0 0.000 0.632 0.008 NA
#> ERR978180 4 0.3887 0.859 0 0 0.000 0.632 0.008 NA
#> ERR978181 4 0.3887 0.859 0 0 0.000 0.632 0.008 NA
#> ERR978182 4 0.3887 0.859 0 0 0.000 0.632 0.008 NA
#> ERR978183 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978184 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978185 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978186 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978187 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978188 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978189 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978190 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978191 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978192 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978193 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978194 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978195 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978196 2 0.0000 1.000 0 1 0.000 0.000 0.000 NA
#> ERR978197 3 0.0260 0.992 0 0 0.992 0.000 0.000 NA
#> ERR978198 3 0.0260 0.992 0 0 0.992 0.000 0.000 NA
#> ERR978199 3 0.0260 0.992 0 0 0.992 0.000 0.000 NA
#> ERR978200 3 0.0260 0.992 0 0 0.992 0.000 0.000 NA
#> ERR978201 3 0.0260 0.992 0 0 0.992 0.000 0.000 NA
#> ERR978202 3 0.0260 0.992 0 0 0.992 0.000 0.000 NA
#> ERR978203 3 0.0260 0.992 0 0 0.992 0.000 0.000 NA
#> ERR978204 3 0.0725 0.985 0 0 0.976 0.012 0.000 NA
#> ERR978205 3 0.0725 0.985 0 0 0.976 0.012 0.000 NA
#> ERR978206 3 0.0725 0.985 0 0 0.976 0.012 0.000 NA
#> ERR978207 3 0.0725 0.985 0 0 0.976 0.012 0.000 NA
#> ERR978208 3 0.0725 0.985 0 0 0.976 0.012 0.000 NA
#> ERR978209 3 0.0725 0.985 0 0 0.976 0.012 0.000 NA
#> ERR978210 3 0.0725 0.985 0 0 0.976 0.012 0.000 NA
#> ERR978211 3 0.0725 0.985 0 0 0.976 0.012 0.000 NA
#> ERR978212 5 0.5962 0.694 0 0 0.000 0.228 0.424 NA
#> ERR978213 5 0.5890 0.710 0 0 0.000 0.212 0.448 NA
#> ERR978214 5 0.5771 0.724 0 0 0.000 0.188 0.476 NA
#> ERR978215 5 0.5688 0.730 0 0 0.000 0.176 0.496 NA
#> ERR978216 5 0.5849 0.717 0 0 0.000 0.204 0.460 NA
#> ERR978217 5 0.5890 0.710 0 0 0.000 0.212 0.448 NA
#> ERR978218 5 0.5975 0.691 0 0 0.000 0.232 0.420 NA
#> ERR978219 5 0.5763 0.725 0 0 0.000 0.188 0.480 NA
#> ERR978220 5 0.5688 0.730 0 0 0.000 0.176 0.496 NA
#> ERR978221 5 0.5590 0.733 0 0 0.000 0.160 0.512 NA
#> ERR978222 5 0.5498 0.735 0 0 0.000 0.148 0.528 NA
#> ERR978223 5 0.5487 0.735 0 0 0.000 0.148 0.532 NA
#> ERR978224 5 0.5665 0.731 0 0 0.000 0.172 0.500 NA
#> ERR978225 5 0.5849 0.717 0 0 0.000 0.204 0.460 NA
#> ERR978226 5 0.5811 0.721 0 0 0.000 0.196 0.468 NA
#> ERR978227 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978228 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978229 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978230 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978231 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978232 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978233 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978234 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978235 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978236 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978237 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978238 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978239 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978240 1 0.0000 1.000 1 0 0.000 0.000 0.000 NA
#> ERR978241 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978242 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978243 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978244 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978245 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978246 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978247 4 0.3838 0.871 0 0 0.000 0.552 0.000 NA
#> ERR978248 4 0.2070 0.536 0 0 0.000 0.892 0.008 NA
#> ERR978249 4 0.1524 0.584 0 0 0.000 0.932 0.008 NA
#> ERR978250 4 0.0622 0.629 0 0 0.000 0.980 0.008 NA
#> ERR978251 4 0.0405 0.642 0 0 0.000 0.988 0.008 NA
#> ERR978252 4 0.0622 0.629 0 0 0.000 0.980 0.008 NA
#> ERR978253 4 0.1524 0.585 0 0 0.000 0.932 0.008 NA
#> ERR978254 4 0.1970 0.545 0 0 0.000 0.900 0.008 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["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 14049 rows and 148 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 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 0.198 0.748 0.784 0.3844 0.520 0.520
#> 3 3 0.569 0.775 0.846 0.4856 0.923 0.852
#> 4 4 0.831 0.859 0.910 0.2609 0.835 0.626
#> 5 5 0.899 0.957 0.877 0.0861 0.917 0.702
#> 6 6 0.938 0.958 0.969 0.0451 0.982 0.907
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
#> ERR978107 2 0.000 0.755 0.000 1.000
#> ERR978108 2 0.000 0.755 0.000 1.000
#> ERR978109 2 0.000 0.755 0.000 1.000
#> ERR978110 2 0.000 0.755 0.000 1.000
#> ERR978111 2 0.000 0.755 0.000 1.000
#> ERR978112 2 0.000 0.755 0.000 1.000
#> ERR978113 2 0.000 0.755 0.000 1.000
#> ERR978114 2 0.000 0.755 0.000 1.000
#> ERR978115 2 0.000 0.755 0.000 1.000
#> ERR978116 2 0.000 0.755 0.000 1.000
#> ERR978117 2 0.000 0.755 0.000 1.000
#> ERR978118 2 0.000 0.755 0.000 1.000
#> ERR978119 2 0.000 0.755 0.000 1.000
#> ERR978120 2 0.000 0.755 0.000 1.000
#> ERR978121 2 0.000 0.755 0.000 1.000
#> ERR978122 2 0.000 0.755 0.000 1.000
#> ERR978123 2 0.839 0.745 0.268 0.732
#> ERR978124 2 0.839 0.745 0.268 0.732
#> ERR978125 2 0.839 0.745 0.268 0.732
#> ERR978126 2 0.839 0.745 0.268 0.732
#> ERR978127 2 0.839 0.745 0.268 0.732
#> ERR978128 2 0.839 0.745 0.268 0.732
#> ERR978129 2 0.839 0.745 0.268 0.732
#> ERR978130 2 0.839 0.745 0.268 0.732
#> ERR978131 2 0.839 0.745 0.268 0.732
#> ERR978132 2 0.839 0.745 0.268 0.732
#> ERR978133 2 0.839 0.745 0.268 0.732
#> ERR978134 2 0.839 0.745 0.268 0.732
#> ERR978135 2 0.839 0.745 0.268 0.732
#> ERR978136 2 0.839 0.745 0.268 0.732
#> ERR978137 2 0.839 0.745 0.268 0.732
#> ERR978138 2 0.644 0.824 0.164 0.836
#> ERR978139 2 0.644 0.824 0.164 0.836
#> ERR978140 2 0.644 0.824 0.164 0.836
#> ERR978141 2 0.644 0.824 0.164 0.836
#> ERR978142 2 0.644 0.824 0.164 0.836
#> ERR978143 2 0.644 0.824 0.164 0.836
#> ERR978144 2 0.644 0.824 0.164 0.836
#> ERR978145 2 0.644 0.824 0.164 0.836
#> ERR978146 2 0.644 0.824 0.164 0.836
#> ERR978147 2 0.644 0.824 0.164 0.836
#> ERR978148 2 0.644 0.824 0.164 0.836
#> ERR978149 2 0.644 0.824 0.164 0.836
#> ERR978150 2 0.644 0.824 0.164 0.836
#> ERR978151 2 0.644 0.824 0.164 0.836
#> ERR978152 2 0.644 0.824 0.164 0.836
#> ERR978153 1 0.722 0.755 0.800 0.200
#> ERR978154 1 0.722 0.755 0.800 0.200
#> ERR978155 1 0.722 0.755 0.800 0.200
#> ERR978156 1 0.722 0.755 0.800 0.200
#> ERR978157 1 0.722 0.755 0.800 0.200
#> ERR978158 1 0.722 0.755 0.800 0.200
#> ERR978159 1 0.722 0.755 0.800 0.200
#> ERR978160 1 0.722 0.755 0.800 0.200
#> ERR978161 1 0.722 0.755 0.800 0.200
#> ERR978162 1 0.722 0.755 0.800 0.200
#> ERR978163 1 0.722 0.755 0.800 0.200
#> ERR978164 1 0.722 0.755 0.800 0.200
#> ERR978165 1 0.722 0.755 0.800 0.200
#> ERR978166 1 0.722 0.755 0.800 0.200
#> ERR978167 1 0.722 0.755 0.800 0.200
#> ERR978168 1 0.722 0.755 0.800 0.200
#> ERR978169 1 0.722 0.703 0.800 0.200
#> ERR978170 1 0.722 0.703 0.800 0.200
#> ERR978171 1 0.722 0.703 0.800 0.200
#> ERR978172 1 0.722 0.703 0.800 0.200
#> ERR978173 1 0.722 0.703 0.800 0.200
#> ERR978174 1 0.722 0.703 0.800 0.200
#> ERR978175 1 0.722 0.703 0.800 0.200
#> ERR978176 1 0.866 0.612 0.712 0.288
#> ERR978177 1 0.866 0.612 0.712 0.288
#> ERR978178 1 0.866 0.612 0.712 0.288
#> ERR978179 1 0.866 0.612 0.712 0.288
#> ERR978180 1 0.866 0.612 0.712 0.288
#> ERR978181 1 0.866 0.612 0.712 0.288
#> ERR978182 1 0.866 0.612 0.712 0.288
#> ERR978183 2 0.000 0.755 0.000 1.000
#> ERR978184 2 0.000 0.755 0.000 1.000
#> ERR978185 2 0.000 0.755 0.000 1.000
#> ERR978186 2 0.000 0.755 0.000 1.000
#> ERR978187 2 0.000 0.755 0.000 1.000
#> ERR978188 2 0.000 0.755 0.000 1.000
#> ERR978189 2 0.000 0.755 0.000 1.000
#> ERR978190 2 0.000 0.755 0.000 1.000
#> ERR978191 2 0.000 0.755 0.000 1.000
#> ERR978192 2 0.000 0.755 0.000 1.000
#> ERR978193 2 0.000 0.755 0.000 1.000
#> ERR978194 2 0.000 0.755 0.000 1.000
#> ERR978195 2 0.000 0.755 0.000 1.000
#> ERR978196 2 0.000 0.755 0.000 1.000
#> ERR978197 2 0.839 0.745 0.268 0.732
#> ERR978198 2 0.839 0.745 0.268 0.732
#> ERR978199 2 0.839 0.745 0.268 0.732
#> ERR978200 2 0.839 0.745 0.268 0.732
#> ERR978201 2 0.839 0.745 0.268 0.732
#> ERR978202 2 0.839 0.745 0.268 0.732
#> ERR978203 2 0.839 0.745 0.268 0.732
#> ERR978204 2 0.839 0.745 0.268 0.732
#> ERR978205 2 0.839 0.745 0.268 0.732
#> ERR978206 2 0.839 0.745 0.268 0.732
#> ERR978207 2 0.839 0.745 0.268 0.732
#> ERR978208 2 0.839 0.745 0.268 0.732
#> ERR978209 2 0.839 0.745 0.268 0.732
#> ERR978210 2 0.839 0.745 0.268 0.732
#> ERR978211 2 0.839 0.745 0.268 0.732
#> ERR978212 2 0.644 0.824 0.164 0.836
#> ERR978213 2 0.644 0.824 0.164 0.836
#> ERR978214 2 0.644 0.824 0.164 0.836
#> ERR978215 2 0.644 0.824 0.164 0.836
#> ERR978216 2 0.644 0.824 0.164 0.836
#> ERR978217 2 0.644 0.824 0.164 0.836
#> ERR978218 2 0.644 0.824 0.164 0.836
#> ERR978219 2 0.644 0.824 0.164 0.836
#> ERR978220 2 0.644 0.824 0.164 0.836
#> ERR978221 2 0.644 0.824 0.164 0.836
#> ERR978222 2 0.644 0.824 0.164 0.836
#> ERR978223 2 0.644 0.824 0.164 0.836
#> ERR978224 2 0.644 0.824 0.164 0.836
#> ERR978225 2 0.644 0.824 0.164 0.836
#> ERR978226 2 0.644 0.824 0.164 0.836
#> ERR978227 1 0.722 0.755 0.800 0.200
#> ERR978228 1 0.722 0.755 0.800 0.200
#> ERR978229 1 0.722 0.755 0.800 0.200
#> ERR978230 1 0.722 0.755 0.800 0.200
#> ERR978231 1 0.722 0.755 0.800 0.200
#> ERR978232 1 0.722 0.755 0.800 0.200
#> ERR978233 1 0.722 0.755 0.800 0.200
#> ERR978234 1 0.722 0.755 0.800 0.200
#> ERR978235 1 0.722 0.755 0.800 0.200
#> ERR978236 1 0.722 0.755 0.800 0.200
#> ERR978237 1 0.722 0.755 0.800 0.200
#> ERR978238 1 0.722 0.755 0.800 0.200
#> ERR978239 1 0.722 0.755 0.800 0.200
#> ERR978240 1 0.722 0.755 0.800 0.200
#> ERR978241 1 0.722 0.703 0.800 0.200
#> ERR978242 1 0.722 0.703 0.800 0.200
#> ERR978243 1 0.722 0.703 0.800 0.200
#> ERR978244 1 0.722 0.703 0.800 0.200
#> ERR978245 1 0.722 0.703 0.800 0.200
#> ERR978246 1 0.722 0.703 0.800 0.200
#> ERR978247 1 0.722 0.703 0.800 0.200
#> ERR978248 1 0.891 0.600 0.692 0.308
#> ERR978249 1 0.891 0.600 0.692 0.308
#> ERR978250 1 0.891 0.600 0.692 0.308
#> ERR978251 1 0.891 0.600 0.692 0.308
#> ERR978252 1 0.891 0.600 0.692 0.308
#> ERR978253 1 0.891 0.600 0.692 0.308
#> ERR978254 1 0.891 0.600 0.692 0.308
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.510 0.681 0.000 0.752 0.248
#> ERR978108 2 0.510 0.681 0.000 0.752 0.248
#> ERR978109 2 0.510 0.681 0.000 0.752 0.248
#> ERR978110 2 0.510 0.681 0.000 0.752 0.248
#> ERR978111 2 0.510 0.681 0.000 0.752 0.248
#> ERR978112 2 0.510 0.681 0.000 0.752 0.248
#> ERR978113 2 0.510 0.681 0.000 0.752 0.248
#> ERR978114 2 0.510 0.681 0.000 0.752 0.248
#> ERR978115 2 0.510 0.681 0.000 0.752 0.248
#> ERR978116 2 0.510 0.681 0.000 0.752 0.248
#> ERR978117 2 0.510 0.681 0.000 0.752 0.248
#> ERR978118 2 0.510 0.681 0.000 0.752 0.248
#> ERR978119 2 0.510 0.681 0.000 0.752 0.248
#> ERR978120 2 0.510 0.681 0.000 0.752 0.248
#> ERR978121 2 0.510 0.681 0.000 0.752 0.248
#> ERR978122 2 0.510 0.681 0.000 0.752 0.248
#> ERR978123 2 0.484 0.553 0.000 0.776 0.224
#> ERR978124 2 0.484 0.553 0.000 0.776 0.224
#> ERR978125 2 0.484 0.553 0.000 0.776 0.224
#> ERR978126 2 0.484 0.553 0.000 0.776 0.224
#> ERR978127 2 0.484 0.553 0.000 0.776 0.224
#> ERR978128 2 0.484 0.553 0.000 0.776 0.224
#> ERR978129 2 0.484 0.553 0.000 0.776 0.224
#> ERR978130 2 0.484 0.553 0.000 0.776 0.224
#> ERR978131 2 0.484 0.553 0.000 0.776 0.224
#> ERR978132 2 0.484 0.553 0.000 0.776 0.224
#> ERR978133 2 0.484 0.553 0.000 0.776 0.224
#> ERR978134 2 0.484 0.553 0.000 0.776 0.224
#> ERR978135 2 0.484 0.553 0.000 0.776 0.224
#> ERR978136 2 0.484 0.553 0.000 0.776 0.224
#> ERR978137 2 0.484 0.553 0.000 0.776 0.224
#> ERR978138 2 0.000 0.741 0.000 1.000 0.000
#> ERR978139 2 0.000 0.741 0.000 1.000 0.000
#> ERR978140 2 0.000 0.741 0.000 1.000 0.000
#> ERR978141 2 0.000 0.741 0.000 1.000 0.000
#> ERR978142 2 0.000 0.741 0.000 1.000 0.000
#> ERR978143 2 0.000 0.741 0.000 1.000 0.000
#> ERR978144 2 0.000 0.741 0.000 1.000 0.000
#> ERR978145 2 0.000 0.741 0.000 1.000 0.000
#> ERR978146 2 0.000 0.741 0.000 1.000 0.000
#> ERR978147 2 0.000 0.741 0.000 1.000 0.000
#> ERR978148 2 0.000 0.741 0.000 1.000 0.000
#> ERR978149 2 0.000 0.741 0.000 1.000 0.000
#> ERR978150 2 0.000 0.741 0.000 1.000 0.000
#> ERR978151 2 0.000 0.741 0.000 1.000 0.000
#> ERR978152 2 0.000 0.741 0.000 1.000 0.000
#> ERR978153 1 0.000 1.000 1.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1.000 0.000 0.000
#> ERR978169 3 0.528 0.919 0.004 0.244 0.752
#> ERR978170 3 0.528 0.919 0.004 0.244 0.752
#> ERR978171 3 0.528 0.919 0.004 0.244 0.752
#> ERR978172 3 0.528 0.919 0.004 0.244 0.752
#> ERR978173 3 0.528 0.919 0.004 0.244 0.752
#> ERR978174 3 0.528 0.919 0.004 0.244 0.752
#> ERR978175 3 0.528 0.919 0.004 0.244 0.752
#> ERR978176 3 0.603 0.910 0.004 0.336 0.660
#> ERR978177 3 0.603 0.910 0.004 0.336 0.660
#> ERR978178 3 0.603 0.910 0.004 0.336 0.660
#> ERR978179 3 0.603 0.910 0.004 0.336 0.660
#> ERR978180 3 0.603 0.910 0.004 0.336 0.660
#> ERR978181 3 0.603 0.910 0.004 0.336 0.660
#> ERR978182 3 0.603 0.910 0.004 0.336 0.660
#> ERR978183 2 0.510 0.681 0.000 0.752 0.248
#> ERR978184 2 0.510 0.681 0.000 0.752 0.248
#> ERR978185 2 0.510 0.681 0.000 0.752 0.248
#> ERR978186 2 0.510 0.681 0.000 0.752 0.248
#> ERR978187 2 0.510 0.681 0.000 0.752 0.248
#> ERR978188 2 0.510 0.681 0.000 0.752 0.248
#> ERR978189 2 0.510 0.681 0.000 0.752 0.248
#> ERR978190 2 0.510 0.681 0.000 0.752 0.248
#> ERR978191 2 0.510 0.681 0.000 0.752 0.248
#> ERR978192 2 0.510 0.681 0.000 0.752 0.248
#> ERR978193 2 0.510 0.681 0.000 0.752 0.248
#> ERR978194 2 0.510 0.681 0.000 0.752 0.248
#> ERR978195 2 0.510 0.681 0.000 0.752 0.248
#> ERR978196 2 0.510 0.681 0.000 0.752 0.248
#> ERR978197 2 0.484 0.553 0.000 0.776 0.224
#> ERR978198 2 0.484 0.553 0.000 0.776 0.224
#> ERR978199 2 0.484 0.553 0.000 0.776 0.224
#> ERR978200 2 0.484 0.553 0.000 0.776 0.224
#> ERR978201 2 0.484 0.553 0.000 0.776 0.224
#> ERR978202 2 0.484 0.553 0.000 0.776 0.224
#> ERR978203 2 0.484 0.553 0.000 0.776 0.224
#> ERR978204 2 0.484 0.553 0.000 0.776 0.224
#> ERR978205 2 0.484 0.553 0.000 0.776 0.224
#> ERR978206 2 0.484 0.553 0.000 0.776 0.224
#> ERR978207 2 0.484 0.553 0.000 0.776 0.224
#> ERR978208 2 0.484 0.553 0.000 0.776 0.224
#> ERR978209 2 0.484 0.553 0.000 0.776 0.224
#> ERR978210 2 0.484 0.553 0.000 0.776 0.224
#> ERR978211 2 0.484 0.553 0.000 0.776 0.224
#> ERR978212 2 0.000 0.741 0.000 1.000 0.000
#> ERR978213 2 0.000 0.741 0.000 1.000 0.000
#> ERR978214 2 0.000 0.741 0.000 1.000 0.000
#> ERR978215 2 0.000 0.741 0.000 1.000 0.000
#> ERR978216 2 0.000 0.741 0.000 1.000 0.000
#> ERR978217 2 0.000 0.741 0.000 1.000 0.000
#> ERR978218 2 0.000 0.741 0.000 1.000 0.000
#> ERR978219 2 0.000 0.741 0.000 1.000 0.000
#> ERR978220 2 0.000 0.741 0.000 1.000 0.000
#> ERR978221 2 0.000 0.741 0.000 1.000 0.000
#> ERR978222 2 0.000 0.741 0.000 1.000 0.000
#> ERR978223 2 0.000 0.741 0.000 1.000 0.000
#> ERR978224 2 0.000 0.741 0.000 1.000 0.000
#> ERR978225 2 0.000 0.741 0.000 1.000 0.000
#> ERR978226 2 0.000 0.741 0.000 1.000 0.000
#> ERR978227 1 0.000 1.000 1.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1.000 0.000 0.000
#> ERR978241 3 0.528 0.919 0.004 0.244 0.752
#> ERR978242 3 0.528 0.919 0.004 0.244 0.752
#> ERR978243 3 0.528 0.919 0.004 0.244 0.752
#> ERR978244 3 0.528 0.919 0.004 0.244 0.752
#> ERR978245 3 0.528 0.919 0.004 0.244 0.752
#> ERR978246 3 0.528 0.919 0.004 0.244 0.752
#> ERR978247 3 0.528 0.919 0.004 0.244 0.752
#> ERR978248 3 0.615 0.890 0.004 0.356 0.640
#> ERR978249 3 0.615 0.890 0.004 0.356 0.640
#> ERR978250 3 0.615 0.890 0.004 0.356 0.640
#> ERR978251 3 0.615 0.890 0.004 0.356 0.640
#> ERR978252 3 0.615 0.890 0.004 0.356 0.640
#> ERR978253 3 0.615 0.890 0.004 0.356 0.640
#> ERR978254 3 0.615 0.890 0.004 0.356 0.640
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978108 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978109 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978110 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978111 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978112 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978113 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978114 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978115 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978116 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978117 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978118 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978119 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978120 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978121 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978122 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978123 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978124 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978125 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978126 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978127 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978128 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978129 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978130 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978131 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978132 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978133 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978134 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978135 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978136 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978137 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978138 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978139 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978140 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978141 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978142 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978143 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978144 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978145 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978146 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978147 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978148 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978149 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978150 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978151 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978152 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978153 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978154 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978155 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978156 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978157 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978158 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978159 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978160 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978161 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978162 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978163 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978164 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978165 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978166 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978167 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978168 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978169 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978170 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978171 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978172 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978173 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978174 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978175 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978176 4 0.499 0.922 0.000 0.00 0.472 0.528
#> ERR978177 4 0.499 0.922 0.000 0.00 0.472 0.528
#> ERR978178 4 0.499 0.922 0.000 0.00 0.472 0.528
#> ERR978179 4 0.499 0.922 0.000 0.00 0.472 0.528
#> ERR978180 4 0.499 0.922 0.000 0.00 0.472 0.528
#> ERR978181 4 0.499 0.922 0.000 0.00 0.472 0.528
#> ERR978182 4 0.499 0.922 0.000 0.00 0.472 0.528
#> ERR978183 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978184 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978185 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978186 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978187 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978188 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978189 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978190 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978191 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978192 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978193 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978194 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978195 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978196 2 0.000 1.000 0.000 1.00 0.000 0.000
#> ERR978197 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978198 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978199 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978200 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978201 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978202 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978203 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978204 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978205 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978206 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978207 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978208 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978209 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978210 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978211 3 0.000 0.635 0.000 0.00 1.000 0.000
#> ERR978212 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978213 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978214 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978215 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978216 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978217 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978218 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978219 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978220 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978221 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978222 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978223 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978224 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978225 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978226 3 0.513 0.737 0.388 0.00 0.604 0.008
#> ERR978227 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978228 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978229 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978230 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978231 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978232 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978233 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978234 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978235 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978236 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978237 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978238 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978239 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978240 1 0.482 1.000 0.612 0.00 0.000 0.388
#> ERR978241 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978242 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978243 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978244 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978245 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978246 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978247 4 0.484 0.932 0.000 0.00 0.396 0.604
#> ERR978248 4 0.560 0.917 0.000 0.02 0.472 0.508
#> ERR978249 4 0.560 0.917 0.000 0.02 0.472 0.508
#> ERR978250 4 0.560 0.917 0.000 0.02 0.472 0.508
#> ERR978251 4 0.560 0.917 0.000 0.02 0.472 0.508
#> ERR978252 4 0.560 0.917 0.000 0.02 0.472 0.508
#> ERR978253 4 0.560 0.917 0.000 0.02 0.472 0.508
#> ERR978254 4 0.560 0.917 0.000 0.02 0.472 0.508
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978123 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978124 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978125 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978126 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978127 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978128 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978129 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978130 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978131 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978132 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978133 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978134 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978135 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978136 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978137 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978138 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978139 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978140 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978141 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978142 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978143 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978144 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978145 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978146 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978147 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978148 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978149 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978150 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978151 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978152 5 0.154 0.942 0 0.00 0.068 0.000 0.932
#> ERR978153 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978169 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978170 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978171 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978172 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978173 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978174 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978175 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978176 4 0.439 0.830 0 0.00 0.380 0.612 0.008
#> ERR978177 4 0.439 0.830 0 0.00 0.380 0.612 0.008
#> ERR978178 4 0.439 0.830 0 0.00 0.380 0.612 0.008
#> ERR978179 4 0.439 0.830 0 0.00 0.380 0.612 0.008
#> ERR978180 4 0.439 0.830 0 0.00 0.380 0.612 0.008
#> ERR978181 4 0.439 0.830 0 0.00 0.380 0.612 0.008
#> ERR978182 4 0.439 0.830 0 0.00 0.380 0.612 0.008
#> ERR978183 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0 1.00 0.000 0.000 0.000
#> ERR978197 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978198 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978199 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978200 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978201 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978202 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978203 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978204 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978205 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978206 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978207 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978208 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978209 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978210 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978211 3 0.377 1.000 0 0.00 0.704 0.000 0.296
#> ERR978212 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978213 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978214 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978215 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978216 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978217 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978218 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978219 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978220 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978221 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978222 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978223 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978224 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978225 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978226 5 0.000 0.945 0 0.00 0.000 0.000 1.000
#> ERR978227 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.00 0.000 0.000 0.000
#> ERR978241 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978242 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978243 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978244 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978245 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978246 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978247 4 0.000 0.841 0 0.00 0.000 1.000 0.000
#> ERR978248 4 0.494 0.825 0 0.02 0.380 0.592 0.008
#> ERR978249 4 0.494 0.825 0 0.02 0.380 0.592 0.008
#> ERR978250 4 0.494 0.825 0 0.02 0.380 0.592 0.008
#> ERR978251 4 0.494 0.825 0 0.02 0.380 0.592 0.008
#> ERR978252 4 0.494 0.825 0 0.02 0.380 0.592 0.008
#> ERR978253 4 0.494 0.825 0 0.02 0.380 0.592 0.008
#> ERR978254 4 0.494 0.825 0 0.02 0.380 0.592 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978108 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978109 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978110 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978111 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978112 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978113 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978114 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978115 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978116 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978117 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978118 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978119 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978120 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978121 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978122 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978123 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978124 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978125 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978126 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978127 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978128 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978129 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978130 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978131 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978132 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978133 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978134 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978135 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978136 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978137 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978138 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978139 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978140 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978141 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978142 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978143 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978144 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978145 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978146 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978147 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978148 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978149 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978150 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978151 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978152 5 0.3371 0.787 0 0 0.292 0.00 0.708 0.00
#> ERR978153 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978154 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978155 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978156 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978157 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978158 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978159 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978160 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978161 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978162 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978163 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978164 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978165 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978166 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978167 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978168 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978169 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978170 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978171 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978172 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978173 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978174 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978175 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978176 6 0.0547 0.989 0 0 0.000 0.02 0.000 0.98
#> ERR978177 6 0.0547 0.989 0 0 0.000 0.02 0.000 0.98
#> ERR978178 6 0.0547 0.989 0 0 0.000 0.02 0.000 0.98
#> ERR978179 6 0.0547 0.989 0 0 0.000 0.02 0.000 0.98
#> ERR978180 6 0.0547 0.989 0 0 0.000 0.02 0.000 0.98
#> ERR978181 6 0.0547 0.989 0 0 0.000 0.02 0.000 0.98
#> ERR978182 6 0.0547 0.989 0 0 0.000 0.02 0.000 0.98
#> ERR978183 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978184 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978185 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978186 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978187 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978188 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978189 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978190 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978191 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978192 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978193 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978194 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978195 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978196 2 0.0000 1.000 0 1 0.000 0.00 0.000 0.00
#> ERR978197 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978198 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978199 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978200 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978201 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978202 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978203 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978204 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978205 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978206 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978207 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978208 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978209 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978210 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978211 3 0.0000 1.000 0 0 1.000 0.00 0.000 0.00
#> ERR978212 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978213 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978214 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978215 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978216 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978217 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978218 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978219 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978220 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978221 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978222 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978223 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978224 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978225 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978226 5 0.0000 0.806 0 0 0.000 0.00 1.000 0.00
#> ERR978227 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978228 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978229 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978230 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978231 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978232 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978233 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978234 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978235 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978236 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978237 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978238 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978239 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978240 1 0.0000 1.000 1 0 0.000 0.00 0.000 0.00
#> ERR978241 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978242 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978243 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978244 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978245 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978246 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978247 4 0.0000 1.000 0 0 0.000 1.00 0.000 0.00
#> ERR978248 6 0.0000 0.989 0 0 0.000 0.00 0.000 1.00
#> ERR978249 6 0.0000 0.989 0 0 0.000 0.00 0.000 1.00
#> ERR978250 6 0.0000 0.989 0 0 0.000 0.00 0.000 1.00
#> ERR978251 6 0.0000 0.989 0 0 0.000 0.00 0.000 1.00
#> ERR978252 6 0.0000 0.989 0 0 0.000 0.00 0.000 1.00
#> ERR978253 6 0.0000 0.989 0 0 0.000 0.00 0.000 1.00
#> ERR978254 6 0.0000 0.989 0 0 0.000 0.00 0.000 1.00
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 14049 rows and 148 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.153 0.710 0.757 0.3813 0.675 0.675
#> 3 3 0.243 0.675 0.756 0.5098 0.724 0.592
#> 4 4 0.535 0.716 0.691 0.2111 0.807 0.554
#> 5 5 0.659 0.786 0.720 0.0718 0.929 0.737
#> 6 6 0.702 0.746 0.745 0.0623 1.000 1.000
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
#> ERR978107 2 0.998 0.589 0.476 0.524
#> ERR978108 2 0.998 0.589 0.476 0.524
#> ERR978109 2 0.998 0.589 0.476 0.524
#> ERR978110 2 0.998 0.589 0.476 0.524
#> ERR978111 2 0.998 0.589 0.476 0.524
#> ERR978112 2 0.998 0.589 0.476 0.524
#> ERR978113 2 0.998 0.589 0.476 0.524
#> ERR978114 2 0.998 0.589 0.476 0.524
#> ERR978115 2 0.998 0.589 0.476 0.524
#> ERR978116 2 0.998 0.589 0.476 0.524
#> ERR978117 2 0.998 0.589 0.476 0.524
#> ERR978118 2 0.998 0.589 0.476 0.524
#> ERR978119 2 0.998 0.589 0.476 0.524
#> ERR978120 2 0.998 0.589 0.476 0.524
#> ERR978121 2 0.998 0.589 0.476 0.524
#> ERR978122 2 0.998 0.589 0.476 0.524
#> ERR978123 2 0.430 0.659 0.088 0.912
#> ERR978124 2 0.430 0.659 0.088 0.912
#> ERR978125 2 0.430 0.659 0.088 0.912
#> ERR978126 2 0.430 0.659 0.088 0.912
#> ERR978127 2 0.430 0.659 0.088 0.912
#> ERR978128 2 0.430 0.659 0.088 0.912
#> ERR978129 2 0.430 0.659 0.088 0.912
#> ERR978130 2 0.430 0.659 0.088 0.912
#> ERR978131 2 0.430 0.659 0.088 0.912
#> ERR978132 2 0.430 0.659 0.088 0.912
#> ERR978133 2 0.430 0.659 0.088 0.912
#> ERR978134 2 0.430 0.659 0.088 0.912
#> ERR978135 2 0.430 0.659 0.088 0.912
#> ERR978136 2 0.430 0.659 0.088 0.912
#> ERR978137 2 0.430 0.659 0.088 0.912
#> ERR978138 2 0.242 0.671 0.040 0.960
#> ERR978139 2 0.242 0.671 0.040 0.960
#> ERR978140 2 0.242 0.671 0.040 0.960
#> ERR978141 2 0.242 0.671 0.040 0.960
#> ERR978142 2 0.242 0.671 0.040 0.960
#> ERR978143 2 0.242 0.671 0.040 0.960
#> ERR978144 2 0.242 0.671 0.040 0.960
#> ERR978145 2 0.242 0.671 0.040 0.960
#> ERR978146 2 0.204 0.666 0.032 0.968
#> ERR978147 2 0.204 0.666 0.032 0.968
#> ERR978148 2 0.204 0.666 0.032 0.968
#> ERR978149 2 0.204 0.666 0.032 0.968
#> ERR978150 2 0.204 0.666 0.032 0.968
#> ERR978151 2 0.204 0.666 0.032 0.968
#> ERR978152 2 0.204 0.666 0.032 0.968
#> ERR978153 1 0.991 1.000 0.556 0.444
#> ERR978154 1 0.991 1.000 0.556 0.444
#> ERR978155 1 0.991 1.000 0.556 0.444
#> ERR978156 1 0.991 1.000 0.556 0.444
#> ERR978157 1 0.991 1.000 0.556 0.444
#> ERR978158 1 0.991 1.000 0.556 0.444
#> ERR978159 1 0.991 1.000 0.556 0.444
#> ERR978160 1 0.991 1.000 0.556 0.444
#> ERR978161 1 0.991 1.000 0.556 0.444
#> ERR978162 1 0.991 1.000 0.556 0.444
#> ERR978163 1 0.991 1.000 0.556 0.444
#> ERR978164 1 0.991 1.000 0.556 0.444
#> ERR978165 1 0.991 1.000 0.556 0.444
#> ERR978166 1 0.991 1.000 0.556 0.444
#> ERR978167 1 0.991 1.000 0.556 0.444
#> ERR978168 1 0.991 1.000 0.556 0.444
#> ERR978169 2 0.584 0.572 0.140 0.860
#> ERR978170 2 0.584 0.572 0.140 0.860
#> ERR978171 2 0.584 0.572 0.140 0.860
#> ERR978172 2 0.584 0.572 0.140 0.860
#> ERR978173 2 0.584 0.572 0.140 0.860
#> ERR978174 2 0.584 0.572 0.140 0.860
#> ERR978175 2 0.584 0.572 0.140 0.860
#> ERR978176 2 0.615 0.569 0.152 0.848
#> ERR978177 2 0.615 0.569 0.152 0.848
#> ERR978178 2 0.615 0.569 0.152 0.848
#> ERR978179 2 0.615 0.569 0.152 0.848
#> ERR978180 2 0.615 0.569 0.152 0.848
#> ERR978181 2 0.615 0.569 0.152 0.848
#> ERR978182 2 0.615 0.569 0.152 0.848
#> ERR978183 2 0.998 0.589 0.472 0.528
#> ERR978184 2 0.998 0.589 0.472 0.528
#> ERR978185 2 0.998 0.589 0.472 0.528
#> ERR978186 2 0.998 0.589 0.472 0.528
#> ERR978187 2 0.998 0.589 0.472 0.528
#> ERR978188 2 0.998 0.589 0.472 0.528
#> ERR978189 2 0.998 0.589 0.472 0.528
#> ERR978190 2 0.998 0.589 0.472 0.528
#> ERR978191 2 0.998 0.589 0.472 0.528
#> ERR978192 2 0.998 0.589 0.472 0.528
#> ERR978193 2 0.998 0.589 0.472 0.528
#> ERR978194 2 0.998 0.589 0.472 0.528
#> ERR978195 2 0.998 0.589 0.472 0.528
#> ERR978196 2 0.998 0.589 0.472 0.528
#> ERR978197 2 0.494 0.700 0.108 0.892
#> ERR978198 2 0.494 0.700 0.108 0.892
#> ERR978199 2 0.494 0.700 0.108 0.892
#> ERR978200 2 0.494 0.700 0.108 0.892
#> ERR978201 2 0.494 0.700 0.108 0.892
#> ERR978202 2 0.494 0.700 0.108 0.892
#> ERR978203 2 0.494 0.700 0.108 0.892
#> ERR978204 2 0.518 0.702 0.116 0.884
#> ERR978205 2 0.518 0.702 0.116 0.884
#> ERR978206 2 0.518 0.702 0.116 0.884
#> ERR978207 2 0.518 0.702 0.116 0.884
#> ERR978208 2 0.518 0.702 0.116 0.884
#> ERR978209 2 0.518 0.702 0.116 0.884
#> ERR978210 2 0.518 0.702 0.116 0.884
#> ERR978211 2 0.518 0.702 0.116 0.884
#> ERR978212 2 0.706 0.693 0.192 0.808
#> ERR978213 2 0.706 0.693 0.192 0.808
#> ERR978214 2 0.706 0.693 0.192 0.808
#> ERR978215 2 0.706 0.693 0.192 0.808
#> ERR978216 2 0.706 0.693 0.192 0.808
#> ERR978217 2 0.706 0.693 0.192 0.808
#> ERR978218 2 0.706 0.693 0.192 0.808
#> ERR978219 2 0.706 0.693 0.192 0.808
#> ERR978220 2 0.706 0.693 0.192 0.808
#> ERR978221 2 0.706 0.693 0.192 0.808
#> ERR978222 2 0.706 0.693 0.192 0.808
#> ERR978223 2 0.706 0.693 0.192 0.808
#> ERR978224 2 0.706 0.693 0.192 0.808
#> ERR978225 2 0.706 0.693 0.192 0.808
#> ERR978226 2 0.697 0.693 0.188 0.812
#> ERR978227 1 0.991 1.000 0.556 0.444
#> ERR978228 1 0.991 1.000 0.556 0.444
#> ERR978229 1 0.991 1.000 0.556 0.444
#> ERR978230 1 0.991 1.000 0.556 0.444
#> ERR978231 1 0.991 1.000 0.556 0.444
#> ERR978232 1 0.991 1.000 0.556 0.444
#> ERR978233 1 0.991 1.000 0.556 0.444
#> ERR978234 1 0.991 1.000 0.556 0.444
#> ERR978235 1 0.991 1.000 0.556 0.444
#> ERR978236 1 0.991 1.000 0.556 0.444
#> ERR978237 1 0.991 1.000 0.556 0.444
#> ERR978238 1 0.991 1.000 0.556 0.444
#> ERR978239 1 0.991 1.000 0.556 0.444
#> ERR978240 1 0.991 1.000 0.556 0.444
#> ERR978241 2 0.563 0.576 0.132 0.868
#> ERR978242 2 0.563 0.576 0.132 0.868
#> ERR978243 2 0.563 0.576 0.132 0.868
#> ERR978244 2 0.563 0.576 0.132 0.868
#> ERR978245 2 0.563 0.576 0.132 0.868
#> ERR978246 2 0.563 0.576 0.132 0.868
#> ERR978247 2 0.563 0.576 0.132 0.868
#> ERR978248 2 0.881 0.664 0.300 0.700
#> ERR978249 2 0.881 0.664 0.300 0.700
#> ERR978250 2 0.881 0.664 0.300 0.700
#> ERR978251 2 0.881 0.664 0.300 0.700
#> ERR978252 2 0.881 0.664 0.300 0.700
#> ERR978253 2 0.881 0.664 0.300 0.700
#> ERR978254 2 0.881 0.664 0.300 0.700
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978108 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978109 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978110 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978111 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978112 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978113 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978114 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978115 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978116 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978117 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978118 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978119 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978120 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978121 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978122 2 0.516 0.8241 0.008 0.776 0.216
#> ERR978123 3 0.432 0.6382 0.088 0.044 0.868
#> ERR978124 3 0.432 0.6382 0.088 0.044 0.868
#> ERR978125 3 0.432 0.6382 0.088 0.044 0.868
#> ERR978126 3 0.432 0.6382 0.088 0.044 0.868
#> ERR978127 3 0.432 0.6382 0.088 0.044 0.868
#> ERR978128 3 0.432 0.6382 0.088 0.044 0.868
#> ERR978129 3 0.432 0.6382 0.088 0.044 0.868
#> ERR978130 3 0.432 0.6382 0.088 0.044 0.868
#> ERR978131 3 0.453 0.6384 0.088 0.052 0.860
#> ERR978132 3 0.453 0.6384 0.088 0.052 0.860
#> ERR978133 3 0.453 0.6384 0.088 0.052 0.860
#> ERR978134 3 0.453 0.6384 0.088 0.052 0.860
#> ERR978135 3 0.453 0.6384 0.088 0.052 0.860
#> ERR978136 3 0.453 0.6384 0.088 0.052 0.860
#> ERR978137 3 0.453 0.6384 0.088 0.052 0.860
#> ERR978138 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978139 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978140 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978141 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978142 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978143 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978144 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978145 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978146 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978147 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978148 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978149 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978150 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978151 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978152 3 0.591 0.6369 0.068 0.144 0.788
#> ERR978153 1 0.280 0.9722 0.924 0.016 0.060
#> ERR978154 1 0.280 0.9722 0.924 0.016 0.060
#> ERR978155 1 0.280 0.9722 0.924 0.016 0.060
#> ERR978156 1 0.280 0.9722 0.924 0.016 0.060
#> ERR978157 1 0.280 0.9722 0.924 0.016 0.060
#> ERR978158 1 0.280 0.9722 0.924 0.016 0.060
#> ERR978159 1 0.280 0.9722 0.924 0.016 0.060
#> ERR978160 1 0.295 0.9711 0.920 0.020 0.060
#> ERR978161 1 0.295 0.9723 0.920 0.020 0.060
#> ERR978162 1 0.295 0.9723 0.920 0.020 0.060
#> ERR978163 1 0.295 0.9723 0.920 0.020 0.060
#> ERR978164 1 0.295 0.9723 0.920 0.020 0.060
#> ERR978165 1 0.295 0.9723 0.920 0.020 0.060
#> ERR978166 1 0.295 0.9723 0.920 0.020 0.060
#> ERR978167 1 0.295 0.9723 0.920 0.020 0.060
#> ERR978168 1 0.295 0.9723 0.920 0.020 0.060
#> ERR978169 3 0.833 0.5689 0.208 0.164 0.628
#> ERR978170 3 0.833 0.5689 0.208 0.164 0.628
#> ERR978171 3 0.833 0.5689 0.208 0.164 0.628
#> ERR978172 3 0.833 0.5689 0.208 0.164 0.628
#> ERR978173 3 0.833 0.5689 0.208 0.164 0.628
#> ERR978174 3 0.833 0.5689 0.208 0.164 0.628
#> ERR978175 3 0.833 0.5689 0.208 0.164 0.628
#> ERR978176 3 0.891 0.5476 0.216 0.212 0.572
#> ERR978177 3 0.891 0.5476 0.216 0.212 0.572
#> ERR978178 3 0.891 0.5476 0.216 0.212 0.572
#> ERR978179 3 0.891 0.5476 0.216 0.212 0.572
#> ERR978180 3 0.891 0.5476 0.216 0.212 0.572
#> ERR978181 3 0.891 0.5476 0.216 0.212 0.572
#> ERR978182 3 0.891 0.5476 0.216 0.212 0.572
#> ERR978183 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978184 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978185 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978186 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978187 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978188 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978189 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978190 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978191 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978192 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978193 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978194 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978195 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978196 2 0.484 0.8224 0.016 0.816 0.168
#> ERR978197 3 0.579 0.6153 0.084 0.116 0.800
#> ERR978198 3 0.579 0.6153 0.084 0.116 0.800
#> ERR978199 3 0.579 0.6153 0.084 0.116 0.800
#> ERR978200 3 0.579 0.6153 0.084 0.116 0.800
#> ERR978201 3 0.579 0.6153 0.084 0.116 0.800
#> ERR978202 3 0.579 0.6153 0.084 0.116 0.800
#> ERR978203 3 0.579 0.6153 0.084 0.116 0.800
#> ERR978204 3 0.571 0.6129 0.080 0.116 0.804
#> ERR978205 3 0.571 0.6129 0.080 0.116 0.804
#> ERR978206 3 0.571 0.6129 0.080 0.116 0.804
#> ERR978207 3 0.571 0.6129 0.080 0.116 0.804
#> ERR978208 3 0.571 0.6129 0.080 0.116 0.804
#> ERR978209 3 0.571 0.6129 0.080 0.116 0.804
#> ERR978210 3 0.571 0.6129 0.080 0.116 0.804
#> ERR978211 3 0.571 0.6129 0.080 0.116 0.804
#> ERR978212 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978213 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978214 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978215 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978216 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978217 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978218 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978219 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978220 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978221 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978222 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978223 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978224 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978225 3 0.760 0.4135 0.056 0.344 0.600
#> ERR978226 3 0.758 0.4206 0.056 0.340 0.604
#> ERR978227 1 0.473 0.9685 0.852 0.088 0.060
#> ERR978228 1 0.473 0.9685 0.852 0.088 0.060
#> ERR978229 1 0.473 0.9685 0.852 0.088 0.060
#> ERR978230 1 0.473 0.9685 0.852 0.088 0.060
#> ERR978231 1 0.473 0.9685 0.852 0.088 0.060
#> ERR978232 1 0.473 0.9685 0.852 0.088 0.060
#> ERR978233 1 0.473 0.9685 0.852 0.088 0.060
#> ERR978234 1 0.481 0.9685 0.848 0.092 0.060
#> ERR978235 1 0.481 0.9685 0.848 0.092 0.060
#> ERR978236 1 0.481 0.9685 0.848 0.092 0.060
#> ERR978237 1 0.481 0.9685 0.848 0.092 0.060
#> ERR978238 1 0.481 0.9685 0.848 0.092 0.060
#> ERR978239 1 0.481 0.9685 0.848 0.092 0.060
#> ERR978240 1 0.481 0.9685 0.848 0.092 0.060
#> ERR978241 3 0.859 0.5675 0.220 0.176 0.604
#> ERR978242 3 0.859 0.5675 0.220 0.176 0.604
#> ERR978243 3 0.859 0.5675 0.220 0.176 0.604
#> ERR978244 3 0.859 0.5675 0.220 0.176 0.604
#> ERR978245 3 0.859 0.5675 0.220 0.176 0.604
#> ERR978246 3 0.859 0.5675 0.220 0.176 0.604
#> ERR978247 3 0.859 0.5675 0.220 0.176 0.604
#> ERR978248 2 0.898 -0.0305 0.132 0.484 0.384
#> ERR978249 2 0.898 -0.0305 0.132 0.484 0.384
#> ERR978250 2 0.898 -0.0305 0.132 0.484 0.384
#> ERR978251 2 0.898 -0.0305 0.132 0.484 0.384
#> ERR978252 2 0.898 -0.0305 0.132 0.484 0.384
#> ERR978253 2 0.898 -0.0305 0.132 0.484 0.384
#> ERR978254 2 0.898 -0.0305 0.132 0.484 0.384
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.3093 0.921 0.004 0.892 0.064 0.040
#> ERR978108 2 0.3093 0.921 0.004 0.892 0.064 0.040
#> ERR978109 2 0.3093 0.921 0.004 0.892 0.064 0.040
#> ERR978110 2 0.3093 0.921 0.004 0.892 0.064 0.040
#> ERR978111 2 0.3093 0.921 0.004 0.892 0.064 0.040
#> ERR978112 2 0.3093 0.921 0.004 0.892 0.064 0.040
#> ERR978113 2 0.3093 0.921 0.004 0.892 0.064 0.040
#> ERR978114 2 0.3093 0.921 0.004 0.892 0.064 0.040
#> ERR978115 2 0.3168 0.921 0.004 0.888 0.068 0.040
#> ERR978116 2 0.3168 0.921 0.004 0.888 0.068 0.040
#> ERR978117 2 0.3168 0.921 0.004 0.888 0.068 0.040
#> ERR978118 2 0.3168 0.921 0.004 0.888 0.068 0.040
#> ERR978119 2 0.3168 0.921 0.004 0.888 0.068 0.040
#> ERR978120 2 0.3168 0.921 0.004 0.888 0.068 0.040
#> ERR978121 2 0.3168 0.921 0.004 0.888 0.068 0.040
#> ERR978122 2 0.3312 0.921 0.008 0.884 0.068 0.040
#> ERR978123 3 0.6935 0.871 0.076 0.044 0.640 0.240
#> ERR978124 3 0.6935 0.871 0.076 0.044 0.640 0.240
#> ERR978125 3 0.6935 0.871 0.076 0.044 0.640 0.240
#> ERR978126 3 0.6935 0.871 0.076 0.044 0.640 0.240
#> ERR978127 3 0.6935 0.871 0.076 0.044 0.640 0.240
#> ERR978128 3 0.6935 0.871 0.076 0.044 0.640 0.240
#> ERR978129 3 0.6935 0.871 0.076 0.044 0.640 0.240
#> ERR978130 3 0.6935 0.871 0.076 0.044 0.640 0.240
#> ERR978131 3 0.6977 0.870 0.084 0.040 0.636 0.240
#> ERR978132 3 0.6977 0.870 0.084 0.040 0.636 0.240
#> ERR978133 3 0.6977 0.870 0.084 0.040 0.636 0.240
#> ERR978134 3 0.6977 0.870 0.084 0.040 0.636 0.240
#> ERR978135 3 0.6977 0.870 0.084 0.040 0.636 0.240
#> ERR978136 3 0.6977 0.870 0.084 0.040 0.636 0.240
#> ERR978137 3 0.6977 0.870 0.084 0.040 0.636 0.240
#> ERR978138 4 0.7443 0.356 0.020 0.108 0.368 0.504
#> ERR978139 4 0.7443 0.356 0.020 0.108 0.368 0.504
#> ERR978140 4 0.7443 0.356 0.020 0.108 0.368 0.504
#> ERR978141 4 0.7443 0.356 0.020 0.108 0.368 0.504
#> ERR978142 4 0.7443 0.356 0.020 0.108 0.368 0.504
#> ERR978143 4 0.7443 0.356 0.020 0.108 0.368 0.504
#> ERR978144 4 0.7443 0.356 0.020 0.108 0.368 0.504
#> ERR978145 4 0.7443 0.356 0.020 0.108 0.368 0.504
#> ERR978146 4 0.7369 0.341 0.020 0.100 0.376 0.504
#> ERR978147 4 0.7369 0.341 0.020 0.100 0.376 0.504
#> ERR978148 4 0.7369 0.341 0.020 0.100 0.376 0.504
#> ERR978149 4 0.7369 0.341 0.020 0.100 0.376 0.504
#> ERR978150 4 0.7369 0.341 0.020 0.100 0.376 0.504
#> ERR978151 4 0.7369 0.341 0.020 0.100 0.376 0.504
#> ERR978152 4 0.7369 0.341 0.020 0.100 0.376 0.504
#> ERR978153 1 0.0469 0.945 0.988 0.000 0.000 0.012
#> ERR978154 1 0.0469 0.945 0.988 0.000 0.000 0.012
#> ERR978155 1 0.0469 0.945 0.988 0.000 0.000 0.012
#> ERR978156 1 0.0469 0.945 0.988 0.000 0.000 0.012
#> ERR978157 1 0.0469 0.945 0.988 0.000 0.000 0.012
#> ERR978158 1 0.0469 0.945 0.988 0.000 0.000 0.012
#> ERR978159 1 0.0469 0.945 0.988 0.000 0.000 0.012
#> ERR978160 1 0.0469 0.945 0.988 0.000 0.000 0.012
#> ERR978161 1 0.0657 0.945 0.984 0.000 0.004 0.012
#> ERR978162 1 0.0657 0.945 0.984 0.000 0.004 0.012
#> ERR978163 1 0.0657 0.945 0.984 0.000 0.004 0.012
#> ERR978164 1 0.0657 0.945 0.984 0.000 0.004 0.012
#> ERR978165 1 0.0657 0.945 0.984 0.000 0.004 0.012
#> ERR978166 1 0.0657 0.945 0.984 0.000 0.004 0.012
#> ERR978167 1 0.0657 0.945 0.984 0.000 0.004 0.012
#> ERR978168 1 0.0657 0.945 0.984 0.000 0.004 0.012
#> ERR978169 4 0.6023 0.400 0.064 0.044 0.160 0.732
#> ERR978170 4 0.6023 0.400 0.064 0.044 0.160 0.732
#> ERR978171 4 0.6023 0.400 0.064 0.044 0.160 0.732
#> ERR978172 4 0.6023 0.400 0.064 0.044 0.160 0.732
#> ERR978173 4 0.6023 0.400 0.064 0.044 0.160 0.732
#> ERR978174 4 0.6023 0.400 0.064 0.044 0.160 0.732
#> ERR978175 4 0.6023 0.400 0.064 0.044 0.160 0.732
#> ERR978176 4 0.4670 0.481 0.064 0.056 0.052 0.828
#> ERR978177 4 0.4670 0.481 0.064 0.056 0.052 0.828
#> ERR978178 4 0.4670 0.481 0.064 0.056 0.052 0.828
#> ERR978179 4 0.4670 0.481 0.064 0.056 0.052 0.828
#> ERR978180 4 0.4670 0.481 0.064 0.056 0.052 0.828
#> ERR978181 4 0.4670 0.481 0.064 0.056 0.052 0.828
#> ERR978182 4 0.4670 0.481 0.064 0.056 0.052 0.828
#> ERR978183 2 0.2189 0.908 0.004 0.932 0.044 0.020
#> ERR978184 2 0.2189 0.908 0.004 0.932 0.044 0.020
#> ERR978185 2 0.2189 0.908 0.004 0.932 0.044 0.020
#> ERR978186 2 0.2189 0.908 0.004 0.932 0.044 0.020
#> ERR978187 2 0.2189 0.908 0.004 0.932 0.044 0.020
#> ERR978188 2 0.2189 0.908 0.004 0.932 0.044 0.020
#> ERR978189 2 0.2189 0.908 0.004 0.932 0.044 0.020
#> ERR978190 2 0.2383 0.909 0.004 0.924 0.048 0.024
#> ERR978191 2 0.2383 0.909 0.004 0.924 0.048 0.024
#> ERR978192 2 0.2383 0.909 0.004 0.924 0.048 0.024
#> ERR978193 2 0.2383 0.909 0.004 0.924 0.048 0.024
#> ERR978194 2 0.2383 0.909 0.004 0.924 0.048 0.024
#> ERR978195 2 0.2383 0.909 0.004 0.924 0.048 0.024
#> ERR978196 2 0.2383 0.909 0.004 0.924 0.048 0.024
#> ERR978197 3 0.7081 0.870 0.076 0.084 0.664 0.176
#> ERR978198 3 0.7081 0.870 0.076 0.084 0.664 0.176
#> ERR978199 3 0.7081 0.870 0.076 0.084 0.664 0.176
#> ERR978200 3 0.7081 0.870 0.076 0.084 0.664 0.176
#> ERR978201 3 0.7081 0.870 0.076 0.084 0.664 0.176
#> ERR978202 3 0.7081 0.870 0.076 0.084 0.664 0.176
#> ERR978203 3 0.7081 0.870 0.076 0.084 0.664 0.176
#> ERR978204 3 0.7019 0.855 0.084 0.088 0.676 0.152
#> ERR978205 3 0.7019 0.855 0.084 0.088 0.676 0.152
#> ERR978206 3 0.7019 0.855 0.084 0.088 0.676 0.152
#> ERR978207 3 0.7019 0.855 0.084 0.088 0.676 0.152
#> ERR978208 3 0.7019 0.855 0.084 0.088 0.676 0.152
#> ERR978209 3 0.7019 0.855 0.084 0.088 0.676 0.152
#> ERR978210 3 0.7019 0.855 0.084 0.088 0.676 0.152
#> ERR978211 3 0.7019 0.855 0.084 0.088 0.676 0.152
#> ERR978212 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978213 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978214 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978215 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978216 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978217 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978218 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978219 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978220 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978221 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978222 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978223 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978224 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978225 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978226 4 0.8265 0.453 0.016 0.264 0.308 0.412
#> ERR978227 1 0.3908 0.937 0.844 0.008 0.116 0.032
#> ERR978228 1 0.3908 0.937 0.844 0.008 0.116 0.032
#> ERR978229 1 0.3908 0.937 0.844 0.008 0.116 0.032
#> ERR978230 1 0.3908 0.937 0.844 0.008 0.116 0.032
#> ERR978231 1 0.3908 0.937 0.844 0.008 0.116 0.032
#> ERR978232 1 0.3908 0.937 0.844 0.008 0.116 0.032
#> ERR978233 1 0.3908 0.937 0.844 0.008 0.116 0.032
#> ERR978234 1 0.3877 0.937 0.840 0.004 0.124 0.032
#> ERR978235 1 0.3877 0.937 0.840 0.004 0.124 0.032
#> ERR978236 1 0.3877 0.937 0.840 0.004 0.124 0.032
#> ERR978237 1 0.3877 0.937 0.840 0.004 0.124 0.032
#> ERR978238 1 0.3877 0.937 0.840 0.004 0.124 0.032
#> ERR978239 1 0.3877 0.937 0.840 0.004 0.124 0.032
#> ERR978240 1 0.3877 0.937 0.840 0.004 0.124 0.032
#> ERR978241 4 0.5506 0.446 0.064 0.040 0.124 0.772
#> ERR978242 4 0.5506 0.446 0.064 0.040 0.124 0.772
#> ERR978243 4 0.5506 0.446 0.064 0.040 0.124 0.772
#> ERR978244 4 0.5506 0.446 0.064 0.040 0.124 0.772
#> ERR978245 4 0.5506 0.446 0.064 0.040 0.124 0.772
#> ERR978246 4 0.5506 0.446 0.064 0.040 0.124 0.772
#> ERR978247 4 0.5506 0.446 0.064 0.040 0.124 0.772
#> ERR978248 4 0.7098 0.423 0.044 0.328 0.056 0.572
#> ERR978249 4 0.7098 0.423 0.044 0.328 0.056 0.572
#> ERR978250 4 0.7098 0.423 0.044 0.328 0.056 0.572
#> ERR978251 4 0.7098 0.423 0.044 0.328 0.056 0.572
#> ERR978252 4 0.7098 0.423 0.044 0.328 0.056 0.572
#> ERR978253 4 0.7098 0.423 0.044 0.328 0.056 0.572
#> ERR978254 4 0.7098 0.423 0.044 0.328 0.056 0.572
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.164 0.8784 0.000 0.932 0.064 0.004 0.000
#> ERR978108 2 0.164 0.8784 0.000 0.932 0.064 0.004 0.000
#> ERR978109 2 0.164 0.8784 0.000 0.932 0.064 0.004 0.000
#> ERR978110 2 0.164 0.8784 0.000 0.932 0.064 0.004 0.000
#> ERR978111 2 0.164 0.8784 0.000 0.932 0.064 0.004 0.000
#> ERR978112 2 0.164 0.8784 0.000 0.932 0.064 0.004 0.000
#> ERR978113 2 0.164 0.8784 0.000 0.932 0.064 0.004 0.000
#> ERR978114 2 0.164 0.8784 0.000 0.932 0.064 0.004 0.000
#> ERR978115 2 0.252 0.8779 0.000 0.900 0.068 0.008 0.024
#> ERR978116 2 0.252 0.8779 0.000 0.900 0.068 0.008 0.024
#> ERR978117 2 0.252 0.8779 0.000 0.900 0.068 0.008 0.024
#> ERR978118 2 0.252 0.8779 0.000 0.900 0.068 0.008 0.024
#> ERR978119 2 0.252 0.8779 0.000 0.900 0.068 0.008 0.024
#> ERR978120 2 0.252 0.8779 0.000 0.900 0.068 0.008 0.024
#> ERR978121 2 0.252 0.8779 0.000 0.900 0.068 0.008 0.024
#> ERR978122 2 0.261 0.8778 0.000 0.896 0.068 0.008 0.028
#> ERR978123 3 0.166 0.8504 0.020 0.008 0.948 0.020 0.004
#> ERR978124 3 0.166 0.8504 0.020 0.008 0.948 0.020 0.004
#> ERR978125 3 0.166 0.8504 0.020 0.008 0.948 0.020 0.004
#> ERR978126 3 0.166 0.8504 0.020 0.008 0.948 0.020 0.004
#> ERR978127 3 0.166 0.8504 0.020 0.008 0.948 0.020 0.004
#> ERR978128 3 0.166 0.8504 0.020 0.008 0.948 0.020 0.004
#> ERR978129 3 0.166 0.8504 0.020 0.008 0.948 0.020 0.004
#> ERR978130 3 0.166 0.8504 0.020 0.008 0.948 0.020 0.004
#> ERR978131 3 0.176 0.8504 0.020 0.008 0.944 0.024 0.004
#> ERR978132 3 0.176 0.8504 0.020 0.008 0.944 0.024 0.004
#> ERR978133 3 0.176 0.8504 0.020 0.008 0.944 0.024 0.004
#> ERR978134 3 0.176 0.8504 0.020 0.008 0.944 0.024 0.004
#> ERR978135 3 0.176 0.8504 0.020 0.008 0.944 0.024 0.004
#> ERR978136 3 0.176 0.8504 0.020 0.008 0.944 0.024 0.004
#> ERR978137 3 0.176 0.8504 0.020 0.008 0.944 0.024 0.004
#> ERR978138 5 0.727 0.6311 0.016 0.060 0.396 0.084 0.444
#> ERR978139 5 0.727 0.6311 0.016 0.060 0.396 0.084 0.444
#> ERR978140 5 0.727 0.6311 0.016 0.060 0.396 0.084 0.444
#> ERR978141 5 0.727 0.6311 0.016 0.060 0.396 0.084 0.444
#> ERR978142 5 0.727 0.6311 0.016 0.060 0.396 0.084 0.444
#> ERR978143 5 0.727 0.6311 0.016 0.060 0.396 0.084 0.444
#> ERR978144 5 0.727 0.6311 0.016 0.060 0.396 0.084 0.444
#> ERR978145 5 0.727 0.6311 0.016 0.060 0.396 0.084 0.444
#> ERR978146 5 0.727 0.6303 0.016 0.060 0.400 0.084 0.440
#> ERR978147 5 0.727 0.6303 0.016 0.060 0.400 0.084 0.440
#> ERR978148 5 0.727 0.6303 0.016 0.060 0.400 0.084 0.440
#> ERR978149 5 0.727 0.6303 0.016 0.060 0.400 0.084 0.440
#> ERR978150 5 0.727 0.6303 0.016 0.060 0.400 0.084 0.440
#> ERR978151 5 0.727 0.6303 0.016 0.060 0.400 0.084 0.440
#> ERR978152 5 0.727 0.6303 0.016 0.060 0.400 0.084 0.440
#> ERR978153 1 0.117 0.8924 0.960 0.000 0.008 0.000 0.032
#> ERR978154 1 0.117 0.8924 0.960 0.000 0.008 0.000 0.032
#> ERR978155 1 0.117 0.8924 0.960 0.000 0.008 0.000 0.032
#> ERR978156 1 0.117 0.8924 0.960 0.000 0.008 0.000 0.032
#> ERR978157 1 0.117 0.8924 0.960 0.000 0.008 0.000 0.032
#> ERR978158 1 0.117 0.8924 0.960 0.000 0.008 0.000 0.032
#> ERR978159 1 0.117 0.8924 0.960 0.000 0.008 0.000 0.032
#> ERR978160 1 0.140 0.8915 0.956 0.004 0.008 0.004 0.028
#> ERR978161 1 0.128 0.8925 0.960 0.008 0.008 0.024 0.000
#> ERR978162 1 0.128 0.8925 0.960 0.008 0.008 0.024 0.000
#> ERR978163 1 0.128 0.8925 0.960 0.008 0.008 0.024 0.000
#> ERR978164 1 0.128 0.8925 0.960 0.008 0.008 0.024 0.000
#> ERR978165 1 0.128 0.8925 0.960 0.008 0.008 0.024 0.000
#> ERR978166 1 0.128 0.8925 0.960 0.008 0.008 0.024 0.000
#> ERR978167 1 0.128 0.8925 0.960 0.008 0.008 0.024 0.000
#> ERR978168 1 0.128 0.8925 0.960 0.008 0.008 0.024 0.000
#> ERR978169 4 0.739 0.8962 0.048 0.028 0.240 0.548 0.136
#> ERR978170 4 0.739 0.8962 0.048 0.028 0.240 0.548 0.136
#> ERR978171 4 0.739 0.8962 0.048 0.028 0.240 0.548 0.136
#> ERR978172 4 0.739 0.8962 0.048 0.028 0.240 0.548 0.136
#> ERR978173 4 0.739 0.8962 0.048 0.028 0.240 0.548 0.136
#> ERR978174 4 0.739 0.8962 0.048 0.028 0.240 0.548 0.136
#> ERR978175 4 0.739 0.8962 0.048 0.028 0.240 0.548 0.136
#> ERR978176 4 0.822 0.8286 0.048 0.056 0.184 0.460 0.252
#> ERR978177 4 0.822 0.8286 0.048 0.056 0.184 0.460 0.252
#> ERR978178 4 0.822 0.8286 0.048 0.056 0.184 0.460 0.252
#> ERR978179 4 0.822 0.8286 0.048 0.056 0.184 0.460 0.252
#> ERR978180 4 0.822 0.8286 0.048 0.056 0.184 0.460 0.252
#> ERR978181 4 0.822 0.8286 0.048 0.056 0.184 0.460 0.252
#> ERR978182 4 0.822 0.8286 0.048 0.056 0.184 0.460 0.252
#> ERR978183 2 0.553 0.8580 0.008 0.724 0.040 0.140 0.088
#> ERR978184 2 0.553 0.8580 0.008 0.724 0.040 0.140 0.088
#> ERR978185 2 0.553 0.8580 0.008 0.724 0.040 0.140 0.088
#> ERR978186 2 0.553 0.8580 0.008 0.724 0.040 0.140 0.088
#> ERR978187 2 0.553 0.8580 0.008 0.724 0.040 0.140 0.088
#> ERR978188 2 0.553 0.8580 0.008 0.724 0.040 0.140 0.088
#> ERR978189 2 0.553 0.8580 0.008 0.724 0.040 0.140 0.088
#> ERR978190 2 0.578 0.8587 0.004 0.696 0.044 0.160 0.096
#> ERR978191 2 0.578 0.8587 0.004 0.696 0.044 0.160 0.096
#> ERR978192 2 0.578 0.8587 0.004 0.696 0.044 0.160 0.096
#> ERR978193 2 0.578 0.8587 0.004 0.696 0.044 0.160 0.096
#> ERR978194 2 0.578 0.8587 0.004 0.696 0.044 0.160 0.096
#> ERR978195 2 0.578 0.8587 0.004 0.696 0.044 0.160 0.096
#> ERR978196 2 0.578 0.8587 0.004 0.696 0.044 0.160 0.096
#> ERR978197 3 0.444 0.8459 0.020 0.004 0.792 0.064 0.120
#> ERR978198 3 0.444 0.8459 0.020 0.004 0.792 0.064 0.120
#> ERR978199 3 0.444 0.8459 0.020 0.004 0.792 0.064 0.120
#> ERR978200 3 0.444 0.8459 0.020 0.004 0.792 0.064 0.120
#> ERR978201 3 0.444 0.8459 0.020 0.004 0.792 0.064 0.120
#> ERR978202 3 0.444 0.8459 0.020 0.004 0.792 0.064 0.120
#> ERR978203 3 0.444 0.8459 0.020 0.004 0.792 0.064 0.120
#> ERR978204 3 0.467 0.8411 0.020 0.008 0.780 0.068 0.124
#> ERR978205 3 0.467 0.8411 0.020 0.008 0.780 0.068 0.124
#> ERR978206 3 0.467 0.8411 0.020 0.008 0.780 0.068 0.124
#> ERR978207 3 0.467 0.8411 0.020 0.008 0.780 0.068 0.124
#> ERR978208 3 0.467 0.8411 0.020 0.008 0.780 0.068 0.124
#> ERR978209 3 0.467 0.8411 0.020 0.008 0.780 0.068 0.124
#> ERR978210 3 0.467 0.8411 0.020 0.008 0.780 0.068 0.124
#> ERR978211 3 0.467 0.8411 0.020 0.008 0.780 0.068 0.124
#> ERR978212 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978213 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978214 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978215 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978216 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978217 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978218 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978219 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978220 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978221 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978222 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978223 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978224 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978225 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978226 5 0.546 0.6853 0.016 0.088 0.200 0.004 0.692
#> ERR978227 1 0.489 0.8790 0.764 0.020 0.008 0.128 0.080
#> ERR978228 1 0.489 0.8790 0.764 0.020 0.008 0.128 0.080
#> ERR978229 1 0.489 0.8790 0.764 0.020 0.008 0.128 0.080
#> ERR978230 1 0.489 0.8790 0.764 0.020 0.008 0.128 0.080
#> ERR978231 1 0.489 0.8790 0.764 0.020 0.008 0.128 0.080
#> ERR978232 1 0.489 0.8790 0.764 0.020 0.008 0.128 0.080
#> ERR978233 1 0.489 0.8790 0.764 0.020 0.008 0.128 0.080
#> ERR978234 1 0.476 0.8790 0.764 0.016 0.008 0.152 0.060
#> ERR978235 1 0.476 0.8790 0.764 0.016 0.008 0.152 0.060
#> ERR978236 1 0.476 0.8790 0.764 0.016 0.008 0.152 0.060
#> ERR978237 1 0.476 0.8790 0.764 0.016 0.008 0.152 0.060
#> ERR978238 1 0.476 0.8790 0.764 0.016 0.008 0.152 0.060
#> ERR978239 1 0.476 0.8790 0.764 0.016 0.008 0.152 0.060
#> ERR978240 1 0.476 0.8790 0.764 0.016 0.008 0.152 0.060
#> ERR978241 4 0.751 0.9008 0.048 0.028 0.216 0.540 0.168
#> ERR978242 4 0.751 0.9008 0.048 0.028 0.216 0.540 0.168
#> ERR978243 4 0.751 0.9008 0.048 0.028 0.216 0.540 0.168
#> ERR978244 4 0.751 0.9008 0.048 0.028 0.216 0.540 0.168
#> ERR978245 4 0.751 0.9008 0.048 0.028 0.216 0.540 0.168
#> ERR978246 4 0.751 0.9008 0.048 0.028 0.216 0.540 0.168
#> ERR978247 4 0.751 0.9008 0.048 0.028 0.216 0.540 0.168
#> ERR978248 5 0.853 0.0131 0.044 0.168 0.120 0.204 0.464
#> ERR978249 5 0.853 0.0131 0.044 0.168 0.120 0.204 0.464
#> ERR978250 5 0.853 0.0131 0.044 0.168 0.120 0.204 0.464
#> ERR978251 5 0.853 0.0131 0.044 0.168 0.120 0.204 0.464
#> ERR978252 5 0.853 0.0131 0.044 0.168 0.120 0.204 0.464
#> ERR978253 5 0.853 0.0131 0.044 0.168 0.120 0.204 0.464
#> ERR978254 5 0.853 0.0131 0.044 0.168 0.120 0.204 0.464
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.4813 0.8590 0.000 0.728 0.024 0.064 0.016 NA
#> ERR978108 2 0.4813 0.8590 0.000 0.728 0.024 0.064 0.016 NA
#> ERR978109 2 0.4813 0.8590 0.000 0.728 0.024 0.064 0.016 NA
#> ERR978110 2 0.4813 0.8590 0.000 0.728 0.024 0.064 0.016 NA
#> ERR978111 2 0.4813 0.8590 0.000 0.728 0.024 0.064 0.016 NA
#> ERR978112 2 0.4813 0.8590 0.000 0.728 0.024 0.064 0.016 NA
#> ERR978113 2 0.4813 0.8590 0.000 0.728 0.024 0.064 0.016 NA
#> ERR978114 2 0.4813 0.8590 0.000 0.728 0.024 0.064 0.016 NA
#> ERR978115 2 0.4996 0.8599 0.000 0.692 0.024 0.052 0.016 NA
#> ERR978116 2 0.4996 0.8599 0.000 0.692 0.024 0.052 0.016 NA
#> ERR978117 2 0.4996 0.8599 0.000 0.692 0.024 0.052 0.016 NA
#> ERR978118 2 0.4996 0.8599 0.000 0.692 0.024 0.052 0.016 NA
#> ERR978119 2 0.4996 0.8599 0.000 0.692 0.024 0.052 0.016 NA
#> ERR978120 2 0.4996 0.8599 0.000 0.692 0.024 0.052 0.016 NA
#> ERR978121 2 0.4996 0.8599 0.000 0.692 0.024 0.052 0.016 NA
#> ERR978122 2 0.5053 0.8598 0.000 0.688 0.024 0.056 0.016 NA
#> ERR978123 3 0.2307 0.8152 0.028 0.016 0.908 0.044 0.004 NA
#> ERR978124 3 0.2307 0.8152 0.028 0.016 0.908 0.044 0.004 NA
#> ERR978125 3 0.2307 0.8152 0.028 0.016 0.908 0.044 0.004 NA
#> ERR978126 3 0.2307 0.8152 0.028 0.016 0.908 0.044 0.004 NA
#> ERR978127 3 0.2307 0.8152 0.028 0.016 0.908 0.044 0.004 NA
#> ERR978128 3 0.2307 0.8152 0.028 0.016 0.908 0.044 0.004 NA
#> ERR978129 3 0.2307 0.8152 0.028 0.016 0.908 0.044 0.004 NA
#> ERR978130 3 0.2307 0.8152 0.028 0.016 0.908 0.044 0.004 NA
#> ERR978131 3 0.2935 0.8139 0.028 0.016 0.884 0.048 0.008 NA
#> ERR978132 3 0.2935 0.8139 0.028 0.016 0.884 0.048 0.008 NA
#> ERR978133 3 0.2935 0.8139 0.028 0.016 0.884 0.048 0.008 NA
#> ERR978134 3 0.2935 0.8139 0.028 0.016 0.884 0.048 0.008 NA
#> ERR978135 3 0.2935 0.8139 0.028 0.016 0.884 0.048 0.008 NA
#> ERR978136 3 0.2935 0.8139 0.028 0.016 0.884 0.048 0.008 NA
#> ERR978137 3 0.2935 0.8139 0.028 0.016 0.884 0.048 0.008 NA
#> ERR978138 5 0.7048 0.5740 0.020 0.004 0.316 0.100 0.472 NA
#> ERR978139 5 0.7048 0.5740 0.020 0.004 0.316 0.100 0.472 NA
#> ERR978140 5 0.7048 0.5740 0.020 0.004 0.316 0.100 0.472 NA
#> ERR978141 5 0.7048 0.5740 0.020 0.004 0.316 0.100 0.472 NA
#> ERR978142 5 0.7048 0.5740 0.020 0.004 0.316 0.100 0.472 NA
#> ERR978143 5 0.7048 0.5740 0.020 0.004 0.316 0.100 0.472 NA
#> ERR978144 5 0.7048 0.5740 0.020 0.004 0.316 0.100 0.472 NA
#> ERR978145 5 0.7048 0.5740 0.020 0.004 0.316 0.100 0.472 NA
#> ERR978146 5 0.7163 0.5657 0.020 0.008 0.324 0.100 0.460 NA
#> ERR978147 5 0.7163 0.5657 0.020 0.008 0.324 0.100 0.460 NA
#> ERR978148 5 0.7163 0.5657 0.020 0.008 0.324 0.100 0.460 NA
#> ERR978149 5 0.7163 0.5657 0.020 0.008 0.324 0.100 0.460 NA
#> ERR978150 5 0.7163 0.5657 0.020 0.008 0.324 0.100 0.460 NA
#> ERR978151 5 0.7163 0.5657 0.020 0.008 0.324 0.100 0.460 NA
#> ERR978152 5 0.7163 0.5657 0.020 0.008 0.324 0.100 0.460 NA
#> ERR978153 1 0.3634 0.8524 0.696 0.000 0.000 0.008 0.000 NA
#> ERR978154 1 0.3634 0.8524 0.696 0.000 0.000 0.008 0.000 NA
#> ERR978155 1 0.3634 0.8524 0.696 0.000 0.000 0.008 0.000 NA
#> ERR978156 1 0.3634 0.8524 0.696 0.000 0.000 0.008 0.000 NA
#> ERR978157 1 0.3634 0.8524 0.696 0.000 0.000 0.008 0.000 NA
#> ERR978158 1 0.3634 0.8524 0.696 0.000 0.000 0.008 0.000 NA
#> ERR978159 1 0.3634 0.8524 0.696 0.000 0.000 0.008 0.000 NA
#> ERR978160 1 0.3935 0.8508 0.692 0.000 0.000 0.012 0.008 NA
#> ERR978161 1 0.4654 0.8525 0.696 0.004 0.004 0.040 0.016 NA
#> ERR978162 1 0.4552 0.8525 0.696 0.000 0.004 0.048 0.012 NA
#> ERR978163 1 0.4552 0.8525 0.696 0.000 0.004 0.048 0.012 NA
#> ERR978164 1 0.4552 0.8525 0.696 0.000 0.004 0.048 0.012 NA
#> ERR978165 1 0.4552 0.8525 0.696 0.000 0.004 0.048 0.012 NA
#> ERR978166 1 0.4654 0.8525 0.696 0.004 0.004 0.040 0.016 NA
#> ERR978167 1 0.4654 0.8525 0.696 0.004 0.004 0.040 0.016 NA
#> ERR978168 1 0.4670 0.8528 0.696 0.004 0.004 0.036 0.020 NA
#> ERR978169 4 0.4047 0.8444 0.016 0.000 0.152 0.776 0.052 NA
#> ERR978170 4 0.4047 0.8444 0.016 0.000 0.152 0.776 0.052 NA
#> ERR978171 4 0.4047 0.8444 0.016 0.000 0.152 0.776 0.052 NA
#> ERR978172 4 0.4047 0.8444 0.016 0.000 0.152 0.776 0.052 NA
#> ERR978173 4 0.4047 0.8444 0.016 0.000 0.152 0.776 0.052 NA
#> ERR978174 4 0.4047 0.8444 0.016 0.000 0.152 0.776 0.052 NA
#> ERR978175 4 0.4047 0.8444 0.016 0.000 0.152 0.776 0.052 NA
#> ERR978176 4 0.6849 0.7490 0.024 0.008 0.112 0.584 0.156 NA
#> ERR978177 4 0.6849 0.7490 0.024 0.008 0.112 0.584 0.156 NA
#> ERR978178 4 0.6849 0.7490 0.024 0.008 0.112 0.584 0.156 NA
#> ERR978179 4 0.6849 0.7490 0.024 0.008 0.112 0.584 0.156 NA
#> ERR978180 4 0.6849 0.7490 0.024 0.008 0.112 0.584 0.156 NA
#> ERR978181 4 0.6849 0.7490 0.024 0.008 0.112 0.584 0.156 NA
#> ERR978182 4 0.6849 0.7490 0.024 0.008 0.112 0.584 0.156 NA
#> ERR978183 2 0.1230 0.8409 0.000 0.956 0.008 0.008 0.028 NA
#> ERR978184 2 0.1230 0.8409 0.000 0.956 0.008 0.008 0.028 NA
#> ERR978185 2 0.1230 0.8409 0.000 0.956 0.008 0.008 0.028 NA
#> ERR978186 2 0.1230 0.8409 0.000 0.956 0.008 0.008 0.028 NA
#> ERR978187 2 0.1230 0.8409 0.000 0.956 0.008 0.008 0.028 NA
#> ERR978188 2 0.1230 0.8409 0.000 0.956 0.008 0.008 0.028 NA
#> ERR978189 2 0.1307 0.8409 0.000 0.952 0.008 0.008 0.032 NA
#> ERR978190 2 0.1988 0.8421 0.000 0.920 0.004 0.004 0.024 NA
#> ERR978191 2 0.1988 0.8421 0.000 0.920 0.004 0.004 0.024 NA
#> ERR978192 2 0.1988 0.8421 0.000 0.920 0.004 0.004 0.024 NA
#> ERR978193 2 0.1988 0.8421 0.000 0.920 0.004 0.004 0.024 NA
#> ERR978194 2 0.1988 0.8421 0.000 0.920 0.004 0.004 0.024 NA
#> ERR978195 2 0.1988 0.8421 0.000 0.920 0.004 0.004 0.024 NA
#> ERR978196 2 0.1988 0.8421 0.000 0.920 0.004 0.004 0.024 NA
#> ERR978197 3 0.5378 0.8073 0.028 0.016 0.720 0.028 0.072 NA
#> ERR978198 3 0.5378 0.8073 0.028 0.016 0.720 0.028 0.072 NA
#> ERR978199 3 0.5378 0.8073 0.028 0.016 0.720 0.028 0.072 NA
#> ERR978200 3 0.5378 0.8073 0.028 0.016 0.720 0.028 0.072 NA
#> ERR978201 3 0.5378 0.8073 0.028 0.016 0.720 0.028 0.072 NA
#> ERR978202 3 0.5378 0.8073 0.028 0.016 0.720 0.028 0.072 NA
#> ERR978203 3 0.5378 0.8073 0.028 0.016 0.720 0.028 0.072 NA
#> ERR978204 3 0.5903 0.7936 0.028 0.016 0.664 0.024 0.100 NA
#> ERR978205 3 0.5903 0.7936 0.028 0.016 0.664 0.024 0.100 NA
#> ERR978206 3 0.5903 0.7936 0.028 0.016 0.664 0.024 0.100 NA
#> ERR978207 3 0.5903 0.7936 0.028 0.016 0.664 0.024 0.100 NA
#> ERR978208 3 0.5903 0.7936 0.028 0.016 0.664 0.024 0.100 NA
#> ERR978209 3 0.5903 0.7936 0.028 0.016 0.664 0.024 0.100 NA
#> ERR978210 3 0.5903 0.7936 0.028 0.016 0.664 0.024 0.100 NA
#> ERR978211 3 0.5903 0.7936 0.028 0.016 0.664 0.024 0.100 NA
#> ERR978212 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978213 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978214 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978215 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978216 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978217 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978218 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978219 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978220 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978221 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978222 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978223 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978224 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978225 5 0.3968 0.6403 0.020 0.060 0.104 0.012 0.804 NA
#> ERR978226 5 0.4058 0.6390 0.020 0.060 0.104 0.016 0.800 NA
#> ERR978227 1 0.1168 0.8349 0.956 0.000 0.000 0.016 0.028 NA
#> ERR978228 1 0.1168 0.8349 0.956 0.000 0.000 0.016 0.028 NA
#> ERR978229 1 0.1168 0.8349 0.956 0.000 0.000 0.016 0.028 NA
#> ERR978230 1 0.1168 0.8349 0.956 0.000 0.000 0.016 0.028 NA
#> ERR978231 1 0.1168 0.8349 0.956 0.000 0.000 0.016 0.028 NA
#> ERR978232 1 0.1176 0.8350 0.956 0.000 0.000 0.020 0.024 NA
#> ERR978233 1 0.1176 0.8350 0.956 0.000 0.000 0.020 0.024 NA
#> ERR978234 1 0.0865 0.8348 0.964 0.000 0.000 0.000 0.000 NA
#> ERR978235 1 0.0865 0.8348 0.964 0.000 0.000 0.000 0.000 NA
#> ERR978236 1 0.0865 0.8348 0.964 0.000 0.000 0.000 0.000 NA
#> ERR978237 1 0.0865 0.8348 0.964 0.000 0.000 0.000 0.000 NA
#> ERR978238 1 0.0865 0.8348 0.964 0.000 0.000 0.000 0.000 NA
#> ERR978239 1 0.0865 0.8348 0.964 0.000 0.000 0.000 0.000 NA
#> ERR978240 1 0.1268 0.8348 0.952 0.004 0.000 0.000 0.008 NA
#> ERR978241 4 0.5045 0.8500 0.016 0.004 0.116 0.728 0.112 NA
#> ERR978242 4 0.5045 0.8500 0.016 0.004 0.116 0.728 0.112 NA
#> ERR978243 4 0.5045 0.8500 0.016 0.004 0.116 0.728 0.112 NA
#> ERR978244 4 0.5045 0.8500 0.016 0.004 0.116 0.728 0.112 NA
#> ERR978245 4 0.5045 0.8500 0.016 0.004 0.116 0.728 0.112 NA
#> ERR978246 4 0.5045 0.8500 0.016 0.004 0.116 0.728 0.112 NA
#> ERR978247 4 0.5045 0.8500 0.016 0.004 0.116 0.728 0.112 NA
#> ERR978248 5 0.8238 0.0125 0.028 0.116 0.032 0.276 0.388 NA
#> ERR978249 5 0.8238 0.0125 0.028 0.116 0.032 0.276 0.388 NA
#> ERR978250 5 0.8238 0.0125 0.028 0.116 0.032 0.276 0.388 NA
#> ERR978251 5 0.8238 0.0125 0.028 0.116 0.032 0.276 0.388 NA
#> ERR978252 5 0.8238 0.0125 0.028 0.116 0.032 0.276 0.388 NA
#> ERR978253 5 0.8238 0.0125 0.028 0.116 0.032 0.276 0.388 NA
#> ERR978254 5 0.8238 0.0125 0.028 0.116 0.032 0.276 0.388 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["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 14049 rows and 148 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 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 0.535 0.705 0.844 0.4752 0.520 0.520
#> 3 3 0.724 0.869 0.923 0.3162 0.609 0.403
#> 4 4 0.759 0.672 0.710 0.1720 0.807 0.554
#> 5 5 0.977 0.965 0.965 0.0953 0.923 0.716
#> 6 6 0.934 0.867 0.882 0.0274 0.986 0.931
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 5
There is also optional best \(k\) = 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0.9286 0.611 0.344 0.656
#> ERR978108 2 0.9286 0.611 0.344 0.656
#> ERR978109 2 0.9286 0.611 0.344 0.656
#> ERR978110 2 0.9286 0.611 0.344 0.656
#> ERR978111 2 0.9286 0.611 0.344 0.656
#> ERR978112 2 0.9286 0.611 0.344 0.656
#> ERR978113 2 0.9286 0.611 0.344 0.656
#> ERR978114 2 0.9286 0.611 0.344 0.656
#> ERR978115 2 0.9286 0.611 0.344 0.656
#> ERR978116 2 0.9286 0.611 0.344 0.656
#> ERR978117 2 0.9286 0.611 0.344 0.656
#> ERR978118 2 0.9286 0.611 0.344 0.656
#> ERR978119 2 0.9286 0.611 0.344 0.656
#> ERR978120 2 0.9286 0.611 0.344 0.656
#> ERR978121 2 0.9286 0.611 0.344 0.656
#> ERR978122 2 0.9286 0.611 0.344 0.656
#> ERR978123 2 0.3879 0.755 0.076 0.924
#> ERR978124 2 0.3879 0.755 0.076 0.924
#> ERR978125 2 0.3879 0.755 0.076 0.924
#> ERR978126 2 0.3879 0.755 0.076 0.924
#> ERR978127 2 0.3879 0.755 0.076 0.924
#> ERR978128 2 0.3879 0.755 0.076 0.924
#> ERR978129 2 0.3879 0.755 0.076 0.924
#> ERR978130 2 0.3879 0.755 0.076 0.924
#> ERR978131 2 0.3879 0.755 0.076 0.924
#> ERR978132 2 0.3879 0.755 0.076 0.924
#> ERR978133 2 0.3879 0.755 0.076 0.924
#> ERR978134 2 0.3879 0.755 0.076 0.924
#> ERR978135 2 0.3879 0.755 0.076 0.924
#> ERR978136 2 0.3879 0.755 0.076 0.924
#> ERR978137 2 0.3879 0.755 0.076 0.924
#> ERR978138 2 0.0672 0.807 0.008 0.992
#> ERR978139 2 0.0672 0.807 0.008 0.992
#> ERR978140 2 0.0672 0.807 0.008 0.992
#> ERR978141 2 0.0672 0.807 0.008 0.992
#> ERR978142 2 0.0672 0.807 0.008 0.992
#> ERR978143 2 0.0672 0.807 0.008 0.992
#> ERR978144 2 0.0672 0.807 0.008 0.992
#> ERR978145 2 0.0672 0.807 0.008 0.992
#> ERR978146 2 0.0672 0.807 0.008 0.992
#> ERR978147 2 0.0672 0.807 0.008 0.992
#> ERR978148 2 0.0672 0.807 0.008 0.992
#> ERR978149 2 0.0672 0.807 0.008 0.992
#> ERR978150 2 0.0672 0.807 0.008 0.992
#> ERR978151 2 0.0672 0.807 0.008 0.992
#> ERR978152 2 0.0672 0.807 0.008 0.992
#> ERR978153 1 0.0000 0.747 1.000 0.000
#> ERR978154 1 0.0000 0.747 1.000 0.000
#> ERR978155 1 0.0000 0.747 1.000 0.000
#> ERR978156 1 0.0000 0.747 1.000 0.000
#> ERR978157 1 0.0000 0.747 1.000 0.000
#> ERR978158 1 0.0000 0.747 1.000 0.000
#> ERR978159 1 0.0000 0.747 1.000 0.000
#> ERR978160 1 0.0000 0.747 1.000 0.000
#> ERR978161 1 0.0000 0.747 1.000 0.000
#> ERR978162 1 0.0000 0.747 1.000 0.000
#> ERR978163 1 0.0000 0.747 1.000 0.000
#> ERR978164 1 0.0000 0.747 1.000 0.000
#> ERR978165 1 0.0000 0.747 1.000 0.000
#> ERR978166 1 0.0000 0.747 1.000 0.000
#> ERR978167 1 0.0000 0.747 1.000 0.000
#> ERR978168 1 0.0000 0.747 1.000 0.000
#> ERR978169 1 0.9635 0.627 0.612 0.388
#> ERR978170 1 0.9635 0.627 0.612 0.388
#> ERR978171 1 0.9635 0.627 0.612 0.388
#> ERR978172 1 0.9635 0.627 0.612 0.388
#> ERR978173 1 0.9635 0.627 0.612 0.388
#> ERR978174 1 0.9635 0.627 0.612 0.388
#> ERR978175 1 0.9635 0.627 0.612 0.388
#> ERR978176 1 0.9635 0.627 0.612 0.388
#> ERR978177 1 0.9635 0.627 0.612 0.388
#> ERR978178 1 0.9635 0.627 0.612 0.388
#> ERR978179 1 0.9635 0.627 0.612 0.388
#> ERR978180 1 0.9635 0.627 0.612 0.388
#> ERR978181 1 0.9635 0.627 0.612 0.388
#> ERR978182 1 0.9635 0.627 0.612 0.388
#> ERR978183 2 0.9286 0.611 0.344 0.656
#> ERR978184 2 0.9286 0.611 0.344 0.656
#> ERR978185 2 0.9286 0.611 0.344 0.656
#> ERR978186 2 0.9286 0.611 0.344 0.656
#> ERR978187 2 0.9286 0.611 0.344 0.656
#> ERR978188 2 0.9286 0.611 0.344 0.656
#> ERR978189 2 0.9286 0.611 0.344 0.656
#> ERR978190 2 0.9286 0.611 0.344 0.656
#> ERR978191 2 0.9286 0.611 0.344 0.656
#> ERR978192 2 0.9286 0.611 0.344 0.656
#> ERR978193 2 0.9286 0.611 0.344 0.656
#> ERR978194 2 0.9286 0.611 0.344 0.656
#> ERR978195 2 0.9286 0.611 0.344 0.656
#> ERR978196 2 0.9286 0.611 0.344 0.656
#> ERR978197 2 0.0000 0.809 0.000 1.000
#> ERR978198 2 0.0000 0.809 0.000 1.000
#> ERR978199 2 0.0000 0.809 0.000 1.000
#> ERR978200 2 0.0000 0.809 0.000 1.000
#> ERR978201 2 0.0000 0.809 0.000 1.000
#> ERR978202 2 0.0000 0.809 0.000 1.000
#> ERR978203 2 0.0000 0.809 0.000 1.000
#> ERR978204 2 0.0000 0.809 0.000 1.000
#> ERR978205 2 0.0000 0.809 0.000 1.000
#> ERR978206 2 0.0000 0.809 0.000 1.000
#> ERR978207 2 0.0000 0.809 0.000 1.000
#> ERR978208 2 0.0000 0.809 0.000 1.000
#> ERR978209 2 0.0000 0.809 0.000 1.000
#> ERR978210 2 0.0000 0.809 0.000 1.000
#> ERR978211 2 0.0000 0.809 0.000 1.000
#> ERR978212 2 0.0000 0.809 0.000 1.000
#> ERR978213 2 0.0000 0.809 0.000 1.000
#> ERR978214 2 0.0000 0.809 0.000 1.000
#> ERR978215 2 0.0000 0.809 0.000 1.000
#> ERR978216 2 0.0000 0.809 0.000 1.000
#> ERR978217 2 0.0000 0.809 0.000 1.000
#> ERR978218 2 0.0000 0.809 0.000 1.000
#> ERR978219 2 0.0000 0.809 0.000 1.000
#> ERR978220 2 0.0000 0.809 0.000 1.000
#> ERR978221 2 0.0000 0.809 0.000 1.000
#> ERR978222 2 0.0000 0.809 0.000 1.000
#> ERR978223 2 0.0000 0.809 0.000 1.000
#> ERR978224 2 0.0000 0.809 0.000 1.000
#> ERR978225 2 0.0000 0.809 0.000 1.000
#> ERR978226 2 0.0000 0.809 0.000 1.000
#> ERR978227 1 0.0000 0.747 1.000 0.000
#> ERR978228 1 0.0000 0.747 1.000 0.000
#> ERR978229 1 0.0000 0.747 1.000 0.000
#> ERR978230 1 0.0000 0.747 1.000 0.000
#> ERR978231 1 0.0000 0.747 1.000 0.000
#> ERR978232 1 0.0000 0.747 1.000 0.000
#> ERR978233 1 0.0000 0.747 1.000 0.000
#> ERR978234 1 0.0000 0.747 1.000 0.000
#> ERR978235 1 0.0000 0.747 1.000 0.000
#> ERR978236 1 0.0000 0.747 1.000 0.000
#> ERR978237 1 0.0000 0.747 1.000 0.000
#> ERR978238 1 0.0000 0.747 1.000 0.000
#> ERR978239 1 0.0000 0.747 1.000 0.000
#> ERR978240 1 0.0000 0.747 1.000 0.000
#> ERR978241 1 0.9635 0.627 0.612 0.388
#> ERR978242 1 0.9635 0.627 0.612 0.388
#> ERR978243 1 0.9635 0.627 0.612 0.388
#> ERR978244 1 0.9635 0.627 0.612 0.388
#> ERR978245 1 0.9635 0.627 0.612 0.388
#> ERR978246 1 0.9635 0.627 0.612 0.388
#> ERR978247 1 0.9635 0.627 0.612 0.388
#> ERR978248 1 0.9996 0.385 0.512 0.488
#> ERR978249 1 0.9996 0.385 0.512 0.488
#> ERR978250 1 0.9996 0.385 0.512 0.488
#> ERR978251 1 0.9996 0.385 0.512 0.488
#> ERR978252 1 0.9996 0.385 0.512 0.488
#> ERR978253 1 0.9996 0.385 0.512 0.488
#> ERR978254 1 0.9996 0.385 0.512 0.488
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978108 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978109 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978110 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978111 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978112 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978113 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978114 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978115 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978116 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978117 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978118 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978119 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978120 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978121 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978122 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978123 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978124 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978125 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978126 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978127 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978128 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978129 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978130 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978131 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978132 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978133 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978134 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978135 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978136 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978137 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978138 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978139 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978140 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978141 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978142 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978143 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978144 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978145 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978146 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978147 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978148 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978149 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978150 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978151 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978152 3 0.0000 0.853 0.000 0.000 1.000
#> ERR978153 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978169 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978170 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978171 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978172 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978173 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978174 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978175 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978176 3 0.7453 0.634 0.092 0.228 0.680
#> ERR978177 3 0.7453 0.634 0.092 0.228 0.680
#> ERR978178 3 0.7453 0.634 0.092 0.228 0.680
#> ERR978179 3 0.7453 0.634 0.092 0.228 0.680
#> ERR978180 3 0.7453 0.634 0.092 0.228 0.680
#> ERR978181 3 0.7453 0.634 0.092 0.228 0.680
#> ERR978182 3 0.7453 0.634 0.092 0.228 0.680
#> ERR978183 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978184 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978185 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978186 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978187 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978188 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978189 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978190 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978191 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978192 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978193 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978194 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978195 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978196 2 0.0237 0.985 0.000 0.996 0.004
#> ERR978197 3 0.1860 0.843 0.000 0.052 0.948
#> ERR978198 3 0.1860 0.843 0.000 0.052 0.948
#> ERR978199 3 0.1860 0.843 0.000 0.052 0.948
#> ERR978200 3 0.1860 0.843 0.000 0.052 0.948
#> ERR978201 3 0.1860 0.843 0.000 0.052 0.948
#> ERR978202 3 0.1860 0.843 0.000 0.052 0.948
#> ERR978203 3 0.1860 0.843 0.000 0.052 0.948
#> ERR978204 3 0.2796 0.827 0.000 0.092 0.908
#> ERR978205 3 0.2796 0.827 0.000 0.092 0.908
#> ERR978206 3 0.2796 0.827 0.000 0.092 0.908
#> ERR978207 3 0.2796 0.827 0.000 0.092 0.908
#> ERR978208 3 0.2796 0.827 0.000 0.092 0.908
#> ERR978209 3 0.2796 0.827 0.000 0.092 0.908
#> ERR978210 3 0.2796 0.827 0.000 0.092 0.908
#> ERR978211 3 0.2796 0.827 0.000 0.092 0.908
#> ERR978212 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978213 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978214 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978215 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978216 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978217 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978218 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978219 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978220 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978221 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978222 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978223 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978224 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978225 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978226 3 0.5733 0.597 0.000 0.324 0.676
#> ERR978227 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978241 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978242 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978243 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978244 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978245 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978246 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978247 3 0.5505 0.780 0.096 0.088 0.816
#> ERR978248 2 0.2689 0.935 0.032 0.932 0.036
#> ERR978249 2 0.2689 0.935 0.032 0.932 0.036
#> ERR978250 2 0.2689 0.935 0.032 0.932 0.036
#> ERR978251 2 0.2689 0.935 0.032 0.932 0.036
#> ERR978252 2 0.2689 0.935 0.032 0.932 0.036
#> ERR978253 2 0.2689 0.935 0.032 0.932 0.036
#> ERR978254 2 0.2689 0.935 0.032 0.932 0.036
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978108 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978109 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978110 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978111 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978112 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978113 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978114 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978115 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978116 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978117 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978118 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978119 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978120 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978121 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978122 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978123 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978124 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978125 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978126 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978127 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978128 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978129 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978130 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978131 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978132 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978133 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978134 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978135 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978136 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978137 4 0.510 0.3045 0.000 0.008 0.380 0.612
#> ERR978138 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978139 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978140 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978141 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978142 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978143 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978144 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978145 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978146 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978147 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978148 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978149 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978150 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978151 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978152 3 0.147 0.8922 0.000 0.000 0.948 0.052
#> ERR978153 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978154 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978155 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978156 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978157 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978158 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978159 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978160 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978161 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978162 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978163 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978164 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978165 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978166 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978167 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978168 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978169 4 0.536 0.1961 0.012 0.004 0.372 0.612
#> ERR978170 4 0.536 0.1961 0.012 0.004 0.372 0.612
#> ERR978171 4 0.536 0.1961 0.012 0.004 0.372 0.612
#> ERR978172 4 0.536 0.1961 0.012 0.004 0.372 0.612
#> ERR978173 4 0.536 0.1961 0.012 0.004 0.372 0.612
#> ERR978174 4 0.536 0.1961 0.012 0.004 0.372 0.612
#> ERR978175 4 0.536 0.1961 0.012 0.004 0.372 0.612
#> ERR978176 4 0.580 0.0958 0.016 0.008 0.464 0.512
#> ERR978177 4 0.580 0.0958 0.016 0.008 0.464 0.512
#> ERR978178 4 0.580 0.0958 0.016 0.008 0.464 0.512
#> ERR978179 4 0.580 0.0958 0.016 0.008 0.464 0.512
#> ERR978180 4 0.580 0.0958 0.016 0.008 0.464 0.512
#> ERR978181 4 0.580 0.0958 0.016 0.008 0.464 0.512
#> ERR978182 4 0.580 0.0958 0.016 0.008 0.464 0.512
#> ERR978183 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978184 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978185 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978186 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978187 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978188 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978189 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978190 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978191 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978192 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978193 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978194 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978195 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978196 2 0.000 1.0000 0.000 1.000 0.000 0.000
#> ERR978197 4 0.524 0.2823 0.000 0.008 0.436 0.556
#> ERR978198 4 0.524 0.2823 0.000 0.008 0.436 0.556
#> ERR978199 4 0.524 0.2823 0.000 0.008 0.436 0.556
#> ERR978200 4 0.524 0.2823 0.000 0.008 0.436 0.556
#> ERR978201 4 0.524 0.2823 0.000 0.008 0.436 0.556
#> ERR978202 4 0.524 0.2823 0.000 0.008 0.436 0.556
#> ERR978203 4 0.524 0.2823 0.000 0.008 0.436 0.556
#> ERR978204 4 0.525 0.2779 0.000 0.008 0.440 0.552
#> ERR978205 4 0.525 0.2779 0.000 0.008 0.440 0.552
#> ERR978206 4 0.525 0.2779 0.000 0.008 0.440 0.552
#> ERR978207 4 0.525 0.2779 0.000 0.008 0.440 0.552
#> ERR978208 4 0.525 0.2779 0.000 0.008 0.440 0.552
#> ERR978209 4 0.525 0.2779 0.000 0.008 0.440 0.552
#> ERR978210 4 0.525 0.2779 0.000 0.008 0.440 0.552
#> ERR978211 4 0.525 0.2779 0.000 0.008 0.440 0.552
#> ERR978212 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978213 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978214 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978215 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978216 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978217 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978218 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978219 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978220 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978221 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978222 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978223 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978224 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978225 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978226 3 0.222 0.8941 0.000 0.060 0.924 0.016
#> ERR978227 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978228 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978229 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978230 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978231 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978232 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978233 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978234 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978235 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978236 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978237 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978238 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978239 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978240 1 0.000 1.0000 1.000 0.000 0.000 0.000
#> ERR978241 4 0.538 0.1937 0.012 0.004 0.376 0.608
#> ERR978242 4 0.538 0.1937 0.012 0.004 0.376 0.608
#> ERR978243 4 0.538 0.1937 0.012 0.004 0.376 0.608
#> ERR978244 4 0.538 0.1937 0.012 0.004 0.376 0.608
#> ERR978245 4 0.538 0.1937 0.012 0.004 0.376 0.608
#> ERR978246 4 0.538 0.1937 0.012 0.004 0.376 0.608
#> ERR978247 4 0.538 0.1937 0.012 0.004 0.376 0.608
#> ERR978248 4 0.819 0.0696 0.008 0.324 0.312 0.356
#> ERR978249 4 0.819 0.0696 0.008 0.324 0.312 0.356
#> ERR978250 4 0.819 0.0696 0.008 0.324 0.312 0.356
#> ERR978251 4 0.819 0.0696 0.008 0.324 0.312 0.356
#> ERR978252 4 0.819 0.0696 0.008 0.324 0.312 0.356
#> ERR978253 4 0.819 0.0696 0.008 0.324 0.312 0.356
#> ERR978254 4 0.819 0.0696 0.008 0.324 0.312 0.356
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978123 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978124 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978125 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978126 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978127 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978128 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978129 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978130 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978131 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978132 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978133 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978134 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978135 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978136 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978137 3 0.1597 0.966 0 0.000 0.940 0.012 0.048
#> ERR978138 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978139 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978140 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978141 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978142 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978143 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978144 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978145 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978146 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978147 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978148 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978149 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978150 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978151 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978152 5 0.2473 0.927 0 0.000 0.032 0.072 0.896
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978170 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978171 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978172 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978173 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978174 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978175 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978176 4 0.0671 0.947 0 0.000 0.004 0.980 0.016
#> ERR978177 4 0.0671 0.947 0 0.000 0.004 0.980 0.016
#> ERR978178 4 0.0671 0.947 0 0.000 0.004 0.980 0.016
#> ERR978179 4 0.0671 0.947 0 0.000 0.004 0.980 0.016
#> ERR978180 4 0.0671 0.947 0 0.000 0.004 0.980 0.016
#> ERR978181 4 0.0671 0.947 0 0.000 0.004 0.980 0.016
#> ERR978182 4 0.0671 0.947 0 0.000 0.004 0.980 0.016
#> ERR978183 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978184 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978185 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978186 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978187 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978188 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978189 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978190 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978191 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978192 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978193 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978194 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978195 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978196 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978197 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978198 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978199 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978200 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978201 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978202 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978203 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978204 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978205 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978206 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978207 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978208 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978209 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978210 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978211 3 0.0162 0.966 0 0.000 0.996 0.000 0.004
#> ERR978212 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978213 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978214 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978215 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978216 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978217 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978218 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978219 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978220 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978221 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978222 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978223 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978224 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978225 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978226 5 0.1638 0.929 0 0.000 0.064 0.004 0.932
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978241 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978242 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978243 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978244 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978245 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978246 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978247 4 0.0898 0.951 0 0.000 0.020 0.972 0.008
#> ERR978248 4 0.3612 0.872 0 0.064 0.004 0.832 0.100
#> ERR978249 4 0.3612 0.872 0 0.064 0.004 0.832 0.100
#> ERR978250 4 0.3612 0.872 0 0.064 0.004 0.832 0.100
#> ERR978251 4 0.3612 0.872 0 0.064 0.004 0.832 0.100
#> ERR978252 4 0.3612 0.872 0 0.064 0.004 0.832 0.100
#> ERR978253 4 0.3612 0.872 0 0.064 0.004 0.832 0.100
#> ERR978254 4 0.3612 0.872 0 0.064 0.004 0.832 0.100
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 0.9717 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978123 3 0.3062 0.9100 0.000 0.000 0.816 0.000 0.024 0.160
#> ERR978124 3 0.3062 0.9100 0.000 0.000 0.816 0.000 0.024 0.160
#> ERR978125 3 0.3062 0.9100 0.000 0.000 0.816 0.000 0.024 0.160
#> ERR978126 3 0.3062 0.9100 0.000 0.000 0.816 0.000 0.024 0.160
#> ERR978127 3 0.3062 0.9100 0.000 0.000 0.816 0.000 0.024 0.160
#> ERR978128 3 0.3062 0.9100 0.000 0.000 0.816 0.000 0.024 0.160
#> ERR978129 3 0.3062 0.9100 0.000 0.000 0.816 0.000 0.024 0.160
#> ERR978130 3 0.3062 0.9100 0.000 0.000 0.816 0.000 0.024 0.160
#> ERR978131 3 0.3025 0.9100 0.000 0.000 0.820 0.000 0.024 0.156
#> ERR978132 3 0.3025 0.9100 0.000 0.000 0.820 0.000 0.024 0.156
#> ERR978133 3 0.3025 0.9100 0.000 0.000 0.820 0.000 0.024 0.156
#> ERR978134 3 0.3025 0.9100 0.000 0.000 0.820 0.000 0.024 0.156
#> ERR978135 3 0.3025 0.9100 0.000 0.000 0.820 0.000 0.024 0.156
#> ERR978136 3 0.3025 0.9100 0.000 0.000 0.820 0.000 0.024 0.156
#> ERR978137 3 0.3025 0.9100 0.000 0.000 0.820 0.000 0.024 0.156
#> ERR978138 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978139 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978140 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978141 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978142 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978143 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978144 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978145 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978146 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978147 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978148 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978149 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978150 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978151 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978152 5 0.0547 0.8043 0.000 0.000 0.000 0.020 0.980 0.000
#> ERR978153 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 0.9871 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000 0.000
#> ERR978170 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000 0.000
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#> ERR978175 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000 0.000
#> ERR978176 4 0.3756 0.0849 0.000 0.000 0.000 0.644 0.004 0.352
#> ERR978177 4 0.3756 0.0849 0.000 0.000 0.000 0.644 0.004 0.352
#> ERR978178 4 0.3756 0.0849 0.000 0.000 0.000 0.644 0.004 0.352
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#> ERR978182 4 0.3756 0.0849 0.000 0.000 0.000 0.644 0.004 0.352
#> ERR978183 2 0.1327 0.9674 0.000 0.936 0.000 0.000 0.000 0.064
#> ERR978184 2 0.1327 0.9674 0.000 0.936 0.000 0.000 0.000 0.064
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#> ERR978197 3 0.0508 0.9097 0.000 0.000 0.984 0.000 0.004 0.012
#> ERR978198 3 0.0508 0.9097 0.000 0.000 0.984 0.000 0.004 0.012
#> ERR978199 3 0.0508 0.9097 0.000 0.000 0.984 0.000 0.004 0.012
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#> ERR978211 3 0.0508 0.9087 0.000 0.000 0.984 0.000 0.004 0.012
#> ERR978212 5 0.3954 0.7966 0.000 0.000 0.012 0.000 0.636 0.352
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#> ERR978227 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978228 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978229 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978230 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978231 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978232 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978233 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978234 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978235 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978236 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978237 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978238 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978239 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978240 1 0.0790 0.9852 0.968 0.000 0.000 0.000 0.000 0.032
#> ERR978241 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000 0.000
#> ERR978242 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000 0.000
#> ERR978243 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000 0.000
#> ERR978244 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000 0.000
#> ERR978245 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000 0.000
#> ERR978246 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000 0.000
#> ERR978247 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000 0.000
#> ERR978248 6 0.5395 1.0000 0.000 0.080 0.000 0.404 0.012 0.504
#> ERR978249 6 0.5395 1.0000 0.000 0.080 0.000 0.404 0.012 0.504
#> ERR978250 6 0.5395 1.0000 0.000 0.080 0.000 0.404 0.012 0.504
#> ERR978251 6 0.5395 1.0000 0.000 0.080 0.000 0.404 0.012 0.504
#> ERR978252 6 0.5395 1.0000 0.000 0.080 0.000 0.404 0.012 0.504
#> ERR978253 6 0.5395 1.0000 0.000 0.080 0.000 0.404 0.012 0.504
#> ERR978254 6 0.5395 1.0000 0.000 0.080 0.000 0.404 0.012 0.504
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 14049 rows and 148 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.3261 0.675 0.675
#> 3 3 1.000 0.971 0.987 0.7712 0.757 0.640
#> 4 4 0.962 0.960 0.980 0.2710 0.840 0.630
#> 5 5 1.000 0.973 0.985 0.0898 0.929 0.737
#> 6 6 0.940 0.916 0.933 0.0344 0.970 0.849
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
#> ERR978109 2 0 1 0 1
#> ERR978110 2 0 1 0 1
#> ERR978111 2 0 1 0 1
#> ERR978112 2 0 1 0 1
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#> ERR978253 2 0 1 0 1
#> ERR978254 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.000 1.000 0 1.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000
#> ERR978123 3 0.000 0.977 0 0.000 1.000
#> ERR978124 3 0.000 0.977 0 0.000 1.000
#> ERR978125 3 0.000 0.977 0 0.000 1.000
#> ERR978126 3 0.000 0.977 0 0.000 1.000
#> ERR978127 3 0.000 0.977 0 0.000 1.000
#> ERR978128 3 0.000 0.977 0 0.000 1.000
#> ERR978129 3 0.000 0.977 0 0.000 1.000
#> ERR978130 3 0.000 0.977 0 0.000 1.000
#> ERR978131 3 0.000 0.977 0 0.000 1.000
#> ERR978132 3 0.000 0.977 0 0.000 1.000
#> ERR978133 3 0.000 0.977 0 0.000 1.000
#> ERR978134 3 0.000 0.977 0 0.000 1.000
#> ERR978135 3 0.000 0.977 0 0.000 1.000
#> ERR978136 3 0.000 0.977 0 0.000 1.000
#> ERR978137 3 0.000 0.977 0 0.000 1.000
#> ERR978138 3 0.000 0.977 0 0.000 1.000
#> ERR978139 3 0.000 0.977 0 0.000 1.000
#> ERR978140 3 0.000 0.977 0 0.000 1.000
#> ERR978141 3 0.000 0.977 0 0.000 1.000
#> ERR978142 3 0.000 0.977 0 0.000 1.000
#> ERR978143 3 0.000 0.977 0 0.000 1.000
#> ERR978144 3 0.000 0.977 0 0.000 1.000
#> ERR978145 3 0.000 0.977 0 0.000 1.000
#> ERR978146 3 0.000 0.977 0 0.000 1.000
#> ERR978147 3 0.000 0.977 0 0.000 1.000
#> ERR978148 3 0.000 0.977 0 0.000 1.000
#> ERR978149 3 0.000 0.977 0 0.000 1.000
#> ERR978150 3 0.000 0.977 0 0.000 1.000
#> ERR978151 3 0.000 0.977 0 0.000 1.000
#> ERR978152 3 0.000 0.977 0 0.000 1.000
#> ERR978153 1 0.000 1.000 1 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000
#> ERR978169 3 0.000 0.977 0 0.000 1.000
#> ERR978170 3 0.000 0.977 0 0.000 1.000
#> ERR978171 3 0.000 0.977 0 0.000 1.000
#> ERR978172 3 0.000 0.977 0 0.000 1.000
#> ERR978173 3 0.000 0.977 0 0.000 1.000
#> ERR978174 3 0.000 0.977 0 0.000 1.000
#> ERR978175 3 0.000 0.977 0 0.000 1.000
#> ERR978176 3 0.000 0.977 0 0.000 1.000
#> ERR978177 3 0.000 0.977 0 0.000 1.000
#> ERR978178 3 0.000 0.977 0 0.000 1.000
#> ERR978179 3 0.000 0.977 0 0.000 1.000
#> ERR978180 3 0.000 0.977 0 0.000 1.000
#> ERR978181 3 0.000 0.977 0 0.000 1.000
#> ERR978182 3 0.000 0.977 0 0.000 1.000
#> ERR978183 2 0.000 1.000 0 1.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000
#> ERR978197 3 0.000 0.977 0 0.000 1.000
#> ERR978198 3 0.000 0.977 0 0.000 1.000
#> ERR978199 3 0.000 0.977 0 0.000 1.000
#> ERR978200 3 0.000 0.977 0 0.000 1.000
#> ERR978201 3 0.000 0.977 0 0.000 1.000
#> ERR978202 3 0.000 0.977 0 0.000 1.000
#> ERR978203 3 0.000 0.977 0 0.000 1.000
#> ERR978204 3 0.000 0.977 0 0.000 1.000
#> ERR978205 3 0.000 0.977 0 0.000 1.000
#> ERR978206 3 0.000 0.977 0 0.000 1.000
#> ERR978207 3 0.000 0.977 0 0.000 1.000
#> ERR978208 3 0.000 0.977 0 0.000 1.000
#> ERR978209 3 0.000 0.977 0 0.000 1.000
#> ERR978210 3 0.000 0.977 0 0.000 1.000
#> ERR978211 3 0.000 0.977 0 0.000 1.000
#> ERR978212 3 0.000 0.977 0 0.000 1.000
#> ERR978213 3 0.000 0.977 0 0.000 1.000
#> ERR978214 3 0.000 0.977 0 0.000 1.000
#> ERR978215 3 0.000 0.977 0 0.000 1.000
#> ERR978216 3 0.000 0.977 0 0.000 1.000
#> ERR978217 3 0.000 0.977 0 0.000 1.000
#> ERR978218 3 0.000 0.977 0 0.000 1.000
#> ERR978219 3 0.000 0.977 0 0.000 1.000
#> ERR978220 3 0.000 0.977 0 0.000 1.000
#> ERR978221 3 0.000 0.977 0 0.000 1.000
#> ERR978222 3 0.000 0.977 0 0.000 1.000
#> ERR978223 3 0.000 0.977 0 0.000 1.000
#> ERR978224 3 0.000 0.977 0 0.000 1.000
#> ERR978225 3 0.000 0.977 0 0.000 1.000
#> ERR978226 3 0.000 0.977 0 0.000 1.000
#> ERR978227 1 0.000 1.000 1 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000
#> ERR978241 3 0.000 0.977 0 0.000 1.000
#> ERR978242 3 0.000 0.977 0 0.000 1.000
#> ERR978243 3 0.000 0.977 0 0.000 1.000
#> ERR978244 3 0.000 0.977 0 0.000 1.000
#> ERR978245 3 0.000 0.977 0 0.000 1.000
#> ERR978246 3 0.000 0.977 0 0.000 1.000
#> ERR978247 3 0.000 0.977 0 0.000 1.000
#> ERR978248 3 0.546 0.624 0 0.288 0.712
#> ERR978249 3 0.529 0.658 0 0.268 0.732
#> ERR978250 3 0.546 0.624 0 0.288 0.712
#> ERR978251 3 0.514 0.683 0 0.252 0.748
#> ERR978252 3 0.522 0.670 0 0.260 0.740
#> ERR978253 3 0.540 0.638 0 0.280 0.720
#> ERR978254 3 0.546 0.624 0 0.288 0.712
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978123 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978124 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978125 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978126 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978127 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978128 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978129 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978130 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978131 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978132 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978133 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978134 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978135 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978136 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978137 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978138 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978139 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978140 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978141 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978142 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978143 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978144 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978145 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978146 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978147 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978148 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978149 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978150 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978151 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978152 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978153 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978169 4 0.270 0.848 0 0.000 0.124 0.876
#> ERR978170 4 0.276 0.844 0 0.000 0.128 0.872
#> ERR978171 4 0.336 0.790 0 0.000 0.176 0.824
#> ERR978172 4 0.327 0.800 0 0.000 0.168 0.832
#> ERR978173 4 0.312 0.814 0 0.000 0.156 0.844
#> ERR978174 4 0.215 0.880 0 0.000 0.088 0.912
#> ERR978175 4 0.307 0.819 0 0.000 0.152 0.848
#> ERR978176 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978177 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978178 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978179 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978180 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978181 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978182 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978183 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978197 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978198 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978199 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978200 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978201 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978202 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978203 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978204 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978205 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978206 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978207 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978208 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978209 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978210 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978211 3 0.000 1.000 0 0.000 1.000 0.000
#> ERR978212 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978213 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978214 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978215 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978216 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978217 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978218 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978219 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978220 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978221 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978222 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978223 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978224 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978225 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978226 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978227 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978241 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978242 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978243 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978244 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978245 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978246 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978247 4 0.000 0.947 0 0.000 0.000 1.000
#> ERR978248 4 0.436 0.629 0 0.292 0.000 0.708
#> ERR978249 4 0.422 0.661 0 0.272 0.000 0.728
#> ERR978250 4 0.430 0.643 0 0.284 0.000 0.716
#> ERR978251 4 0.401 0.702 0 0.244 0.000 0.756
#> ERR978252 4 0.416 0.673 0 0.264 0.000 0.736
#> ERR978253 4 0.433 0.636 0 0.288 0.000 0.712
#> ERR978254 4 0.433 0.636 0 0.288 0.000 0.712
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978123 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978124 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978125 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978126 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978127 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978128 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978129 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978130 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978131 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978132 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978133 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978134 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978135 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978136 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978137 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978138 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978139 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978140 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978141 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978142 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978143 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978144 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978145 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978146 5 0.0290 0.939 0 0.000 0.008 0.000 0.992
#> ERR978147 5 0.0290 0.939 0 0.000 0.008 0.000 0.992
#> ERR978148 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978149 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978150 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978151 5 0.0290 0.939 0 0.000 0.008 0.000 0.992
#> ERR978152 5 0.0162 0.942 0 0.000 0.004 0.000 0.996
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978170 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978171 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978172 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978173 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978174 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978175 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978176 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978177 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978178 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978179 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978180 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978181 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978182 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978183 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978184 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978185 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978186 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978187 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978188 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978189 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978190 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978191 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978192 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978193 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978194 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978195 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978196 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978197 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978198 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978199 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978200 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978201 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978202 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978203 3 0.0000 0.992 0 0.000 1.000 0.000 0.000
#> ERR978204 3 0.0794 0.975 0 0.000 0.972 0.000 0.028
#> ERR978205 3 0.0794 0.975 0 0.000 0.972 0.000 0.028
#> ERR978206 3 0.0794 0.975 0 0.000 0.972 0.000 0.028
#> ERR978207 3 0.0794 0.975 0 0.000 0.972 0.000 0.028
#> ERR978208 3 0.0794 0.975 0 0.000 0.972 0.000 0.028
#> ERR978209 3 0.0794 0.975 0 0.000 0.972 0.000 0.028
#> ERR978210 3 0.0794 0.975 0 0.000 0.972 0.000 0.028
#> ERR978211 3 0.0290 0.988 0 0.000 0.992 0.000 0.008
#> ERR978212 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978213 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978214 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978215 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978216 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978217 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978218 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978219 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978220 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978221 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978222 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978223 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978224 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978225 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978226 5 0.0000 0.944 0 0.000 0.000 0.000 1.000
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978241 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978242 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978243 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978244 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978245 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978246 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978247 4 0.0000 1.000 0 0.000 0.000 1.000 0.000
#> ERR978248 5 0.4800 0.725 0 0.196 0.000 0.088 0.716
#> ERR978249 5 0.4800 0.725 0 0.196 0.000 0.088 0.716
#> ERR978250 5 0.4800 0.725 0 0.196 0.000 0.088 0.716
#> ERR978251 5 0.4734 0.733 0 0.188 0.000 0.088 0.724
#> ERR978252 5 0.4800 0.725 0 0.196 0.000 0.088 0.716
#> ERR978253 5 0.4800 0.725 0 0.196 0.000 0.088 0.716
#> ERR978254 5 0.4800 0.725 0 0.196 0.000 0.088 0.716
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978123 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978124 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978125 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978126 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978127 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978128 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978129 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978130 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978131 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978132 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978133 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978134 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978135 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978136 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978137 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978138 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978139 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978140 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978141 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978142 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978143 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978144 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978145 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978146 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978147 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978148 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978149 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978150 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978151 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978152 5 0.000 1.000 0 0 0.000 0.000 1.000 0.000
#> ERR978153 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978169 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978170 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978171 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978172 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978173 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978174 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978175 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978176 4 0.374 0.710 0 0 0.000 0.608 0.000 0.392
#> ERR978177 4 0.374 0.710 0 0 0.000 0.608 0.000 0.392
#> ERR978178 4 0.374 0.710 0 0 0.000 0.608 0.000 0.392
#> ERR978179 4 0.374 0.710 0 0 0.000 0.608 0.000 0.392
#> ERR978180 4 0.374 0.710 0 0 0.000 0.608 0.000 0.392
#> ERR978181 4 0.374 0.710 0 0 0.000 0.608 0.000 0.392
#> ERR978182 4 0.374 0.710 0 0 0.000 0.608 0.000 0.392
#> ERR978183 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978197 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978198 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978199 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978200 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978201 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978202 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978203 3 0.000 0.951 0 0 1.000 0.000 0.000 0.000
#> ERR978204 3 0.270 0.812 0 0 0.812 0.000 0.000 0.188
#> ERR978205 3 0.234 0.851 0 0 0.852 0.000 0.000 0.148
#> ERR978206 3 0.249 0.837 0 0 0.836 0.000 0.000 0.164
#> ERR978207 3 0.273 0.807 0 0 0.808 0.000 0.000 0.192
#> ERR978208 3 0.273 0.807 0 0 0.808 0.000 0.000 0.192
#> ERR978209 3 0.263 0.821 0 0 0.820 0.000 0.000 0.180
#> ERR978210 3 0.263 0.821 0 0 0.820 0.000 0.000 0.180
#> ERR978211 3 0.144 0.908 0 0 0.928 0.000 0.000 0.072
#> ERR978212 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978213 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978214 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978215 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978216 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978217 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978218 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978219 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978220 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978221 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978222 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978223 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978224 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978225 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978226 6 0.374 0.768 0 0 0.000 0.000 0.392 0.608
#> ERR978227 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978241 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978242 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978243 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978244 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978245 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978246 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978247 4 0.000 0.875 0 0 0.000 1.000 0.000 0.000
#> ERR978248 6 0.000 0.614 0 0 0.000 0.000 0.000 1.000
#> ERR978249 6 0.000 0.614 0 0 0.000 0.000 0.000 1.000
#> ERR978250 6 0.000 0.614 0 0 0.000 0.000 0.000 1.000
#> ERR978251 6 0.000 0.614 0 0 0.000 0.000 0.000 1.000
#> ERR978252 6 0.000 0.614 0 0 0.000 0.000 0.000 1.000
#> ERR978253 6 0.000 0.614 0 0 0.000 0.000 0.000 1.000
#> ERR978254 6 0.000 0.614 0 0 0.000 0.000 0.000 1.000
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 14049 rows and 148 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.3261 0.675 0.675
#> 3 3 0.616 0.782 0.856 0.8619 0.757 0.640
#> 4 4 0.741 0.825 0.877 0.1953 0.840 0.630
#> 5 5 0.811 0.774 0.808 0.0945 0.941 0.782
#> 6 6 0.802 0.783 0.777 0.0258 0.958 0.800
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
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
#> ERR978109 2 0 1 0 1
#> ERR978110 2 0 1 0 1
#> ERR978111 2 0 1 0 1
#> ERR978112 2 0 1 0 1
#> ERR978113 2 0 1 0 1
#> ERR978114 2 0 1 0 1
#> ERR978115 2 0 1 0 1
#> ERR978116 2 0 1 0 1
#> ERR978117 2 0 1 0 1
#> ERR978118 2 0 1 0 1
#> ERR978119 2 0 1 0 1
#> ERR978120 2 0 1 0 1
#> ERR978121 2 0 1 0 1
#> ERR978122 2 0 1 0 1
#> ERR978123 2 0 1 0 1
#> ERR978124 2 0 1 0 1
#> ERR978125 2 0 1 0 1
#> ERR978126 2 0 1 0 1
#> ERR978127 2 0 1 0 1
#> ERR978128 2 0 1 0 1
#> ERR978129 2 0 1 0 1
#> ERR978130 2 0 1 0 1
#> ERR978131 2 0 1 0 1
#> ERR978132 2 0 1 0 1
#> ERR978133 2 0 1 0 1
#> ERR978134 2 0 1 0 1
#> ERR978135 2 0 1 0 1
#> ERR978136 2 0 1 0 1
#> ERR978137 2 0 1 0 1
#> ERR978138 2 0 1 0 1
#> ERR978139 2 0 1 0 1
#> ERR978140 2 0 1 0 1
#> ERR978141 2 0 1 0 1
#> ERR978142 2 0 1 0 1
#> ERR978143 2 0 1 0 1
#> ERR978144 2 0 1 0 1
#> ERR978145 2 0 1 0 1
#> ERR978146 2 0 1 0 1
#> ERR978147 2 0 1 0 1
#> ERR978148 2 0 1 0 1
#> ERR978149 2 0 1 0 1
#> ERR978150 2 0 1 0 1
#> ERR978151 2 0 1 0 1
#> ERR978152 2 0 1 0 1
#> ERR978153 1 0 1 1 0
#> ERR978154 1 0 1 1 0
#> ERR978155 1 0 1 1 0
#> ERR978156 1 0 1 1 0
#> ERR978157 1 0 1 1 0
#> ERR978158 1 0 1 1 0
#> ERR978159 1 0 1 1 0
#> ERR978160 1 0 1 1 0
#> ERR978161 1 0 1 1 0
#> ERR978162 1 0 1 1 0
#> ERR978163 1 0 1 1 0
#> ERR978164 1 0 1 1 0
#> ERR978165 1 0 1 1 0
#> ERR978166 1 0 1 1 0
#> ERR978167 1 0 1 1 0
#> ERR978168 1 0 1 1 0
#> ERR978169 2 0 1 0 1
#> ERR978170 2 0 1 0 1
#> ERR978171 2 0 1 0 1
#> ERR978172 2 0 1 0 1
#> ERR978173 2 0 1 0 1
#> ERR978174 2 0 1 0 1
#> ERR978175 2 0 1 0 1
#> ERR978176 2 0 1 0 1
#> ERR978177 2 0 1 0 1
#> ERR978178 2 0 1 0 1
#> ERR978179 2 0 1 0 1
#> ERR978180 2 0 1 0 1
#> ERR978181 2 0 1 0 1
#> ERR978182 2 0 1 0 1
#> ERR978183 2 0 1 0 1
#> ERR978184 2 0 1 0 1
#> ERR978185 2 0 1 0 1
#> ERR978186 2 0 1 0 1
#> ERR978187 2 0 1 0 1
#> ERR978188 2 0 1 0 1
#> ERR978189 2 0 1 0 1
#> ERR978190 2 0 1 0 1
#> ERR978191 2 0 1 0 1
#> ERR978192 2 0 1 0 1
#> ERR978193 2 0 1 0 1
#> ERR978194 2 0 1 0 1
#> ERR978195 2 0 1 0 1
#> ERR978196 2 0 1 0 1
#> ERR978197 2 0 1 0 1
#> ERR978198 2 0 1 0 1
#> ERR978199 2 0 1 0 1
#> ERR978200 2 0 1 0 1
#> ERR978201 2 0 1 0 1
#> ERR978202 2 0 1 0 1
#> ERR978203 2 0 1 0 1
#> ERR978204 2 0 1 0 1
#> ERR978205 2 0 1 0 1
#> ERR978206 2 0 1 0 1
#> ERR978207 2 0 1 0 1
#> ERR978208 2 0 1 0 1
#> ERR978209 2 0 1 0 1
#> ERR978210 2 0 1 0 1
#> ERR978211 2 0 1 0 1
#> ERR978212 2 0 1 0 1
#> ERR978213 2 0 1 0 1
#> ERR978214 2 0 1 0 1
#> ERR978215 2 0 1 0 1
#> ERR978216 2 0 1 0 1
#> ERR978217 2 0 1 0 1
#> ERR978218 2 0 1 0 1
#> ERR978219 2 0 1 0 1
#> ERR978220 2 0 1 0 1
#> ERR978221 2 0 1 0 1
#> ERR978222 2 0 1 0 1
#> ERR978223 2 0 1 0 1
#> ERR978224 2 0 1 0 1
#> ERR978225 2 0 1 0 1
#> ERR978226 2 0 1 0 1
#> ERR978227 1 0 1 1 0
#> ERR978228 1 0 1 1 0
#> ERR978229 1 0 1 1 0
#> ERR978230 1 0 1 1 0
#> ERR978231 1 0 1 1 0
#> ERR978232 1 0 1 1 0
#> ERR978233 1 0 1 1 0
#> ERR978234 1 0 1 1 0
#> ERR978235 1 0 1 1 0
#> ERR978236 1 0 1 1 0
#> ERR978237 1 0 1 1 0
#> ERR978238 1 0 1 1 0
#> ERR978239 1 0 1 1 0
#> ERR978240 1 0 1 1 0
#> ERR978241 2 0 1 0 1
#> ERR978242 2 0 1 0 1
#> ERR978243 2 0 1 0 1
#> ERR978244 2 0 1 0 1
#> ERR978245 2 0 1 0 1
#> ERR978246 2 0 1 0 1
#> ERR978247 2 0 1 0 1
#> ERR978248 2 0 1 0 1
#> ERR978249 2 0 1 0 1
#> ERR978250 2 0 1 0 1
#> ERR978251 2 0 1 0 1
#> ERR978252 2 0 1 0 1
#> ERR978253 2 0 1 0 1
#> ERR978254 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.000 1.000 0 1.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000
#> ERR978123 3 0.141 0.711 0 0.036 0.964
#> ERR978124 3 0.141 0.711 0 0.036 0.964
#> ERR978125 3 0.141 0.711 0 0.036 0.964
#> ERR978126 3 0.141 0.711 0 0.036 0.964
#> ERR978127 3 0.141 0.711 0 0.036 0.964
#> ERR978128 3 0.141 0.711 0 0.036 0.964
#> ERR978129 3 0.141 0.711 0 0.036 0.964
#> ERR978130 3 0.141 0.711 0 0.036 0.964
#> ERR978131 3 0.141 0.711 0 0.036 0.964
#> ERR978132 3 0.141 0.711 0 0.036 0.964
#> ERR978133 3 0.141 0.711 0 0.036 0.964
#> ERR978134 3 0.141 0.711 0 0.036 0.964
#> ERR978135 3 0.141 0.711 0 0.036 0.964
#> ERR978136 3 0.141 0.711 0 0.036 0.964
#> ERR978137 3 0.141 0.711 0 0.036 0.964
#> ERR978138 3 0.543 0.690 0 0.284 0.716
#> ERR978139 3 0.543 0.690 0 0.284 0.716
#> ERR978140 3 0.543 0.690 0 0.284 0.716
#> ERR978141 3 0.543 0.690 0 0.284 0.716
#> ERR978142 3 0.543 0.690 0 0.284 0.716
#> ERR978143 3 0.543 0.690 0 0.284 0.716
#> ERR978144 3 0.543 0.690 0 0.284 0.716
#> ERR978145 3 0.543 0.690 0 0.284 0.716
#> ERR978146 3 0.543 0.690 0 0.284 0.716
#> ERR978147 3 0.543 0.690 0 0.284 0.716
#> ERR978148 3 0.543 0.690 0 0.284 0.716
#> ERR978149 3 0.543 0.690 0 0.284 0.716
#> ERR978150 3 0.543 0.690 0 0.284 0.716
#> ERR978151 3 0.543 0.690 0 0.284 0.716
#> ERR978152 3 0.543 0.690 0 0.284 0.716
#> ERR978153 1 0.000 1.000 1 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000
#> ERR978169 3 0.556 0.539 0 0.300 0.700
#> ERR978170 3 0.556 0.539 0 0.300 0.700
#> ERR978171 3 0.556 0.539 0 0.300 0.700
#> ERR978172 3 0.556 0.539 0 0.300 0.700
#> ERR978173 3 0.556 0.539 0 0.300 0.700
#> ERR978174 3 0.556 0.539 0 0.300 0.700
#> ERR978175 3 0.556 0.539 0 0.300 0.700
#> ERR978176 3 0.618 0.499 0 0.416 0.584
#> ERR978177 3 0.618 0.499 0 0.416 0.584
#> ERR978178 3 0.618 0.499 0 0.416 0.584
#> ERR978179 3 0.618 0.499 0 0.416 0.584
#> ERR978180 3 0.618 0.499 0 0.416 0.584
#> ERR978181 3 0.618 0.499 0 0.416 0.584
#> ERR978182 3 0.618 0.499 0 0.416 0.584
#> ERR978183 2 0.000 1.000 0 1.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000
#> ERR978197 3 0.153 0.712 0 0.040 0.960
#> ERR978198 3 0.153 0.712 0 0.040 0.960
#> ERR978199 3 0.153 0.712 0 0.040 0.960
#> ERR978200 3 0.153 0.712 0 0.040 0.960
#> ERR978201 3 0.153 0.712 0 0.040 0.960
#> ERR978202 3 0.153 0.712 0 0.040 0.960
#> ERR978203 3 0.153 0.712 0 0.040 0.960
#> ERR978204 3 0.153 0.712 0 0.040 0.960
#> ERR978205 3 0.153 0.712 0 0.040 0.960
#> ERR978206 3 0.153 0.712 0 0.040 0.960
#> ERR978207 3 0.153 0.712 0 0.040 0.960
#> ERR978208 3 0.153 0.712 0 0.040 0.960
#> ERR978209 3 0.153 0.712 0 0.040 0.960
#> ERR978210 3 0.153 0.712 0 0.040 0.960
#> ERR978211 3 0.153 0.712 0 0.040 0.960
#> ERR978212 3 0.543 0.690 0 0.284 0.716
#> ERR978213 3 0.543 0.690 0 0.284 0.716
#> ERR978214 3 0.543 0.690 0 0.284 0.716
#> ERR978215 3 0.543 0.690 0 0.284 0.716
#> ERR978216 3 0.543 0.690 0 0.284 0.716
#> ERR978217 3 0.543 0.690 0 0.284 0.716
#> ERR978218 3 0.543 0.690 0 0.284 0.716
#> ERR978219 3 0.543 0.690 0 0.284 0.716
#> ERR978220 3 0.543 0.690 0 0.284 0.716
#> ERR978221 3 0.543 0.690 0 0.284 0.716
#> ERR978222 3 0.543 0.690 0 0.284 0.716
#> ERR978223 3 0.543 0.690 0 0.284 0.716
#> ERR978224 3 0.543 0.690 0 0.284 0.716
#> ERR978225 3 0.543 0.690 0 0.284 0.716
#> ERR978226 3 0.543 0.690 0 0.284 0.716
#> ERR978227 1 0.000 1.000 1 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000
#> ERR978241 3 0.627 0.473 0 0.452 0.548
#> ERR978242 3 0.627 0.473 0 0.452 0.548
#> ERR978243 3 0.627 0.473 0 0.452 0.548
#> ERR978244 3 0.627 0.473 0 0.452 0.548
#> ERR978245 3 0.627 0.473 0 0.452 0.548
#> ERR978246 3 0.627 0.473 0 0.452 0.548
#> ERR978247 3 0.627 0.473 0 0.452 0.548
#> ERR978248 3 0.631 0.434 0 0.488 0.512
#> ERR978249 3 0.631 0.434 0 0.488 0.512
#> ERR978250 3 0.631 0.434 0 0.488 0.512
#> ERR978251 3 0.631 0.434 0 0.488 0.512
#> ERR978252 3 0.631 0.434 0 0.488 0.512
#> ERR978253 3 0.631 0.434 0 0.488 0.512
#> ERR978254 3 0.631 0.434 0 0.488 0.512
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978123 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978124 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978125 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978126 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978127 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978128 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978129 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978130 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978131 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978132 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978133 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978134 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978135 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978136 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978137 3 0.0707 0.982 0 0.000 0.980 0.020
#> ERR978138 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978139 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978140 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978141 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978142 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978143 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978144 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978145 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978146 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978147 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978148 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978149 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978150 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978151 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978152 4 0.4961 0.545 0 0.000 0.448 0.552
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978169 4 0.3688 0.545 0 0.000 0.208 0.792
#> ERR978170 4 0.3688 0.545 0 0.000 0.208 0.792
#> ERR978171 4 0.3649 0.550 0 0.000 0.204 0.796
#> ERR978172 4 0.3649 0.550 0 0.000 0.204 0.796
#> ERR978173 4 0.3649 0.550 0 0.000 0.204 0.796
#> ERR978174 4 0.3649 0.550 0 0.000 0.204 0.796
#> ERR978175 4 0.3688 0.545 0 0.000 0.208 0.792
#> ERR978176 4 0.2921 0.598 0 0.000 0.140 0.860
#> ERR978177 4 0.2921 0.598 0 0.000 0.140 0.860
#> ERR978178 4 0.2921 0.598 0 0.000 0.140 0.860
#> ERR978179 4 0.2921 0.598 0 0.000 0.140 0.860
#> ERR978180 4 0.2921 0.598 0 0.000 0.140 0.860
#> ERR978181 4 0.2921 0.598 0 0.000 0.140 0.860
#> ERR978182 4 0.2921 0.598 0 0.000 0.140 0.860
#> ERR978183 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978184 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978185 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978186 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978187 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978188 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978189 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978190 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978191 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978192 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978193 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978194 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978195 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978196 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978197 3 0.0000 0.980 0 0.000 1.000 0.000
#> ERR978198 3 0.0000 0.980 0 0.000 1.000 0.000
#> ERR978199 3 0.0000 0.980 0 0.000 1.000 0.000
#> ERR978200 3 0.0000 0.980 0 0.000 1.000 0.000
#> ERR978201 3 0.0000 0.980 0 0.000 1.000 0.000
#> ERR978202 3 0.0000 0.980 0 0.000 1.000 0.000
#> ERR978203 3 0.0000 0.980 0 0.000 1.000 0.000
#> ERR978204 3 0.0336 0.977 0 0.000 0.992 0.008
#> ERR978205 3 0.0336 0.977 0 0.000 0.992 0.008
#> ERR978206 3 0.0336 0.977 0 0.000 0.992 0.008
#> ERR978207 3 0.0336 0.977 0 0.000 0.992 0.008
#> ERR978208 3 0.0336 0.977 0 0.000 0.992 0.008
#> ERR978209 3 0.0336 0.977 0 0.000 0.992 0.008
#> ERR978210 3 0.0336 0.977 0 0.000 0.992 0.008
#> ERR978211 3 0.0336 0.977 0 0.000 0.992 0.008
#> ERR978212 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978213 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978214 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978215 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978216 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978217 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978218 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978219 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978220 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978221 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978222 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978223 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978224 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978225 4 0.4933 0.542 0 0.000 0.432 0.568
#> ERR978226 4 0.4967 0.541 0 0.000 0.452 0.548
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978241 4 0.2216 0.614 0 0.000 0.092 0.908
#> ERR978242 4 0.2216 0.614 0 0.000 0.092 0.908
#> ERR978243 4 0.2216 0.614 0 0.000 0.092 0.908
#> ERR978244 4 0.2216 0.614 0 0.000 0.092 0.908
#> ERR978245 4 0.2216 0.614 0 0.000 0.092 0.908
#> ERR978246 4 0.2216 0.614 0 0.000 0.092 0.908
#> ERR978247 4 0.2216 0.614 0 0.000 0.092 0.908
#> ERR978248 4 0.5160 0.589 0 0.136 0.104 0.760
#> ERR978249 4 0.5160 0.589 0 0.136 0.104 0.760
#> ERR978250 4 0.5160 0.589 0 0.136 0.104 0.760
#> ERR978251 4 0.5160 0.589 0 0.136 0.104 0.760
#> ERR978252 4 0.5160 0.589 0 0.136 0.104 0.760
#> ERR978253 4 0.5160 0.589 0 0.136 0.104 0.760
#> ERR978254 4 0.5160 0.589 0 0.136 0.104 0.760
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 0.998 0.000 1.000 0.000 0.000 0.000
#> ERR978123 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978124 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978125 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978126 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978127 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978128 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978129 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978130 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978131 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978132 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978133 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978134 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978135 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978136 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978137 3 0.4801 0.656 0.372 0.000 0.604 0.020 0.004
#> ERR978138 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978139 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978140 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978141 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978142 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978143 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978144 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978145 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978146 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978147 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978148 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978149 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978150 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978151 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978152 4 0.6028 0.490 0.372 0.000 0.008 0.524 0.096
#> ERR978153 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978154 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978155 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978156 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978157 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978158 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978159 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978160 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978161 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978162 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978163 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978164 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978165 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978166 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978167 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978168 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978169 4 0.4126 0.297 0.000 0.000 0.380 0.620 0.000
#> ERR978170 4 0.4126 0.297 0.000 0.000 0.380 0.620 0.000
#> ERR978171 4 0.4126 0.297 0.000 0.000 0.380 0.620 0.000
#> ERR978172 4 0.4126 0.297 0.000 0.000 0.380 0.620 0.000
#> ERR978173 4 0.4126 0.297 0.000 0.000 0.380 0.620 0.000
#> ERR978174 4 0.4126 0.297 0.000 0.000 0.380 0.620 0.000
#> ERR978175 4 0.4126 0.297 0.000 0.000 0.380 0.620 0.000
#> ERR978176 4 0.2462 0.611 0.000 0.000 0.112 0.880 0.008
#> ERR978177 4 0.2462 0.611 0.000 0.000 0.112 0.880 0.008
#> ERR978178 4 0.2462 0.611 0.000 0.000 0.112 0.880 0.008
#> ERR978179 4 0.2462 0.611 0.000 0.000 0.112 0.880 0.008
#> ERR978180 4 0.2462 0.611 0.000 0.000 0.112 0.880 0.008
#> ERR978181 4 0.2462 0.611 0.000 0.000 0.112 0.880 0.008
#> ERR978182 4 0.2462 0.611 0.000 0.000 0.112 0.880 0.008
#> ERR978183 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978184 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978185 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978186 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978187 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978188 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978189 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978190 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978191 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978192 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978193 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978194 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978195 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978196 2 0.0162 0.998 0.000 0.996 0.000 0.000 0.004
#> ERR978197 3 0.1121 0.583 0.000 0.000 0.956 0.044 0.000
#> ERR978198 3 0.1121 0.583 0.000 0.000 0.956 0.044 0.000
#> ERR978199 3 0.1121 0.583 0.000 0.000 0.956 0.044 0.000
#> ERR978200 3 0.1121 0.583 0.000 0.000 0.956 0.044 0.000
#> ERR978201 3 0.1121 0.583 0.000 0.000 0.956 0.044 0.000
#> ERR978202 3 0.1121 0.583 0.000 0.000 0.956 0.044 0.000
#> ERR978203 3 0.1121 0.583 0.000 0.000 0.956 0.044 0.000
#> ERR978204 3 0.1341 0.579 0.000 0.000 0.944 0.056 0.000
#> ERR978205 3 0.1341 0.579 0.000 0.000 0.944 0.056 0.000
#> ERR978206 3 0.1341 0.579 0.000 0.000 0.944 0.056 0.000
#> ERR978207 3 0.1341 0.579 0.000 0.000 0.944 0.056 0.000
#> ERR978208 3 0.1341 0.579 0.000 0.000 0.944 0.056 0.000
#> ERR978209 3 0.1341 0.579 0.000 0.000 0.944 0.056 0.000
#> ERR978210 3 0.1341 0.579 0.000 0.000 0.944 0.056 0.000
#> ERR978211 3 0.1341 0.579 0.000 0.000 0.944 0.056 0.000
#> ERR978212 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978213 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978214 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978215 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978216 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978217 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978218 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978219 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978220 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978221 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978222 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978223 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978224 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978225 5 0.4464 0.988 0.000 0.000 0.408 0.008 0.584
#> ERR978226 5 0.6055 0.823 0.000 0.000 0.408 0.120 0.472
#> ERR978227 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978228 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978229 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978230 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978231 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978232 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978233 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978234 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978235 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978236 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978237 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978238 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978239 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978240 1 0.4101 1.000 0.628 0.000 0.000 0.000 0.372
#> ERR978241 4 0.2411 0.615 0.000 0.000 0.108 0.884 0.008
#> ERR978242 4 0.2411 0.615 0.000 0.000 0.108 0.884 0.008
#> ERR978243 4 0.2411 0.615 0.000 0.000 0.108 0.884 0.008
#> ERR978244 4 0.2411 0.615 0.000 0.000 0.108 0.884 0.008
#> ERR978245 4 0.2411 0.615 0.000 0.000 0.108 0.884 0.008
#> ERR978246 4 0.2411 0.615 0.000 0.000 0.108 0.884 0.008
#> ERR978247 4 0.2411 0.615 0.000 0.000 0.108 0.884 0.008
#> ERR978248 4 0.5405 0.491 0.000 0.136 0.016 0.700 0.148
#> ERR978249 4 0.5405 0.491 0.000 0.136 0.016 0.700 0.148
#> ERR978250 4 0.5405 0.491 0.000 0.136 0.016 0.700 0.148
#> ERR978251 4 0.5405 0.491 0.000 0.136 0.016 0.700 0.148
#> ERR978252 4 0.5405 0.491 0.000 0.136 0.016 0.700 0.148
#> ERR978253 4 0.5405 0.491 0.000 0.136 0.016 0.700 0.148
#> ERR978254 4 0.5405 0.491 0.000 0.136 0.016 0.700 0.148
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 0.788 0 1.000 0.000 0.000 0.000 0.000
#> ERR978123 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978124 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978125 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978126 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978127 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978128 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978129 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978130 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978131 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978132 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978133 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978134 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978135 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978136 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978137 3 0.4473 0.649 0 0.000 0.584 0.000 0.036 0.380
#> ERR978138 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978139 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978140 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978141 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978142 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978143 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978144 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978145 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978146 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978147 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978148 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978149 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978150 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978151 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978152 6 0.0865 0.746 0 0.000 0.000 0.000 0.036 0.964
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978169 4 0.5368 0.608 0 0.000 0.400 0.488 0.000 0.112
#> ERR978170 4 0.5368 0.608 0 0.000 0.400 0.488 0.000 0.112
#> ERR978171 4 0.5368 0.608 0 0.000 0.400 0.488 0.000 0.112
#> ERR978172 4 0.5368 0.608 0 0.000 0.400 0.488 0.000 0.112
#> ERR978173 4 0.5368 0.608 0 0.000 0.400 0.488 0.000 0.112
#> ERR978174 4 0.5368 0.608 0 0.000 0.400 0.488 0.000 0.112
#> ERR978175 4 0.5368 0.608 0 0.000 0.400 0.488 0.000 0.112
#> ERR978176 4 0.5576 0.792 0 0.000 0.116 0.512 0.008 0.364
#> ERR978177 4 0.5576 0.792 0 0.000 0.116 0.512 0.008 0.364
#> ERR978178 4 0.5576 0.792 0 0.000 0.116 0.512 0.008 0.364
#> ERR978179 4 0.5576 0.792 0 0.000 0.116 0.512 0.008 0.364
#> ERR978180 4 0.5576 0.792 0 0.000 0.116 0.512 0.008 0.364
#> ERR978181 4 0.5576 0.792 0 0.000 0.116 0.512 0.008 0.364
#> ERR978182 4 0.5576 0.792 0 0.000 0.116 0.512 0.008 0.364
#> ERR978183 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978184 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978185 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978186 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978187 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978188 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978189 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978190 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978191 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978192 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978193 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978194 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978195 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978196 2 0.3867 0.752 0 0.512 0.000 0.488 0.000 0.000
#> ERR978197 3 0.4919 0.578 0 0.000 0.536 0.012 0.412 0.040
#> ERR978198 3 0.4919 0.578 0 0.000 0.536 0.012 0.412 0.040
#> ERR978199 3 0.4919 0.578 0 0.000 0.536 0.012 0.412 0.040
#> ERR978200 3 0.4919 0.578 0 0.000 0.536 0.012 0.412 0.040
#> ERR978201 3 0.4919 0.578 0 0.000 0.536 0.012 0.412 0.040
#> ERR978202 3 0.4919 0.578 0 0.000 0.536 0.012 0.412 0.040
#> ERR978203 3 0.4919 0.578 0 0.000 0.536 0.012 0.412 0.040
#> ERR978204 3 0.5499 0.563 0 0.000 0.500 0.036 0.412 0.052
#> ERR978205 3 0.5499 0.563 0 0.000 0.500 0.036 0.412 0.052
#> ERR978206 3 0.5499 0.563 0 0.000 0.500 0.036 0.412 0.052
#> ERR978207 3 0.5499 0.563 0 0.000 0.500 0.036 0.412 0.052
#> ERR978208 3 0.5499 0.563 0 0.000 0.500 0.036 0.412 0.052
#> ERR978209 3 0.5499 0.563 0 0.000 0.500 0.036 0.412 0.052
#> ERR978210 3 0.5499 0.563 0 0.000 0.500 0.036 0.412 0.052
#> ERR978211 3 0.5499 0.563 0 0.000 0.500 0.036 0.412 0.052
#> ERR978212 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978213 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978214 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978215 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978216 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978217 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978218 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978219 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978220 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978221 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978222 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978223 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978224 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978225 5 0.0146 0.991 0 0.000 0.000 0.000 0.996 0.004
#> ERR978226 5 0.1714 0.863 0 0.000 0.000 0.000 0.908 0.092
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978241 4 0.5334 0.790 0 0.000 0.112 0.512 0.000 0.376
#> ERR978242 4 0.5334 0.790 0 0.000 0.112 0.512 0.000 0.376
#> ERR978243 4 0.5334 0.790 0 0.000 0.112 0.512 0.000 0.376
#> ERR978244 4 0.5334 0.790 0 0.000 0.112 0.512 0.000 0.376
#> ERR978245 4 0.5334 0.790 0 0.000 0.112 0.512 0.000 0.376
#> ERR978246 4 0.5334 0.790 0 0.000 0.112 0.512 0.000 0.376
#> ERR978247 4 0.5334 0.790 0 0.000 0.112 0.512 0.000 0.376
#> ERR978248 6 0.5585 0.456 0 0.000 0.416 0.000 0.140 0.444
#> ERR978249 6 0.5585 0.456 0 0.000 0.416 0.000 0.140 0.444
#> ERR978250 6 0.5585 0.456 0 0.000 0.416 0.000 0.140 0.444
#> ERR978251 6 0.5585 0.456 0 0.000 0.416 0.000 0.140 0.444
#> ERR978252 6 0.5585 0.456 0 0.000 0.416 0.000 0.140 0.444
#> ERR978253 6 0.5585 0.456 0 0.000 0.416 0.000 0.140 0.444
#> ERR978254 6 0.5585 0.456 0 0.000 0.416 0.000 0.140 0.444
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 14049 rows and 148 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.3261 0.675 0.675
#> 3 3 1.000 0.962 0.984 0.7780 0.757 0.640
#> 4 4 0.769 0.833 0.873 0.2560 0.846 0.642
#> 5 5 1.000 1.000 1.000 0.1061 0.917 0.702
#> 6 6 0.964 0.942 0.947 0.0199 0.986 0.931
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 5
There is also optional best \(k\) = 2 3 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
#> ERR978109 2 0 1 0 1
#> ERR978110 2 0 1 0 1
#> ERR978111 2 0 1 0 1
#> ERR978112 2 0 1 0 1
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cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.000 1.000 0 1.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000
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#> ERR978120 2 0.000 1.000 0 1.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000
#> ERR978123 3 0.000 0.972 0 0.000 1.000
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#> ERR978193 2 0.000 1.000 0 1.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000
#> ERR978197 3 0.000 0.972 0 0.000 1.000
#> ERR978198 3 0.000 0.972 0 0.000 1.000
#> ERR978199 3 0.000 0.972 0 0.000 1.000
#> ERR978200 3 0.000 0.972 0 0.000 1.000
#> ERR978201 3 0.000 0.972 0 0.000 1.000
#> ERR978202 3 0.000 0.972 0 0.000 1.000
#> ERR978203 3 0.000 0.972 0 0.000 1.000
#> ERR978204 3 0.000 0.972 0 0.000 1.000
#> ERR978205 3 0.000 0.972 0 0.000 1.000
#> ERR978206 3 0.000 0.972 0 0.000 1.000
#> ERR978207 3 0.000 0.972 0 0.000 1.000
#> ERR978208 3 0.000 0.972 0 0.000 1.000
#> ERR978209 3 0.000 0.972 0 0.000 1.000
#> ERR978210 3 0.000 0.972 0 0.000 1.000
#> ERR978211 3 0.000 0.972 0 0.000 1.000
#> ERR978212 3 0.000 0.972 0 0.000 1.000
#> ERR978213 3 0.000 0.972 0 0.000 1.000
#> ERR978214 3 0.000 0.972 0 0.000 1.000
#> ERR978215 3 0.000 0.972 0 0.000 1.000
#> ERR978216 3 0.000 0.972 0 0.000 1.000
#> ERR978217 3 0.000 0.972 0 0.000 1.000
#> ERR978218 3 0.000 0.972 0 0.000 1.000
#> ERR978219 3 0.000 0.972 0 0.000 1.000
#> ERR978220 3 0.000 0.972 0 0.000 1.000
#> ERR978221 3 0.000 0.972 0 0.000 1.000
#> ERR978222 3 0.000 0.972 0 0.000 1.000
#> ERR978223 3 0.000 0.972 0 0.000 1.000
#> ERR978224 3 0.000 0.972 0 0.000 1.000
#> ERR978225 3 0.000 0.972 0 0.000 1.000
#> ERR978226 3 0.000 0.972 0 0.000 1.000
#> ERR978227 1 0.000 1.000 1 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000
#> ERR978241 3 0.000 0.972 0 0.000 1.000
#> ERR978242 3 0.000 0.972 0 0.000 1.000
#> ERR978243 3 0.000 0.972 0 0.000 1.000
#> ERR978244 3 0.000 0.972 0 0.000 1.000
#> ERR978245 3 0.000 0.972 0 0.000 1.000
#> ERR978246 3 0.000 0.972 0 0.000 1.000
#> ERR978247 3 0.000 0.972 0 0.000 1.000
#> ERR978248 3 0.617 0.348 0 0.412 0.588
#> ERR978249 3 0.579 0.534 0 0.332 0.668
#> ERR978250 3 0.579 0.534 0 0.332 0.668
#> ERR978251 3 0.543 0.621 0 0.284 0.716
#> ERR978252 3 0.562 0.580 0 0.308 0.692
#> ERR978253 3 0.581 0.526 0 0.336 0.664
#> ERR978254 3 0.603 0.439 0 0.376 0.624
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978123 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978124 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978125 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978126 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978127 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978128 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978129 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978130 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978131 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978132 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978133 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978134 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978135 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978136 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978137 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978138 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978139 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978140 3 0.5000 0.606 0 0.000 0.504 0.496
#> ERR978141 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978142 3 0.5000 0.606 0 0.000 0.504 0.496
#> ERR978143 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978144 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978145 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978146 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978147 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978148 3 0.5000 0.606 0 0.000 0.504 0.496
#> ERR978149 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978150 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978151 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978152 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978169 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978170 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978171 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978172 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978173 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978174 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978175 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978176 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978177 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978178 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978179 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978180 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978181 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978182 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978183 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978184 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978185 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978186 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978187 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978188 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978189 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978190 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978191 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978192 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978193 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978194 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978195 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978196 2 0.0000 1.000 0 1.000 0.000 0.000
#> ERR978197 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978198 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978199 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978200 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978201 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978202 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978203 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978204 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978205 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978206 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978207 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978208 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978209 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978210 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978211 3 0.0000 0.677 0 0.000 1.000 0.000
#> ERR978212 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978213 3 0.5000 0.606 0 0.000 0.504 0.496
#> ERR978214 3 0.5000 0.606 0 0.000 0.504 0.496
#> ERR978215 3 0.5000 0.606 0 0.000 0.504 0.496
#> ERR978216 3 0.5000 0.606 0 0.000 0.504 0.496
#> ERR978217 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978218 3 0.5000 0.606 0 0.000 0.504 0.496
#> ERR978219 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978220 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978221 3 0.5000 0.606 0 0.000 0.504 0.496
#> ERR978222 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978223 3 0.5000 0.606 0 0.000 0.504 0.496
#> ERR978224 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978225 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978226 3 0.4999 0.610 0 0.000 0.508 0.492
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978241 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978242 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978243 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978244 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978245 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978246 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978247 4 0.3569 0.925 0 0.000 0.196 0.804
#> ERR978248 4 0.1661 0.749 0 0.052 0.004 0.944
#> ERR978249 4 0.1209 0.766 0 0.032 0.004 0.964
#> ERR978250 4 0.1488 0.775 0 0.032 0.012 0.956
#> ERR978251 4 0.1042 0.765 0 0.020 0.008 0.972
#> ERR978252 4 0.1820 0.784 0 0.036 0.020 0.944
#> ERR978253 4 0.0927 0.763 0 0.016 0.008 0.976
#> ERR978254 4 0.1452 0.766 0 0.036 0.008 0.956
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978108 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978109 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978110 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978111 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978112 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978113 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978114 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978115 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978116 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978117 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978118 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978119 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978120 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978121 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978122 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978123 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978124 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978125 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978126 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978127 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978128 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978129 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978130 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978131 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978132 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978133 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978134 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978135 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978136 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978137 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978138 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978139 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978140 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978141 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978142 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978143 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978144 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978145 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978146 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978147 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978148 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978149 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978150 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978151 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978152 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978153 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978154 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978155 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978156 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978157 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978158 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978159 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978160 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978161 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978162 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978163 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978164 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978165 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978166 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978167 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978168 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978169 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978170 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978171 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978172 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978173 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978174 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978175 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978176 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978177 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978178 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978179 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978180 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978181 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978182 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978183 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978184 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978185 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978186 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978187 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978188 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978189 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978190 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978191 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978192 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978193 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978194 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978195 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978196 2 0.0000 1.000 0 1.000 0 0.000 0
#> ERR978197 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978198 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978199 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978200 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978201 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978202 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978203 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978204 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978205 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978206 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978207 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978208 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978209 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978210 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978211 3 0.0000 1.000 0 0.000 1 0.000 0
#> ERR978212 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978213 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978214 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978215 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978216 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978217 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978218 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978219 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978220 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978221 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978222 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978223 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978224 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978225 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978226 5 0.0000 1.000 0 0.000 0 0.000 1
#> ERR978227 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978228 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978229 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978230 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978231 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978232 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978233 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978234 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978235 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978236 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978237 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978238 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978239 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978240 1 0.0000 1.000 1 0.000 0 0.000 0
#> ERR978241 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978242 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978243 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978244 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978245 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978246 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978247 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978248 4 0.0162 0.996 0 0.004 0 0.996 0
#> ERR978249 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978250 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978251 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978252 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978253 4 0.0000 1.000 0 0.000 0 1.000 0
#> ERR978254 4 0.0162 0.996 0 0.004 0 0.996 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978123 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978124 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978125 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978126 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978127 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978128 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978129 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978130 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978131 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978132 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978133 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978134 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978135 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978136 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978137 3 0.0146 0.998 0 0 0.996 0.000 0.000 0.004
#> ERR978138 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978139 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978140 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978141 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978142 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978143 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978144 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978145 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978146 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978147 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978148 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978149 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978150 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978151 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978152 5 0.1765 0.916 0 0 0.000 0.000 0.904 0.096
#> ERR978153 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978170 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978171 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978172 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978173 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978174 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978175 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978176 4 0.3428 0.555 0 0 0.000 0.696 0.000 0.304
#> ERR978177 4 0.3428 0.555 0 0 0.000 0.696 0.000 0.304
#> ERR978178 4 0.3428 0.555 0 0 0.000 0.696 0.000 0.304
#> ERR978179 4 0.3428 0.555 0 0 0.000 0.696 0.000 0.304
#> ERR978180 4 0.3428 0.555 0 0 0.000 0.696 0.000 0.304
#> ERR978181 4 0.3428 0.555 0 0 0.000 0.696 0.000 0.304
#> ERR978182 4 0.3428 0.555 0 0 0.000 0.696 0.000 0.304
#> ERR978183 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978184 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978185 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978186 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978187 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978188 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978189 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978190 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978191 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978192 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978193 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978194 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978195 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978196 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978197 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978198 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978199 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978200 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978201 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978202 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978203 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978204 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978205 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978206 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978207 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978208 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978209 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978210 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978211 3 0.0000 0.998 0 0 1.000 0.000 0.000 0.000
#> ERR978212 5 0.2454 0.845 0 0 0.000 0.000 0.840 0.160
#> ERR978213 5 0.2219 0.863 0 0 0.000 0.000 0.864 0.136
#> ERR978214 5 0.1663 0.889 0 0 0.000 0.000 0.912 0.088
#> ERR978215 5 0.1501 0.894 0 0 0.000 0.000 0.924 0.076
#> ERR978216 5 0.2048 0.873 0 0 0.000 0.000 0.880 0.120
#> ERR978217 5 0.2340 0.854 0 0 0.000 0.000 0.852 0.148
#> ERR978218 5 0.2854 0.800 0 0 0.000 0.000 0.792 0.208
#> ERR978219 5 0.1556 0.890 0 0 0.000 0.000 0.920 0.080
#> ERR978220 5 0.1007 0.902 0 0 0.000 0.000 0.956 0.044
#> ERR978221 5 0.0713 0.906 0 0 0.000 0.000 0.972 0.028
#> ERR978222 5 0.0713 0.906 0 0 0.000 0.000 0.972 0.028
#> ERR978223 5 0.0790 0.905 0 0 0.000 0.000 0.968 0.032
#> ERR978224 5 0.0865 0.904 0 0 0.000 0.000 0.964 0.036
#> ERR978225 5 0.1610 0.889 0 0 0.000 0.000 0.916 0.084
#> ERR978226 5 0.2562 0.822 0 0 0.000 0.000 0.828 0.172
#> ERR978227 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978241 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978242 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978243 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978244 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978245 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978246 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978247 4 0.0000 0.844 0 0 0.000 1.000 0.000 0.000
#> ERR978248 6 0.2854 0.979 0 0 0.000 0.208 0.000 0.792
#> ERR978249 6 0.2941 0.988 0 0 0.000 0.220 0.000 0.780
#> ERR978250 6 0.2969 0.986 0 0 0.000 0.224 0.000 0.776
#> ERR978251 6 0.3023 0.976 0 0 0.000 0.232 0.000 0.768
#> ERR978252 6 0.2969 0.986 0 0 0.000 0.224 0.000 0.776
#> ERR978253 6 0.2912 0.987 0 0 0.000 0.216 0.000 0.784
#> ERR978254 6 0.2883 0.984 0 0 0.000 0.212 0.000 0.788
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 14049 rows and 148 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.259 0.815 0.791 0.3890 0.515 0.515
#> 3 3 0.419 0.887 0.836 0.5479 0.840 0.689
#> 4 4 0.765 0.853 0.864 0.1776 0.943 0.840
#> 5 5 0.841 0.888 0.903 0.1103 0.917 0.722
#> 6 6 0.899 0.904 0.926 0.0438 0.961 0.821
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0.7299 0.991 0.204 0.796
#> ERR978108 2 0.7299 0.991 0.204 0.796
#> ERR978109 2 0.7299 0.991 0.204 0.796
#> ERR978110 2 0.7299 0.991 0.204 0.796
#> ERR978111 2 0.7299 0.991 0.204 0.796
#> ERR978112 2 0.7299 0.991 0.204 0.796
#> ERR978113 2 0.7299 0.991 0.204 0.796
#> ERR978114 2 0.7299 0.991 0.204 0.796
#> ERR978115 2 0.7299 0.991 0.204 0.796
#> ERR978116 2 0.7299 0.991 0.204 0.796
#> ERR978117 2 0.7299 0.991 0.204 0.796
#> ERR978118 2 0.7299 0.991 0.204 0.796
#> ERR978119 2 0.7299 0.991 0.204 0.796
#> ERR978120 2 0.7299 0.991 0.204 0.796
#> ERR978121 2 0.7299 0.991 0.204 0.796
#> ERR978122 2 0.7299 0.991 0.204 0.796
#> ERR978123 1 0.8555 0.709 0.720 0.280
#> ERR978124 1 0.8555 0.709 0.720 0.280
#> ERR978125 1 0.8555 0.709 0.720 0.280
#> ERR978126 1 0.8555 0.709 0.720 0.280
#> ERR978127 1 0.8555 0.709 0.720 0.280
#> ERR978128 1 0.8555 0.709 0.720 0.280
#> ERR978129 1 0.8555 0.709 0.720 0.280
#> ERR978130 1 0.8555 0.709 0.720 0.280
#> ERR978131 1 0.8555 0.709 0.720 0.280
#> ERR978132 1 0.8555 0.709 0.720 0.280
#> ERR978133 1 0.8555 0.709 0.720 0.280
#> ERR978134 1 0.8555 0.709 0.720 0.280
#> ERR978135 1 0.8555 0.709 0.720 0.280
#> ERR978136 1 0.8555 0.709 0.720 0.280
#> ERR978137 1 0.8555 0.709 0.720 0.280
#> ERR978138 1 0.8555 0.709 0.720 0.280
#> ERR978139 1 0.8555 0.709 0.720 0.280
#> ERR978140 1 0.8555 0.709 0.720 0.280
#> ERR978141 1 0.8555 0.709 0.720 0.280
#> ERR978142 1 0.8555 0.709 0.720 0.280
#> ERR978143 1 0.8555 0.709 0.720 0.280
#> ERR978144 1 0.8555 0.709 0.720 0.280
#> ERR978145 1 0.8555 0.709 0.720 0.280
#> ERR978146 1 0.8555 0.709 0.720 0.280
#> ERR978147 1 0.8555 0.709 0.720 0.280
#> ERR978148 1 0.8555 0.709 0.720 0.280
#> ERR978149 1 0.8555 0.709 0.720 0.280
#> ERR978150 1 0.8555 0.709 0.720 0.280
#> ERR978151 1 0.8555 0.709 0.720 0.280
#> ERR978152 1 0.8555 0.709 0.720 0.280
#> ERR978153 1 0.0672 0.732 0.992 0.008
#> ERR978154 1 0.0672 0.732 0.992 0.008
#> ERR978155 1 0.0672 0.732 0.992 0.008
#> ERR978156 1 0.0672 0.732 0.992 0.008
#> ERR978157 1 0.0672 0.732 0.992 0.008
#> ERR978158 1 0.0672 0.732 0.992 0.008
#> ERR978159 1 0.0672 0.732 0.992 0.008
#> ERR978160 1 0.0672 0.732 0.992 0.008
#> ERR978161 1 0.0672 0.732 0.992 0.008
#> ERR978162 1 0.0672 0.732 0.992 0.008
#> ERR978163 1 0.0672 0.732 0.992 0.008
#> ERR978164 1 0.0672 0.732 0.992 0.008
#> ERR978165 1 0.0672 0.732 0.992 0.008
#> ERR978166 1 0.0672 0.732 0.992 0.008
#> ERR978167 1 0.0672 0.732 0.992 0.008
#> ERR978168 1 0.0672 0.732 0.992 0.008
#> ERR978169 1 0.9608 0.652 0.616 0.384
#> ERR978170 1 0.9608 0.652 0.616 0.384
#> ERR978171 1 0.9608 0.652 0.616 0.384
#> ERR978172 1 0.9608 0.652 0.616 0.384
#> ERR978173 1 0.9608 0.652 0.616 0.384
#> ERR978174 1 0.9608 0.652 0.616 0.384
#> ERR978175 1 0.9608 0.652 0.616 0.384
#> ERR978176 1 0.9608 0.652 0.616 0.384
#> ERR978177 1 0.9608 0.652 0.616 0.384
#> ERR978178 1 0.9608 0.652 0.616 0.384
#> ERR978179 1 0.9608 0.652 0.616 0.384
#> ERR978180 1 0.9608 0.652 0.616 0.384
#> ERR978181 1 0.9608 0.652 0.616 0.384
#> ERR978182 1 0.9608 0.652 0.616 0.384
#> ERR978183 2 0.7299 0.991 0.204 0.796
#> ERR978184 2 0.7299 0.991 0.204 0.796
#> ERR978185 2 0.7299 0.991 0.204 0.796
#> ERR978186 2 0.7299 0.991 0.204 0.796
#> ERR978187 2 0.7299 0.991 0.204 0.796
#> ERR978188 2 0.7299 0.991 0.204 0.796
#> ERR978189 2 0.7299 0.991 0.204 0.796
#> ERR978190 2 0.7299 0.991 0.204 0.796
#> ERR978191 2 0.7299 0.991 0.204 0.796
#> ERR978192 2 0.7299 0.991 0.204 0.796
#> ERR978193 2 0.7299 0.991 0.204 0.796
#> ERR978194 2 0.7299 0.991 0.204 0.796
#> ERR978195 2 0.7299 0.991 0.204 0.796
#> ERR978196 2 0.7299 0.991 0.204 0.796
#> ERR978197 2 0.7674 0.972 0.224 0.776
#> ERR978198 2 0.7674 0.972 0.224 0.776
#> ERR978199 2 0.7674 0.972 0.224 0.776
#> ERR978200 2 0.7674 0.972 0.224 0.776
#> ERR978201 2 0.7674 0.972 0.224 0.776
#> ERR978202 2 0.7674 0.972 0.224 0.776
#> ERR978203 2 0.7674 0.972 0.224 0.776
#> ERR978204 2 0.7674 0.972 0.224 0.776
#> ERR978205 2 0.7674 0.972 0.224 0.776
#> ERR978206 2 0.7674 0.972 0.224 0.776
#> ERR978207 2 0.7674 0.972 0.224 0.776
#> ERR978208 2 0.7674 0.972 0.224 0.776
#> ERR978209 2 0.7674 0.972 0.224 0.776
#> ERR978210 2 0.7674 0.972 0.224 0.776
#> ERR978211 2 0.7674 0.972 0.224 0.776
#> ERR978212 2 0.7299 0.991 0.204 0.796
#> ERR978213 2 0.7299 0.991 0.204 0.796
#> ERR978214 2 0.7299 0.991 0.204 0.796
#> ERR978215 2 0.7299 0.991 0.204 0.796
#> ERR978216 2 0.7299 0.991 0.204 0.796
#> ERR978217 2 0.7299 0.991 0.204 0.796
#> ERR978218 2 0.7299 0.991 0.204 0.796
#> ERR978219 2 0.7299 0.991 0.204 0.796
#> ERR978220 2 0.7299 0.991 0.204 0.796
#> ERR978221 2 0.7299 0.991 0.204 0.796
#> ERR978222 2 0.7299 0.991 0.204 0.796
#> ERR978223 2 0.7299 0.991 0.204 0.796
#> ERR978224 2 0.7299 0.991 0.204 0.796
#> ERR978225 2 0.7299 0.991 0.204 0.796
#> ERR978226 2 0.7299 0.991 0.204 0.796
#> ERR978227 1 0.0672 0.732 0.992 0.008
#> ERR978228 1 0.0672 0.732 0.992 0.008
#> ERR978229 1 0.0672 0.732 0.992 0.008
#> ERR978230 1 0.0672 0.732 0.992 0.008
#> ERR978231 1 0.0672 0.732 0.992 0.008
#> ERR978232 1 0.0672 0.732 0.992 0.008
#> ERR978233 1 0.0672 0.732 0.992 0.008
#> ERR978234 1 0.0672 0.732 0.992 0.008
#> ERR978235 1 0.0672 0.732 0.992 0.008
#> ERR978236 1 0.0672 0.732 0.992 0.008
#> ERR978237 1 0.0672 0.732 0.992 0.008
#> ERR978238 1 0.0672 0.732 0.992 0.008
#> ERR978239 1 0.0672 0.732 0.992 0.008
#> ERR978240 1 0.0672 0.732 0.992 0.008
#> ERR978241 1 0.8955 0.650 0.688 0.312
#> ERR978242 1 0.8955 0.650 0.688 0.312
#> ERR978243 1 0.8955 0.650 0.688 0.312
#> ERR978244 1 0.8955 0.650 0.688 0.312
#> ERR978245 1 0.8955 0.650 0.688 0.312
#> ERR978246 1 0.8955 0.650 0.688 0.312
#> ERR978247 1 0.8955 0.650 0.688 0.312
#> ERR978248 1 0.8955 0.650 0.688 0.312
#> ERR978249 1 0.8955 0.650 0.688 0.312
#> ERR978250 1 0.8955 0.650 0.688 0.312
#> ERR978251 1 0.8955 0.650 0.688 0.312
#> ERR978252 1 0.8955 0.650 0.688 0.312
#> ERR978253 1 0.8955 0.650 0.688 0.312
#> ERR978254 1 0.8955 0.650 0.688 0.312
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.000 0.913 0.000 1.000 0.000
#> ERR978108 2 0.000 0.913 0.000 1.000 0.000
#> ERR978109 2 0.000 0.913 0.000 1.000 0.000
#> ERR978110 2 0.000 0.913 0.000 1.000 0.000
#> ERR978111 2 0.000 0.913 0.000 1.000 0.000
#> ERR978112 2 0.000 0.913 0.000 1.000 0.000
#> ERR978113 2 0.000 0.913 0.000 1.000 0.000
#> ERR978114 2 0.000 0.913 0.000 1.000 0.000
#> ERR978115 2 0.000 0.913 0.000 1.000 0.000
#> ERR978116 2 0.000 0.913 0.000 1.000 0.000
#> ERR978117 2 0.000 0.913 0.000 1.000 0.000
#> ERR978118 2 0.000 0.913 0.000 1.000 0.000
#> ERR978119 2 0.000 0.913 0.000 1.000 0.000
#> ERR978120 2 0.000 0.913 0.000 1.000 0.000
#> ERR978121 2 0.000 0.913 0.000 1.000 0.000
#> ERR978122 2 0.000 0.913 0.000 1.000 0.000
#> ERR978123 3 0.811 0.863 0.212 0.144 0.644
#> ERR978124 3 0.811 0.863 0.212 0.144 0.644
#> ERR978125 3 0.811 0.863 0.212 0.144 0.644
#> ERR978126 3 0.811 0.863 0.212 0.144 0.644
#> ERR978127 3 0.811 0.863 0.212 0.144 0.644
#> ERR978128 3 0.811 0.863 0.212 0.144 0.644
#> ERR978129 3 0.811 0.863 0.212 0.144 0.644
#> ERR978130 3 0.811 0.863 0.212 0.144 0.644
#> ERR978131 3 0.811 0.863 0.212 0.144 0.644
#> ERR978132 3 0.811 0.863 0.212 0.144 0.644
#> ERR978133 3 0.811 0.863 0.212 0.144 0.644
#> ERR978134 3 0.811 0.863 0.212 0.144 0.644
#> ERR978135 3 0.811 0.863 0.212 0.144 0.644
#> ERR978136 3 0.811 0.863 0.212 0.144 0.644
#> ERR978137 3 0.811 0.863 0.212 0.144 0.644
#> ERR978138 3 0.817 0.862 0.212 0.148 0.640
#> ERR978139 3 0.817 0.862 0.212 0.148 0.640
#> ERR978140 3 0.817 0.862 0.212 0.148 0.640
#> ERR978141 3 0.817 0.862 0.212 0.148 0.640
#> ERR978142 3 0.817 0.862 0.212 0.148 0.640
#> ERR978143 3 0.817 0.862 0.212 0.148 0.640
#> ERR978144 3 0.817 0.862 0.212 0.148 0.640
#> ERR978145 3 0.817 0.862 0.212 0.148 0.640
#> ERR978146 3 0.817 0.862 0.212 0.148 0.640
#> ERR978147 3 0.817 0.862 0.212 0.148 0.640
#> ERR978148 3 0.817 0.862 0.212 0.148 0.640
#> ERR978149 3 0.817 0.862 0.212 0.148 0.640
#> ERR978150 3 0.817 0.862 0.212 0.148 0.640
#> ERR978151 3 0.817 0.862 0.212 0.148 0.640
#> ERR978152 3 0.817 0.862 0.212 0.148 0.640
#> ERR978153 1 0.000 1.000 1.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1.000 0.000 0.000
#> ERR978169 3 0.000 0.696 0.000 0.000 1.000
#> ERR978170 3 0.000 0.696 0.000 0.000 1.000
#> ERR978171 3 0.000 0.696 0.000 0.000 1.000
#> ERR978172 3 0.000 0.696 0.000 0.000 1.000
#> ERR978173 3 0.000 0.696 0.000 0.000 1.000
#> ERR978174 3 0.000 0.696 0.000 0.000 1.000
#> ERR978175 3 0.000 0.696 0.000 0.000 1.000
#> ERR978176 3 0.000 0.696 0.000 0.000 1.000
#> ERR978177 3 0.000 0.696 0.000 0.000 1.000
#> ERR978178 3 0.000 0.696 0.000 0.000 1.000
#> ERR978179 3 0.000 0.696 0.000 0.000 1.000
#> ERR978180 3 0.000 0.696 0.000 0.000 1.000
#> ERR978181 3 0.000 0.696 0.000 0.000 1.000
#> ERR978182 3 0.000 0.696 0.000 0.000 1.000
#> ERR978183 2 0.000 0.913 0.000 1.000 0.000
#> ERR978184 2 0.000 0.913 0.000 1.000 0.000
#> ERR978185 2 0.000 0.913 0.000 1.000 0.000
#> ERR978186 2 0.000 0.913 0.000 1.000 0.000
#> ERR978187 2 0.000 0.913 0.000 1.000 0.000
#> ERR978188 2 0.000 0.913 0.000 1.000 0.000
#> ERR978189 2 0.000 0.913 0.000 1.000 0.000
#> ERR978190 2 0.000 0.913 0.000 1.000 0.000
#> ERR978191 2 0.000 0.913 0.000 1.000 0.000
#> ERR978192 2 0.000 0.913 0.000 1.000 0.000
#> ERR978193 2 0.000 0.913 0.000 1.000 0.000
#> ERR978194 2 0.000 0.913 0.000 1.000 0.000
#> ERR978195 2 0.000 0.913 0.000 1.000 0.000
#> ERR978196 2 0.000 0.913 0.000 1.000 0.000
#> ERR978197 2 0.388 0.879 0.000 0.848 0.152
#> ERR978198 2 0.388 0.879 0.000 0.848 0.152
#> ERR978199 2 0.388 0.879 0.000 0.848 0.152
#> ERR978200 2 0.388 0.879 0.000 0.848 0.152
#> ERR978201 2 0.388 0.879 0.000 0.848 0.152
#> ERR978202 2 0.388 0.879 0.000 0.848 0.152
#> ERR978203 2 0.388 0.879 0.000 0.848 0.152
#> ERR978204 2 0.388 0.879 0.000 0.848 0.152
#> ERR978205 2 0.388 0.879 0.000 0.848 0.152
#> ERR978206 2 0.388 0.879 0.000 0.848 0.152
#> ERR978207 2 0.388 0.879 0.000 0.848 0.152
#> ERR978208 2 0.388 0.879 0.000 0.848 0.152
#> ERR978209 2 0.388 0.879 0.000 0.848 0.152
#> ERR978210 2 0.388 0.879 0.000 0.848 0.152
#> ERR978211 2 0.388 0.879 0.000 0.848 0.152
#> ERR978212 2 0.327 0.903 0.000 0.884 0.116
#> ERR978213 2 0.327 0.903 0.000 0.884 0.116
#> ERR978214 2 0.327 0.903 0.000 0.884 0.116
#> ERR978215 2 0.327 0.903 0.000 0.884 0.116
#> ERR978216 2 0.327 0.903 0.000 0.884 0.116
#> ERR978217 2 0.327 0.903 0.000 0.884 0.116
#> ERR978218 2 0.327 0.903 0.000 0.884 0.116
#> ERR978219 2 0.327 0.903 0.000 0.884 0.116
#> ERR978220 2 0.327 0.903 0.000 0.884 0.116
#> ERR978221 2 0.327 0.903 0.000 0.884 0.116
#> ERR978222 2 0.327 0.903 0.000 0.884 0.116
#> ERR978223 2 0.327 0.903 0.000 0.884 0.116
#> ERR978224 2 0.327 0.903 0.000 0.884 0.116
#> ERR978225 2 0.327 0.903 0.000 0.884 0.116
#> ERR978226 2 0.327 0.903 0.000 0.884 0.116
#> ERR978227 1 0.000 1.000 1.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1.000 0.000 0.000
#> ERR978241 3 0.800 0.829 0.212 0.136 0.652
#> ERR978242 3 0.800 0.829 0.212 0.136 0.652
#> ERR978243 3 0.800 0.829 0.212 0.136 0.652
#> ERR978244 3 0.800 0.829 0.212 0.136 0.652
#> ERR978245 3 0.800 0.829 0.212 0.136 0.652
#> ERR978246 3 0.800 0.829 0.212 0.136 0.652
#> ERR978247 3 0.800 0.829 0.212 0.136 0.652
#> ERR978248 3 0.800 0.829 0.212 0.136 0.652
#> ERR978249 3 0.800 0.829 0.212 0.136 0.652
#> ERR978250 3 0.800 0.829 0.212 0.136 0.652
#> ERR978251 3 0.800 0.829 0.212 0.136 0.652
#> ERR978252 3 0.800 0.829 0.212 0.136 0.652
#> ERR978253 3 0.800 0.829 0.212 0.136 0.652
#> ERR978254 3 0.800 0.829 0.212 0.136 0.652
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978108 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978109 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978110 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978111 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978112 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978113 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978114 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978115 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978116 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978117 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978118 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978119 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978120 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978121 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978122 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978123 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978124 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978125 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978126 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978127 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978128 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978129 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978130 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978131 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978132 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978133 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978134 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978135 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978136 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978137 3 0.0000 0.876 0 0.000 1.000 0.000
#> ERR978138 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978139 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978140 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978141 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978142 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978143 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978144 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978145 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978146 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978147 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978148 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978149 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978150 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978151 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978152 3 0.0188 0.876 0 0.004 0.996 0.000
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978169 4 0.1867 0.997 0 0.000 0.072 0.928
#> ERR978170 4 0.1867 0.997 0 0.000 0.072 0.928
#> ERR978171 4 0.1867 0.997 0 0.000 0.072 0.928
#> ERR978172 4 0.1867 0.997 0 0.000 0.072 0.928
#> ERR978173 4 0.1867 0.997 0 0.000 0.072 0.928
#> ERR978174 4 0.1867 0.997 0 0.000 0.072 0.928
#> ERR978175 4 0.1867 0.997 0 0.000 0.072 0.928
#> ERR978176 4 0.1940 0.997 0 0.000 0.076 0.924
#> ERR978177 4 0.1940 0.997 0 0.000 0.076 0.924
#> ERR978178 4 0.1940 0.997 0 0.000 0.076 0.924
#> ERR978179 4 0.1940 0.997 0 0.000 0.076 0.924
#> ERR978180 4 0.1940 0.997 0 0.000 0.076 0.924
#> ERR978181 4 0.1940 0.997 0 0.000 0.076 0.924
#> ERR978182 4 0.1940 0.997 0 0.000 0.076 0.924
#> ERR978183 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978184 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978185 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978186 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978187 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978188 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978189 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978190 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978191 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978192 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978193 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978194 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978195 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978196 2 0.0000 0.793 0 1.000 0.000 0.000
#> ERR978197 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978198 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978199 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978200 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978201 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978202 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978203 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978204 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978205 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978206 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978207 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978208 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978209 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978210 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978211 2 0.6058 0.750 0 0.632 0.296 0.072
#> ERR978212 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978213 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978214 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978215 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978216 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978217 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978218 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978219 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978220 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978221 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978222 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978223 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978224 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978225 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978226 2 0.5837 0.772 0 0.668 0.260 0.072
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978241 3 0.4605 0.665 0 0.000 0.664 0.336
#> ERR978242 3 0.4605 0.665 0 0.000 0.664 0.336
#> ERR978243 3 0.4605 0.665 0 0.000 0.664 0.336
#> ERR978244 3 0.4605 0.665 0 0.000 0.664 0.336
#> ERR978245 3 0.4605 0.665 0 0.000 0.664 0.336
#> ERR978246 3 0.4605 0.665 0 0.000 0.664 0.336
#> ERR978247 3 0.4605 0.665 0 0.000 0.664 0.336
#> ERR978248 3 0.4585 0.668 0 0.000 0.668 0.332
#> ERR978249 3 0.4585 0.668 0 0.000 0.668 0.332
#> ERR978250 3 0.4585 0.668 0 0.000 0.668 0.332
#> ERR978251 3 0.4585 0.668 0 0.000 0.668 0.332
#> ERR978252 3 0.4585 0.668 0 0.000 0.668 0.332
#> ERR978253 3 0.4585 0.668 0 0.000 0.668 0.332
#> ERR978254 3 0.4585 0.668 0 0.000 0.668 0.332
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978123 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978124 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978125 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978126 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978127 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978128 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978129 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978130 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978131 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978132 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978133 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978134 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978135 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978136 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978137 3 0.000 0.783 0 0.000 1.000 0.000 0.000
#> ERR978138 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978139 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978140 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978141 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978142 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978143 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978144 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978145 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978146 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978147 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978148 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978149 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978150 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978151 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978152 3 0.300 0.789 0 0.000 0.812 0.000 0.188
#> ERR978153 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978169 4 0.000 0.977 0 0.000 0.000 1.000 0.000
#> ERR978170 4 0.000 0.977 0 0.000 0.000 1.000 0.000
#> ERR978171 4 0.000 0.977 0 0.000 0.000 1.000 0.000
#> ERR978172 4 0.000 0.977 0 0.000 0.000 1.000 0.000
#> ERR978173 4 0.000 0.977 0 0.000 0.000 1.000 0.000
#> ERR978174 4 0.000 0.977 0 0.000 0.000 1.000 0.000
#> ERR978175 4 0.000 0.977 0 0.000 0.000 1.000 0.000
#> ERR978176 4 0.120 0.977 0 0.000 0.000 0.952 0.048
#> ERR978177 4 0.120 0.977 0 0.000 0.000 0.952 0.048
#> ERR978178 4 0.120 0.977 0 0.000 0.000 0.952 0.048
#> ERR978179 4 0.120 0.977 0 0.000 0.000 0.952 0.048
#> ERR978180 4 0.120 0.977 0 0.000 0.000 0.952 0.048
#> ERR978181 4 0.120 0.977 0 0.000 0.000 0.952 0.048
#> ERR978182 4 0.120 0.977 0 0.000 0.000 0.952 0.048
#> ERR978183 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978197 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978198 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978199 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978200 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978201 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978202 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978203 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978204 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978205 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978206 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978207 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978208 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978209 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978210 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978211 5 0.440 0.835 0 0.028 0.276 0.000 0.696
#> ERR978212 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978213 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978214 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978215 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978216 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978217 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978218 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978219 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978220 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978221 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978222 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978223 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978224 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978225 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978226 5 0.120 0.838 0 0.048 0.000 0.000 0.952
#> ERR978227 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978241 3 0.577 0.652 0 0.000 0.600 0.264 0.136
#> ERR978242 3 0.577 0.652 0 0.000 0.600 0.264 0.136
#> ERR978243 3 0.577 0.652 0 0.000 0.600 0.264 0.136
#> ERR978244 3 0.577 0.652 0 0.000 0.600 0.264 0.136
#> ERR978245 3 0.577 0.652 0 0.000 0.600 0.264 0.136
#> ERR978246 3 0.577 0.652 0 0.000 0.600 0.264 0.136
#> ERR978247 3 0.577 0.652 0 0.000 0.600 0.264 0.136
#> ERR978248 3 0.590 0.652 0 0.000 0.600 0.216 0.184
#> ERR978249 3 0.590 0.652 0 0.000 0.600 0.216 0.184
#> ERR978250 3 0.590 0.652 0 0.000 0.600 0.216 0.184
#> ERR978251 3 0.590 0.652 0 0.000 0.600 0.216 0.184
#> ERR978252 3 0.590 0.652 0 0.000 0.600 0.216 0.184
#> ERR978253 3 0.590 0.652 0 0.000 0.600 0.216 0.184
#> ERR978254 3 0.590 0.652 0 0.000 0.600 0.216 0.184
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978123 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978124 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978125 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978126 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978127 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978128 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978129 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978130 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978131 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978132 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978133 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978134 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978135 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978136 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978137 3 0.270 0.873 0 0 0.812 0.000 0.188 0.000
#> ERR978138 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978139 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978140 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978141 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978142 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978143 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978144 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978145 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978146 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978147 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978148 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978149 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978150 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978151 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978152 3 0.000 0.872 0 0 1.000 0.000 0.000 0.000
#> ERR978153 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978169 4 0.000 0.813 0 0 0.000 1.000 0.000 0.000
#> ERR978170 4 0.000 0.813 0 0 0.000 1.000 0.000 0.000
#> ERR978171 4 0.000 0.813 0 0 0.000 1.000 0.000 0.000
#> ERR978172 4 0.000 0.813 0 0 0.000 1.000 0.000 0.000
#> ERR978173 4 0.000 0.813 0 0 0.000 1.000 0.000 0.000
#> ERR978174 4 0.000 0.813 0 0 0.000 1.000 0.000 0.000
#> ERR978175 4 0.000 0.813 0 0 0.000 1.000 0.000 0.000
#> ERR978176 4 0.322 0.813 0 0 0.000 0.736 0.000 0.264
#> ERR978177 4 0.322 0.813 0 0 0.000 0.736 0.000 0.264
#> ERR978178 4 0.322 0.813 0 0 0.000 0.736 0.000 0.264
#> ERR978179 4 0.322 0.813 0 0 0.000 0.736 0.000 0.264
#> ERR978180 4 0.322 0.813 0 0 0.000 0.736 0.000 0.264
#> ERR978181 4 0.322 0.813 0 0 0.000 0.736 0.000 0.264
#> ERR978182 4 0.322 0.813 0 0 0.000 0.736 0.000 0.264
#> ERR978183 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978197 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978198 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978199 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978200 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978201 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978202 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978203 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978204 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978205 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978206 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978207 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978208 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978209 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978210 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978211 5 0.166 0.828 0 0 0.088 0.000 0.912 0.000
#> ERR978212 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978213 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978214 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978215 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978216 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978217 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978218 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978219 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978220 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978221 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978222 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978223 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978224 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978225 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978226 5 0.270 0.834 0 0 0.188 0.000 0.812 0.000
#> ERR978227 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978241 6 0.322 0.813 0 0 0.000 0.264 0.000 0.736
#> ERR978242 6 0.322 0.813 0 0 0.000 0.264 0.000 0.736
#> ERR978243 6 0.322 0.813 0 0 0.000 0.264 0.000 0.736
#> ERR978244 6 0.322 0.813 0 0 0.000 0.264 0.000 0.736
#> ERR978245 6 0.322 0.813 0 0 0.000 0.264 0.000 0.736
#> ERR978246 6 0.322 0.813 0 0 0.000 0.264 0.000 0.736
#> ERR978247 6 0.322 0.813 0 0 0.000 0.264 0.000 0.736
#> ERR978248 6 0.000 0.813 0 0 0.000 0.000 0.000 1.000
#> ERR978249 6 0.000 0.813 0 0 0.000 0.000 0.000 1.000
#> ERR978250 6 0.000 0.813 0 0 0.000 0.000 0.000 1.000
#> ERR978251 6 0.000 0.813 0 0 0.000 0.000 0.000 1.000
#> ERR978252 6 0.000 0.813 0 0 0.000 0.000 0.000 1.000
#> ERR978253 6 0.000 0.813 0 0 0.000 0.000 0.000 1.000
#> ERR978254 6 0.000 0.813 0 0 0.000 0.000 0.000 1.000
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", "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 14049 rows and 148 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 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 0.175 0.710 0.793 0.3827 0.675 0.675
#> 3 3 0.312 0.635 0.695 0.4978 0.686 0.534
#> 4 4 0.444 0.688 0.731 0.1976 0.724 0.397
#> 5 5 0.621 0.665 0.713 0.0917 1.000 1.000
#> 6 6 0.718 0.688 0.710 0.0606 0.917 0.702
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
#> ERR978107 2 0.6438 0.690 0.164 0.836
#> ERR978108 2 0.6438 0.690 0.164 0.836
#> ERR978109 2 0.6438 0.690 0.164 0.836
#> ERR978110 2 0.6438 0.690 0.164 0.836
#> ERR978111 2 0.6438 0.690 0.164 0.836
#> ERR978112 2 0.6438 0.690 0.164 0.836
#> ERR978113 2 0.6438 0.690 0.164 0.836
#> ERR978114 2 0.6438 0.690 0.164 0.836
#> ERR978115 2 0.6438 0.690 0.164 0.836
#> ERR978116 2 0.6438 0.690 0.164 0.836
#> ERR978117 2 0.6438 0.690 0.164 0.836
#> ERR978118 2 0.6438 0.690 0.164 0.836
#> ERR978119 2 0.6438 0.690 0.164 0.836
#> ERR978120 2 0.6438 0.690 0.164 0.836
#> ERR978121 2 0.6438 0.690 0.164 0.836
#> ERR978122 2 0.6438 0.690 0.164 0.836
#> ERR978123 2 0.8386 0.585 0.268 0.732
#> ERR978124 2 0.8386 0.585 0.268 0.732
#> ERR978125 2 0.8386 0.585 0.268 0.732
#> ERR978126 2 0.8386 0.585 0.268 0.732
#> ERR978127 2 0.8386 0.585 0.268 0.732
#> ERR978128 2 0.8386 0.585 0.268 0.732
#> ERR978129 2 0.8386 0.585 0.268 0.732
#> ERR978130 2 0.8386 0.585 0.268 0.732
#> ERR978131 2 0.8386 0.585 0.268 0.732
#> ERR978132 2 0.8386 0.585 0.268 0.732
#> ERR978133 2 0.8386 0.585 0.268 0.732
#> ERR978134 2 0.8386 0.585 0.268 0.732
#> ERR978135 2 0.8386 0.585 0.268 0.732
#> ERR978136 2 0.8386 0.585 0.268 0.732
#> ERR978137 2 0.8386 0.585 0.268 0.732
#> ERR978138 2 0.7139 0.669 0.196 0.804
#> ERR978139 2 0.7139 0.669 0.196 0.804
#> ERR978140 2 0.7139 0.669 0.196 0.804
#> ERR978141 2 0.7139 0.669 0.196 0.804
#> ERR978142 2 0.7139 0.669 0.196 0.804
#> ERR978143 2 0.7139 0.669 0.196 0.804
#> ERR978144 2 0.7139 0.669 0.196 0.804
#> ERR978145 2 0.7139 0.669 0.196 0.804
#> ERR978146 2 0.7376 0.659 0.208 0.792
#> ERR978147 2 0.7376 0.659 0.208 0.792
#> ERR978148 2 0.7376 0.659 0.208 0.792
#> ERR978149 2 0.7376 0.659 0.208 0.792
#> ERR978150 2 0.7376 0.659 0.208 0.792
#> ERR978151 2 0.7376 0.659 0.208 0.792
#> ERR978152 2 0.7376 0.659 0.208 0.792
#> ERR978153 1 0.7950 1.000 0.760 0.240
#> ERR978154 1 0.7950 1.000 0.760 0.240
#> ERR978155 1 0.7950 1.000 0.760 0.240
#> ERR978156 1 0.7950 1.000 0.760 0.240
#> ERR978157 1 0.7950 1.000 0.760 0.240
#> ERR978158 1 0.7950 1.000 0.760 0.240
#> ERR978159 1 0.7950 1.000 0.760 0.240
#> ERR978160 1 0.7950 1.000 0.760 0.240
#> ERR978161 1 0.7950 1.000 0.760 0.240
#> ERR978162 1 0.7950 1.000 0.760 0.240
#> ERR978163 1 0.7950 1.000 0.760 0.240
#> ERR978164 1 0.7950 1.000 0.760 0.240
#> ERR978165 1 0.7950 1.000 0.760 0.240
#> ERR978166 1 0.7950 1.000 0.760 0.240
#> ERR978167 1 0.7950 1.000 0.760 0.240
#> ERR978168 1 0.7950 1.000 0.760 0.240
#> ERR978169 2 0.9970 0.374 0.468 0.532
#> ERR978170 2 0.9970 0.374 0.468 0.532
#> ERR978171 2 0.9970 0.374 0.468 0.532
#> ERR978172 2 0.9970 0.374 0.468 0.532
#> ERR978173 2 0.9970 0.374 0.468 0.532
#> ERR978174 2 0.9970 0.374 0.468 0.532
#> ERR978175 2 0.9970 0.374 0.468 0.532
#> ERR978176 2 0.9977 0.373 0.472 0.528
#> ERR978177 2 0.9977 0.373 0.472 0.528
#> ERR978178 2 0.9977 0.373 0.472 0.528
#> ERR978179 2 0.9977 0.373 0.472 0.528
#> ERR978180 2 0.9977 0.373 0.472 0.528
#> ERR978181 2 0.9977 0.373 0.472 0.528
#> ERR978182 2 0.9977 0.373 0.472 0.528
#> ERR978183 2 0.6438 0.690 0.164 0.836
#> ERR978184 2 0.6438 0.690 0.164 0.836
#> ERR978185 2 0.6438 0.690 0.164 0.836
#> ERR978186 2 0.6438 0.690 0.164 0.836
#> ERR978187 2 0.6438 0.690 0.164 0.836
#> ERR978188 2 0.6438 0.690 0.164 0.836
#> ERR978189 2 0.6438 0.690 0.164 0.836
#> ERR978190 2 0.6438 0.690 0.164 0.836
#> ERR978191 2 0.6438 0.690 0.164 0.836
#> ERR978192 2 0.6438 0.690 0.164 0.836
#> ERR978193 2 0.6438 0.690 0.164 0.836
#> ERR978194 2 0.6438 0.690 0.164 0.836
#> ERR978195 2 0.6438 0.690 0.164 0.836
#> ERR978196 2 0.6438 0.690 0.164 0.836
#> ERR978197 2 0.0000 0.741 0.000 1.000
#> ERR978198 2 0.0000 0.741 0.000 1.000
#> ERR978199 2 0.0000 0.741 0.000 1.000
#> ERR978200 2 0.0000 0.741 0.000 1.000
#> ERR978201 2 0.0000 0.741 0.000 1.000
#> ERR978202 2 0.0000 0.741 0.000 1.000
#> ERR978203 2 0.0000 0.741 0.000 1.000
#> ERR978204 2 0.0376 0.741 0.004 0.996
#> ERR978205 2 0.0376 0.741 0.004 0.996
#> ERR978206 2 0.0376 0.741 0.004 0.996
#> ERR978207 2 0.0376 0.741 0.004 0.996
#> ERR978208 2 0.0376 0.741 0.004 0.996
#> ERR978209 2 0.0376 0.741 0.004 0.996
#> ERR978210 2 0.0376 0.741 0.004 0.996
#> ERR978211 2 0.0376 0.741 0.004 0.996
#> ERR978212 2 0.0938 0.740 0.012 0.988
#> ERR978213 2 0.0938 0.740 0.012 0.988
#> ERR978214 2 0.0938 0.740 0.012 0.988
#> ERR978215 2 0.0938 0.740 0.012 0.988
#> ERR978216 2 0.0938 0.740 0.012 0.988
#> ERR978217 2 0.0938 0.740 0.012 0.988
#> ERR978218 2 0.0938 0.740 0.012 0.988
#> ERR978219 2 0.0938 0.740 0.012 0.988
#> ERR978220 2 0.0938 0.740 0.012 0.988
#> ERR978221 2 0.0938 0.740 0.012 0.988
#> ERR978222 2 0.0938 0.740 0.012 0.988
#> ERR978223 2 0.0938 0.740 0.012 0.988
#> ERR978224 2 0.0938 0.740 0.012 0.988
#> ERR978225 2 0.0938 0.740 0.012 0.988
#> ERR978226 2 0.0938 0.740 0.012 0.988
#> ERR978227 1 0.7950 1.000 0.760 0.240
#> ERR978228 1 0.7950 1.000 0.760 0.240
#> ERR978229 1 0.7950 1.000 0.760 0.240
#> ERR978230 1 0.7950 1.000 0.760 0.240
#> ERR978231 1 0.7950 1.000 0.760 0.240
#> ERR978232 1 0.7950 1.000 0.760 0.240
#> ERR978233 1 0.7950 1.000 0.760 0.240
#> ERR978234 1 0.7950 1.000 0.760 0.240
#> ERR978235 1 0.7950 1.000 0.760 0.240
#> ERR978236 1 0.7950 1.000 0.760 0.240
#> ERR978237 1 0.7950 1.000 0.760 0.240
#> ERR978238 1 0.7950 1.000 0.760 0.240
#> ERR978239 1 0.7950 1.000 0.760 0.240
#> ERR978240 1 0.7950 1.000 0.760 0.240
#> ERR978241 2 0.9795 0.441 0.416 0.584
#> ERR978242 2 0.9795 0.441 0.416 0.584
#> ERR978243 2 0.9795 0.441 0.416 0.584
#> ERR978244 2 0.9795 0.441 0.416 0.584
#> ERR978245 2 0.9795 0.441 0.416 0.584
#> ERR978246 2 0.9795 0.441 0.416 0.584
#> ERR978247 2 0.9795 0.441 0.416 0.584
#> ERR978248 2 0.5737 0.726 0.136 0.864
#> ERR978249 2 0.5737 0.726 0.136 0.864
#> ERR978250 2 0.5737 0.726 0.136 0.864
#> ERR978251 2 0.5737 0.726 0.136 0.864
#> ERR978252 2 0.5737 0.726 0.136 0.864
#> ERR978253 2 0.5737 0.726 0.136 0.864
#> ERR978254 2 0.5737 0.726 0.136 0.864
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.249 0.622 0.016 0.936 0.048
#> ERR978108 2 0.249 0.622 0.016 0.936 0.048
#> ERR978109 2 0.249 0.622 0.016 0.936 0.048
#> ERR978110 2 0.249 0.622 0.016 0.936 0.048
#> ERR978111 2 0.249 0.622 0.016 0.936 0.048
#> ERR978112 2 0.249 0.622 0.016 0.936 0.048
#> ERR978113 2 0.249 0.622 0.016 0.936 0.048
#> ERR978114 2 0.249 0.622 0.016 0.936 0.048
#> ERR978115 2 0.249 0.622 0.016 0.936 0.048
#> ERR978116 2 0.249 0.622 0.016 0.936 0.048
#> ERR978117 2 0.249 0.622 0.016 0.936 0.048
#> ERR978118 2 0.249 0.622 0.016 0.936 0.048
#> ERR978119 2 0.249 0.622 0.016 0.936 0.048
#> ERR978120 2 0.249 0.622 0.016 0.936 0.048
#> ERR978121 2 0.249 0.622 0.016 0.936 0.048
#> ERR978122 2 0.249 0.622 0.016 0.936 0.048
#> ERR978123 3 0.841 0.606 0.112 0.308 0.580
#> ERR978124 3 0.841 0.606 0.112 0.308 0.580
#> ERR978125 3 0.841 0.606 0.112 0.308 0.580
#> ERR978126 3 0.841 0.606 0.112 0.308 0.580
#> ERR978127 3 0.841 0.606 0.112 0.308 0.580
#> ERR978128 3 0.841 0.606 0.112 0.308 0.580
#> ERR978129 3 0.841 0.606 0.112 0.308 0.580
#> ERR978130 3 0.841 0.606 0.112 0.308 0.580
#> ERR978131 3 0.845 0.598 0.112 0.316 0.572
#> ERR978132 3 0.845 0.598 0.112 0.316 0.572
#> ERR978133 3 0.845 0.598 0.112 0.316 0.572
#> ERR978134 3 0.845 0.598 0.112 0.316 0.572
#> ERR978135 3 0.845 0.598 0.112 0.316 0.572
#> ERR978136 3 0.845 0.598 0.112 0.316 0.572
#> ERR978137 3 0.845 0.598 0.112 0.316 0.572
#> ERR978138 3 0.816 0.600 0.092 0.320 0.588
#> ERR978139 3 0.816 0.600 0.092 0.320 0.588
#> ERR978140 3 0.816 0.600 0.092 0.320 0.588
#> ERR978141 3 0.816 0.600 0.092 0.320 0.588
#> ERR978142 3 0.816 0.600 0.092 0.320 0.588
#> ERR978143 3 0.816 0.600 0.092 0.320 0.588
#> ERR978144 3 0.816 0.600 0.092 0.320 0.588
#> ERR978145 3 0.816 0.600 0.092 0.320 0.588
#> ERR978146 3 0.816 0.600 0.092 0.320 0.588
#> ERR978147 3 0.816 0.600 0.092 0.320 0.588
#> ERR978148 3 0.816 0.600 0.092 0.320 0.588
#> ERR978149 3 0.816 0.600 0.092 0.320 0.588
#> ERR978150 3 0.816 0.600 0.092 0.320 0.588
#> ERR978151 3 0.816 0.600 0.092 0.320 0.588
#> ERR978152 3 0.816 0.600 0.092 0.320 0.588
#> ERR978153 1 0.113 0.963 0.976 0.020 0.004
#> ERR978154 1 0.113 0.963 0.976 0.020 0.004
#> ERR978155 1 0.113 0.963 0.976 0.020 0.004
#> ERR978156 1 0.113 0.963 0.976 0.020 0.004
#> ERR978157 1 0.113 0.963 0.976 0.020 0.004
#> ERR978158 1 0.113 0.963 0.976 0.020 0.004
#> ERR978159 1 0.113 0.963 0.976 0.020 0.004
#> ERR978160 1 0.113 0.963 0.976 0.020 0.004
#> ERR978161 1 0.113 0.963 0.976 0.020 0.004
#> ERR978162 1 0.113 0.963 0.976 0.020 0.004
#> ERR978163 1 0.113 0.963 0.976 0.020 0.004
#> ERR978164 1 0.113 0.963 0.976 0.020 0.004
#> ERR978165 1 0.113 0.963 0.976 0.020 0.004
#> ERR978166 1 0.113 0.963 0.976 0.020 0.004
#> ERR978167 1 0.113 0.963 0.976 0.020 0.004
#> ERR978168 1 0.113 0.963 0.976 0.020 0.004
#> ERR978169 3 0.812 0.648 0.164 0.188 0.648
#> ERR978170 3 0.812 0.648 0.164 0.188 0.648
#> ERR978171 3 0.812 0.648 0.164 0.188 0.648
#> ERR978172 3 0.812 0.648 0.164 0.188 0.648
#> ERR978173 3 0.812 0.648 0.164 0.188 0.648
#> ERR978174 3 0.812 0.648 0.164 0.188 0.648
#> ERR978175 3 0.812 0.648 0.164 0.188 0.648
#> ERR978176 3 0.825 0.643 0.164 0.200 0.636
#> ERR978177 3 0.825 0.643 0.164 0.200 0.636
#> ERR978178 3 0.825 0.643 0.164 0.200 0.636
#> ERR978179 3 0.825 0.643 0.164 0.200 0.636
#> ERR978180 3 0.825 0.643 0.164 0.200 0.636
#> ERR978181 3 0.825 0.643 0.164 0.200 0.636
#> ERR978182 3 0.825 0.643 0.164 0.200 0.636
#> ERR978183 2 0.178 0.630 0.020 0.960 0.020
#> ERR978184 2 0.178 0.630 0.020 0.960 0.020
#> ERR978185 2 0.178 0.630 0.020 0.960 0.020
#> ERR978186 2 0.178 0.630 0.020 0.960 0.020
#> ERR978187 2 0.178 0.630 0.020 0.960 0.020
#> ERR978188 2 0.178 0.630 0.020 0.960 0.020
#> ERR978189 2 0.178 0.630 0.020 0.960 0.020
#> ERR978190 2 0.178 0.630 0.020 0.960 0.020
#> ERR978191 2 0.178 0.630 0.020 0.960 0.020
#> ERR978192 2 0.178 0.630 0.020 0.960 0.020
#> ERR978193 2 0.178 0.630 0.020 0.960 0.020
#> ERR978194 2 0.178 0.630 0.020 0.960 0.020
#> ERR978195 2 0.178 0.630 0.020 0.960 0.020
#> ERR978196 2 0.178 0.630 0.020 0.960 0.020
#> ERR978197 2 0.692 0.412 0.020 0.580 0.400
#> ERR978198 2 0.692 0.412 0.020 0.580 0.400
#> ERR978199 2 0.692 0.412 0.020 0.580 0.400
#> ERR978200 2 0.692 0.412 0.020 0.580 0.400
#> ERR978201 2 0.692 0.412 0.020 0.580 0.400
#> ERR978202 2 0.692 0.412 0.020 0.580 0.400
#> ERR978203 2 0.692 0.412 0.020 0.580 0.400
#> ERR978204 2 0.675 0.440 0.016 0.596 0.388
#> ERR978205 2 0.675 0.440 0.016 0.596 0.388
#> ERR978206 2 0.675 0.440 0.016 0.596 0.388
#> ERR978207 2 0.675 0.440 0.016 0.596 0.388
#> ERR978208 2 0.675 0.440 0.016 0.596 0.388
#> ERR978209 2 0.675 0.440 0.016 0.596 0.388
#> ERR978210 2 0.675 0.440 0.016 0.596 0.388
#> ERR978211 2 0.675 0.440 0.016 0.596 0.388
#> ERR978212 2 0.757 0.470 0.052 0.592 0.356
#> ERR978213 2 0.757 0.470 0.052 0.592 0.356
#> ERR978214 2 0.757 0.470 0.052 0.592 0.356
#> ERR978215 2 0.757 0.470 0.052 0.592 0.356
#> ERR978216 2 0.757 0.470 0.052 0.592 0.356
#> ERR978217 2 0.757 0.470 0.052 0.592 0.356
#> ERR978218 2 0.757 0.470 0.052 0.592 0.356
#> ERR978219 2 0.757 0.470 0.052 0.592 0.356
#> ERR978220 2 0.757 0.470 0.052 0.592 0.356
#> ERR978221 2 0.757 0.470 0.052 0.592 0.356
#> ERR978222 2 0.757 0.470 0.052 0.592 0.356
#> ERR978223 2 0.757 0.470 0.052 0.592 0.356
#> ERR978224 2 0.757 0.470 0.052 0.592 0.356
#> ERR978225 2 0.757 0.470 0.052 0.592 0.356
#> ERR978226 2 0.757 0.470 0.052 0.592 0.356
#> ERR978227 1 0.391 0.958 0.876 0.020 0.104
#> ERR978228 1 0.391 0.958 0.876 0.020 0.104
#> ERR978229 1 0.391 0.958 0.876 0.020 0.104
#> ERR978230 1 0.391 0.958 0.876 0.020 0.104
#> ERR978231 1 0.391 0.958 0.876 0.020 0.104
#> ERR978232 1 0.391 0.958 0.876 0.020 0.104
#> ERR978233 1 0.391 0.958 0.876 0.020 0.104
#> ERR978234 1 0.391 0.958 0.876 0.020 0.104
#> ERR978235 1 0.391 0.958 0.876 0.020 0.104
#> ERR978236 1 0.391 0.958 0.876 0.020 0.104
#> ERR978237 1 0.391 0.958 0.876 0.020 0.104
#> ERR978238 1 0.391 0.958 0.876 0.020 0.104
#> ERR978239 1 0.391 0.958 0.876 0.020 0.104
#> ERR978240 1 0.391 0.958 0.876 0.020 0.104
#> ERR978241 3 0.788 0.651 0.172 0.160 0.668
#> ERR978242 3 0.788 0.651 0.172 0.160 0.668
#> ERR978243 3 0.788 0.651 0.172 0.160 0.668
#> ERR978244 3 0.788 0.651 0.172 0.160 0.668
#> ERR978245 3 0.788 0.651 0.172 0.160 0.668
#> ERR978246 3 0.788 0.651 0.172 0.160 0.668
#> ERR978247 3 0.788 0.651 0.172 0.160 0.668
#> ERR978248 2 0.851 0.199 0.100 0.528 0.372
#> ERR978249 2 0.851 0.199 0.100 0.528 0.372
#> ERR978250 2 0.851 0.199 0.100 0.528 0.372
#> ERR978251 2 0.851 0.199 0.100 0.528 0.372
#> ERR978252 2 0.851 0.199 0.100 0.528 0.372
#> ERR978253 2 0.851 0.199 0.100 0.528 0.372
#> ERR978254 2 0.851 0.199 0.100 0.528 0.372
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.5127 0.882 0.016 0.776 0.152 0.056
#> ERR978108 2 0.5127 0.882 0.016 0.776 0.152 0.056
#> ERR978109 2 0.5127 0.882 0.016 0.776 0.152 0.056
#> ERR978110 2 0.5127 0.882 0.016 0.776 0.152 0.056
#> ERR978111 2 0.5127 0.882 0.016 0.776 0.152 0.056
#> ERR978112 2 0.5127 0.882 0.016 0.776 0.152 0.056
#> ERR978113 2 0.5127 0.882 0.016 0.776 0.152 0.056
#> ERR978114 2 0.5127 0.882 0.016 0.776 0.152 0.056
#> ERR978115 2 0.4960 0.882 0.008 0.780 0.152 0.060
#> ERR978116 2 0.4960 0.882 0.008 0.780 0.152 0.060
#> ERR978117 2 0.4960 0.882 0.008 0.780 0.152 0.060
#> ERR978118 2 0.4960 0.882 0.008 0.780 0.152 0.060
#> ERR978119 2 0.4960 0.882 0.008 0.780 0.152 0.060
#> ERR978120 2 0.4960 0.882 0.008 0.780 0.152 0.060
#> ERR978121 2 0.4960 0.882 0.008 0.780 0.152 0.060
#> ERR978122 2 0.4960 0.882 0.008 0.780 0.152 0.060
#> ERR978123 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978124 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978125 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978126 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978127 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978128 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978129 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978130 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978131 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978132 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978133 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978134 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978135 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978136 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978137 3 0.3322 0.486 0.040 0.032 0.892 0.036
#> ERR978138 3 0.5723 0.481 0.020 0.076 0.740 0.164
#> ERR978139 3 0.5723 0.481 0.020 0.076 0.740 0.164
#> ERR978140 3 0.5723 0.481 0.020 0.076 0.740 0.164
#> ERR978141 3 0.5723 0.481 0.020 0.076 0.740 0.164
#> ERR978142 3 0.5723 0.481 0.020 0.076 0.740 0.164
#> ERR978143 3 0.5723 0.481 0.020 0.076 0.740 0.164
#> ERR978144 3 0.5723 0.481 0.020 0.076 0.740 0.164
#> ERR978145 3 0.5723 0.481 0.020 0.076 0.740 0.164
#> ERR978146 3 0.5589 0.481 0.020 0.076 0.752 0.152
#> ERR978147 3 0.5589 0.481 0.020 0.076 0.752 0.152
#> ERR978148 3 0.5589 0.481 0.020 0.076 0.752 0.152
#> ERR978149 3 0.5589 0.481 0.020 0.076 0.752 0.152
#> ERR978150 3 0.5589 0.481 0.020 0.076 0.752 0.152
#> ERR978151 3 0.5589 0.481 0.020 0.076 0.752 0.152
#> ERR978152 3 0.5589 0.481 0.020 0.076 0.752 0.152
#> ERR978153 1 0.4469 0.926 0.816 0.044 0.012 0.128
#> ERR978154 1 0.4469 0.926 0.816 0.044 0.012 0.128
#> ERR978155 1 0.4469 0.926 0.816 0.044 0.012 0.128
#> ERR978156 1 0.4469 0.926 0.816 0.044 0.012 0.128
#> ERR978157 1 0.4469 0.926 0.816 0.044 0.012 0.128
#> ERR978158 1 0.4469 0.926 0.816 0.044 0.012 0.128
#> ERR978159 1 0.4469 0.926 0.816 0.044 0.012 0.128
#> ERR978160 1 0.4469 0.926 0.816 0.044 0.012 0.128
#> ERR978161 1 0.4360 0.926 0.816 0.032 0.012 0.140
#> ERR978162 1 0.4360 0.926 0.816 0.032 0.012 0.140
#> ERR978163 1 0.4360 0.926 0.816 0.032 0.012 0.140
#> ERR978164 1 0.4360 0.926 0.816 0.032 0.012 0.140
#> ERR978165 1 0.4360 0.926 0.816 0.032 0.012 0.140
#> ERR978166 1 0.4360 0.926 0.816 0.032 0.012 0.140
#> ERR978167 1 0.4360 0.926 0.816 0.032 0.012 0.140
#> ERR978168 1 0.4360 0.926 0.816 0.032 0.012 0.140
#> ERR978169 4 0.7458 0.774 0.120 0.024 0.304 0.552
#> ERR978170 4 0.7458 0.774 0.120 0.024 0.304 0.552
#> ERR978171 4 0.7458 0.774 0.120 0.024 0.304 0.552
#> ERR978172 4 0.7458 0.774 0.120 0.024 0.304 0.552
#> ERR978173 4 0.7458 0.774 0.120 0.024 0.304 0.552
#> ERR978174 4 0.7458 0.774 0.120 0.024 0.304 0.552
#> ERR978175 4 0.7458 0.774 0.120 0.024 0.304 0.552
#> ERR978176 4 0.7708 0.777 0.116 0.040 0.296 0.548
#> ERR978177 4 0.7708 0.777 0.116 0.040 0.296 0.548
#> ERR978178 4 0.7708 0.777 0.116 0.040 0.296 0.548
#> ERR978179 4 0.7708 0.777 0.116 0.040 0.296 0.548
#> ERR978180 4 0.7708 0.777 0.116 0.040 0.296 0.548
#> ERR978181 4 0.7708 0.777 0.116 0.040 0.296 0.548
#> ERR978182 4 0.7708 0.777 0.116 0.040 0.296 0.548
#> ERR978183 2 0.4004 0.866 0.012 0.852 0.068 0.068
#> ERR978184 2 0.4004 0.866 0.012 0.852 0.068 0.068
#> ERR978185 2 0.4004 0.866 0.012 0.852 0.068 0.068
#> ERR978186 2 0.4004 0.866 0.012 0.852 0.068 0.068
#> ERR978187 2 0.4004 0.866 0.012 0.852 0.068 0.068
#> ERR978188 2 0.4004 0.866 0.012 0.852 0.068 0.068
#> ERR978189 2 0.4004 0.866 0.012 0.852 0.068 0.068
#> ERR978190 2 0.3876 0.866 0.008 0.856 0.068 0.068
#> ERR978191 2 0.3876 0.866 0.008 0.856 0.068 0.068
#> ERR978192 2 0.3876 0.866 0.008 0.856 0.068 0.068
#> ERR978193 2 0.3876 0.866 0.008 0.856 0.068 0.068
#> ERR978194 2 0.3876 0.866 0.008 0.856 0.068 0.068
#> ERR978195 2 0.3876 0.866 0.008 0.856 0.068 0.068
#> ERR978196 2 0.3876 0.866 0.008 0.856 0.068 0.068
#> ERR978197 3 0.5653 0.587 0.000 0.192 0.712 0.096
#> ERR978198 3 0.5653 0.587 0.000 0.192 0.712 0.096
#> ERR978199 3 0.5653 0.587 0.000 0.192 0.712 0.096
#> ERR978200 3 0.5653 0.587 0.000 0.192 0.712 0.096
#> ERR978201 3 0.5653 0.587 0.000 0.192 0.712 0.096
#> ERR978202 3 0.5653 0.587 0.000 0.192 0.712 0.096
#> ERR978203 3 0.5653 0.587 0.000 0.192 0.712 0.096
#> ERR978204 3 0.6195 0.561 0.000 0.252 0.648 0.100
#> ERR978205 3 0.6195 0.561 0.000 0.252 0.648 0.100
#> ERR978206 3 0.6195 0.561 0.000 0.252 0.648 0.100
#> ERR978207 3 0.6195 0.561 0.000 0.252 0.648 0.100
#> ERR978208 3 0.6195 0.561 0.000 0.252 0.648 0.100
#> ERR978209 3 0.6195 0.561 0.000 0.252 0.648 0.100
#> ERR978210 3 0.6195 0.561 0.000 0.252 0.648 0.100
#> ERR978211 3 0.6195 0.561 0.000 0.252 0.648 0.100
#> ERR978212 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978213 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978214 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978215 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978216 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978217 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978218 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978219 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978220 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978221 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978222 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978223 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978224 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978225 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978226 3 0.7908 0.494 0.004 0.340 0.416 0.240
#> ERR978227 1 0.0657 0.917 0.984 0.004 0.012 0.000
#> ERR978228 1 0.0657 0.917 0.984 0.004 0.012 0.000
#> ERR978229 1 0.0657 0.917 0.984 0.004 0.012 0.000
#> ERR978230 1 0.0657 0.917 0.984 0.004 0.012 0.000
#> ERR978231 1 0.0657 0.917 0.984 0.004 0.012 0.000
#> ERR978232 1 0.0657 0.917 0.984 0.004 0.012 0.000
#> ERR978233 1 0.0657 0.917 0.984 0.004 0.012 0.000
#> ERR978234 1 0.0657 0.917 0.984 0.000 0.012 0.004
#> ERR978235 1 0.0657 0.917 0.984 0.000 0.012 0.004
#> ERR978236 1 0.0657 0.917 0.984 0.000 0.012 0.004
#> ERR978237 1 0.0657 0.917 0.984 0.000 0.012 0.004
#> ERR978238 1 0.0657 0.917 0.984 0.000 0.012 0.004
#> ERR978239 1 0.0657 0.917 0.984 0.000 0.012 0.004
#> ERR978240 1 0.0657 0.917 0.984 0.000 0.012 0.004
#> ERR978241 4 0.6840 0.761 0.104 0.004 0.332 0.560
#> ERR978242 4 0.6840 0.761 0.104 0.004 0.332 0.560
#> ERR978243 4 0.6840 0.761 0.104 0.004 0.332 0.560
#> ERR978244 4 0.6840 0.761 0.104 0.004 0.332 0.560
#> ERR978245 4 0.6840 0.761 0.104 0.004 0.332 0.560
#> ERR978246 4 0.6840 0.761 0.104 0.004 0.332 0.560
#> ERR978247 4 0.6840 0.761 0.104 0.004 0.332 0.560
#> ERR978248 4 0.9163 0.170 0.068 0.292 0.300 0.340
#> ERR978249 4 0.9163 0.170 0.068 0.292 0.300 0.340
#> ERR978250 4 0.9163 0.170 0.068 0.292 0.300 0.340
#> ERR978251 4 0.9163 0.170 0.068 0.292 0.300 0.340
#> ERR978252 4 0.9163 0.170 0.068 0.292 0.300 0.340
#> ERR978253 4 0.9163 0.170 0.068 0.292 0.300 0.340
#> ERR978254 4 0.9163 0.170 0.068 0.292 0.300 0.340
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.1310 0.869 0.000 0.956 0.020 0.024 NA
#> ERR978108 2 0.1310 0.869 0.000 0.956 0.020 0.024 NA
#> ERR978109 2 0.1310 0.869 0.000 0.956 0.020 0.024 NA
#> ERR978110 2 0.1310 0.869 0.000 0.956 0.020 0.024 NA
#> ERR978111 2 0.1310 0.869 0.000 0.956 0.020 0.024 NA
#> ERR978112 2 0.1310 0.869 0.000 0.956 0.020 0.024 NA
#> ERR978113 2 0.1310 0.869 0.000 0.956 0.020 0.024 NA
#> ERR978114 2 0.1310 0.869 0.000 0.956 0.020 0.024 NA
#> ERR978115 2 0.2006 0.869 0.000 0.932 0.020 0.024 NA
#> ERR978116 2 0.2006 0.869 0.000 0.932 0.020 0.024 NA
#> ERR978117 2 0.2006 0.869 0.000 0.932 0.020 0.024 NA
#> ERR978118 2 0.2006 0.869 0.000 0.932 0.020 0.024 NA
#> ERR978119 2 0.2006 0.869 0.000 0.932 0.020 0.024 NA
#> ERR978120 2 0.2006 0.869 0.000 0.932 0.020 0.024 NA
#> ERR978121 2 0.2006 0.869 0.000 0.932 0.020 0.024 NA
#> ERR978122 2 0.2006 0.869 0.000 0.932 0.020 0.024 NA
#> ERR978123 3 0.7699 0.468 0.028 0.048 0.500 0.164 NA
#> ERR978124 3 0.7699 0.468 0.028 0.048 0.500 0.164 NA
#> ERR978125 3 0.7699 0.468 0.028 0.048 0.500 0.164 NA
#> ERR978126 3 0.7699 0.468 0.028 0.048 0.500 0.164 NA
#> ERR978127 3 0.7699 0.468 0.028 0.048 0.500 0.164 NA
#> ERR978128 3 0.7699 0.468 0.028 0.048 0.500 0.164 NA
#> ERR978129 3 0.7699 0.468 0.028 0.048 0.500 0.164 NA
#> ERR978130 3 0.7699 0.468 0.028 0.048 0.500 0.164 NA
#> ERR978131 3 0.7729 0.469 0.028 0.052 0.500 0.160 NA
#> ERR978132 3 0.7729 0.469 0.028 0.052 0.500 0.160 NA
#> ERR978133 3 0.7729 0.469 0.028 0.052 0.500 0.160 NA
#> ERR978134 3 0.7729 0.469 0.028 0.052 0.500 0.160 NA
#> ERR978135 3 0.7729 0.469 0.028 0.052 0.500 0.160 NA
#> ERR978136 3 0.7729 0.469 0.028 0.052 0.500 0.160 NA
#> ERR978137 3 0.7729 0.469 0.028 0.052 0.500 0.160 NA
#> ERR978138 3 0.6147 0.438 0.016 0.028 0.624 0.268 NA
#> ERR978139 3 0.6147 0.438 0.016 0.028 0.624 0.268 NA
#> ERR978140 3 0.6147 0.438 0.016 0.028 0.624 0.268 NA
#> ERR978141 3 0.6147 0.438 0.016 0.028 0.624 0.268 NA
#> ERR978142 3 0.6147 0.438 0.016 0.028 0.624 0.268 NA
#> ERR978143 3 0.6147 0.438 0.016 0.028 0.624 0.268 NA
#> ERR978144 3 0.6147 0.438 0.016 0.028 0.624 0.268 NA
#> ERR978145 3 0.6147 0.438 0.016 0.028 0.624 0.268 NA
#> ERR978146 3 0.6420 0.430 0.016 0.028 0.596 0.280 NA
#> ERR978147 3 0.6420 0.430 0.016 0.028 0.596 0.280 NA
#> ERR978148 3 0.6420 0.430 0.016 0.028 0.596 0.280 NA
#> ERR978149 3 0.6420 0.430 0.016 0.028 0.596 0.280 NA
#> ERR978150 3 0.6420 0.430 0.016 0.028 0.596 0.280 NA
#> ERR978151 3 0.6420 0.430 0.016 0.028 0.596 0.280 NA
#> ERR978152 3 0.6420 0.430 0.016 0.028 0.596 0.280 NA
#> ERR978153 1 0.0000 0.881 1.000 0.000 0.000 0.000 NA
#> ERR978154 1 0.0000 0.881 1.000 0.000 0.000 0.000 NA
#> ERR978155 1 0.0000 0.881 1.000 0.000 0.000 0.000 NA
#> ERR978156 1 0.0000 0.881 1.000 0.000 0.000 0.000 NA
#> ERR978157 1 0.0000 0.881 1.000 0.000 0.000 0.000 NA
#> ERR978158 1 0.0000 0.881 1.000 0.000 0.000 0.000 NA
#> ERR978159 1 0.0000 0.881 1.000 0.000 0.000 0.000 NA
#> ERR978160 1 0.0000 0.881 1.000 0.000 0.000 0.000 NA
#> ERR978161 1 0.0566 0.881 0.984 0.000 0.004 0.012 NA
#> ERR978162 1 0.0566 0.881 0.984 0.000 0.004 0.012 NA
#> ERR978163 1 0.0566 0.881 0.984 0.000 0.004 0.012 NA
#> ERR978164 1 0.0566 0.881 0.984 0.000 0.004 0.012 NA
#> ERR978165 1 0.0566 0.881 0.984 0.000 0.004 0.012 NA
#> ERR978166 1 0.0566 0.881 0.984 0.000 0.004 0.012 NA
#> ERR978167 1 0.0566 0.881 0.984 0.000 0.004 0.012 NA
#> ERR978168 1 0.0566 0.881 0.984 0.000 0.004 0.012 NA
#> ERR978169 4 0.3800 0.770 0.068 0.020 0.060 0.844 NA
#> ERR978170 4 0.3800 0.770 0.068 0.020 0.060 0.844 NA
#> ERR978171 4 0.3800 0.770 0.068 0.020 0.060 0.844 NA
#> ERR978172 4 0.3800 0.770 0.068 0.020 0.060 0.844 NA
#> ERR978173 4 0.3800 0.770 0.068 0.020 0.060 0.844 NA
#> ERR978174 4 0.3800 0.770 0.068 0.020 0.060 0.844 NA
#> ERR978175 4 0.3800 0.770 0.068 0.020 0.060 0.844 NA
#> ERR978176 4 0.4759 0.773 0.068 0.024 0.052 0.800 NA
#> ERR978177 4 0.4759 0.773 0.068 0.024 0.052 0.800 NA
#> ERR978178 4 0.4759 0.773 0.068 0.024 0.052 0.800 NA
#> ERR978179 4 0.4759 0.773 0.068 0.024 0.052 0.800 NA
#> ERR978180 4 0.4759 0.773 0.068 0.024 0.052 0.800 NA
#> ERR978181 4 0.4759 0.773 0.068 0.024 0.052 0.800 NA
#> ERR978182 4 0.4759 0.773 0.068 0.024 0.052 0.800 NA
#> ERR978183 2 0.4604 0.851 0.000 0.748 0.040 0.020 NA
#> ERR978184 2 0.4604 0.851 0.000 0.748 0.040 0.020 NA
#> ERR978185 2 0.4604 0.851 0.000 0.748 0.040 0.020 NA
#> ERR978186 2 0.4604 0.851 0.000 0.748 0.040 0.020 NA
#> ERR978187 2 0.4604 0.851 0.000 0.748 0.040 0.020 NA
#> ERR978188 2 0.4604 0.851 0.000 0.748 0.040 0.020 NA
#> ERR978189 2 0.4658 0.851 0.000 0.748 0.040 0.024 NA
#> ERR978190 2 0.4558 0.851 0.000 0.728 0.040 0.008 NA
#> ERR978191 2 0.4558 0.851 0.000 0.728 0.040 0.008 NA
#> ERR978192 2 0.4558 0.851 0.000 0.728 0.040 0.008 NA
#> ERR978193 2 0.4558 0.851 0.000 0.728 0.040 0.008 NA
#> ERR978194 2 0.4558 0.851 0.000 0.728 0.040 0.008 NA
#> ERR978195 2 0.4558 0.851 0.000 0.728 0.040 0.008 NA
#> ERR978196 2 0.4558 0.851 0.000 0.728 0.040 0.008 NA
#> ERR978197 3 0.5440 0.535 0.000 0.132 0.696 0.016 NA
#> ERR978198 3 0.5440 0.535 0.000 0.132 0.696 0.016 NA
#> ERR978199 3 0.5440 0.535 0.000 0.132 0.696 0.016 NA
#> ERR978200 3 0.5440 0.535 0.000 0.132 0.696 0.016 NA
#> ERR978201 3 0.5440 0.535 0.000 0.132 0.696 0.016 NA
#> ERR978202 3 0.5440 0.535 0.000 0.132 0.696 0.016 NA
#> ERR978203 3 0.5440 0.535 0.000 0.132 0.696 0.016 NA
#> ERR978204 3 0.5384 0.522 0.000 0.156 0.696 0.012 NA
#> ERR978205 3 0.5384 0.522 0.000 0.156 0.696 0.012 NA
#> ERR978206 3 0.5384 0.522 0.000 0.156 0.696 0.012 NA
#> ERR978207 3 0.5384 0.522 0.000 0.156 0.696 0.012 NA
#> ERR978208 3 0.5384 0.522 0.000 0.156 0.696 0.012 NA
#> ERR978209 3 0.5384 0.522 0.000 0.156 0.696 0.012 NA
#> ERR978210 3 0.5384 0.522 0.000 0.156 0.696 0.012 NA
#> ERR978211 3 0.5384 0.522 0.000 0.156 0.696 0.012 NA
#> ERR978212 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978213 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978214 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978215 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978216 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978217 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978218 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978219 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978220 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978221 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978222 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978223 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978224 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978225 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978226 3 0.6594 0.439 0.004 0.176 0.632 0.104 NA
#> ERR978227 1 0.4497 0.859 0.716 0.000 0.008 0.028 NA
#> ERR978228 1 0.4497 0.859 0.716 0.000 0.008 0.028 NA
#> ERR978229 1 0.4497 0.859 0.716 0.000 0.008 0.028 NA
#> ERR978230 1 0.4497 0.859 0.716 0.000 0.008 0.028 NA
#> ERR978231 1 0.4497 0.859 0.716 0.000 0.008 0.028 NA
#> ERR978232 1 0.4497 0.859 0.716 0.000 0.008 0.028 NA
#> ERR978233 1 0.4497 0.859 0.716 0.000 0.008 0.028 NA
#> ERR978234 1 0.3968 0.859 0.716 0.004 0.000 0.004 NA
#> ERR978235 1 0.3968 0.859 0.716 0.004 0.000 0.004 NA
#> ERR978236 1 0.3934 0.859 0.716 0.000 0.000 0.008 NA
#> ERR978237 1 0.3934 0.859 0.716 0.000 0.000 0.008 NA
#> ERR978238 1 0.3934 0.859 0.716 0.000 0.000 0.008 NA
#> ERR978239 1 0.3968 0.859 0.716 0.004 0.000 0.004 NA
#> ERR978240 1 0.3968 0.859 0.716 0.004 0.000 0.004 NA
#> ERR978241 4 0.4874 0.765 0.064 0.008 0.100 0.780 NA
#> ERR978242 4 0.4874 0.765 0.064 0.008 0.100 0.780 NA
#> ERR978243 4 0.4874 0.765 0.064 0.008 0.100 0.780 NA
#> ERR978244 4 0.4874 0.765 0.064 0.008 0.100 0.780 NA
#> ERR978245 4 0.4874 0.765 0.064 0.008 0.100 0.780 NA
#> ERR978246 4 0.4874 0.765 0.064 0.008 0.100 0.780 NA
#> ERR978247 4 0.4874 0.765 0.064 0.008 0.100 0.780 NA
#> ERR978248 4 0.8878 0.316 0.040 0.176 0.324 0.332 NA
#> ERR978249 4 0.8878 0.316 0.040 0.176 0.324 0.332 NA
#> ERR978250 4 0.8878 0.316 0.040 0.176 0.324 0.332 NA
#> ERR978251 4 0.8878 0.316 0.040 0.176 0.324 0.332 NA
#> ERR978252 4 0.8878 0.316 0.040 0.176 0.324 0.332 NA
#> ERR978253 4 0.8878 0.316 0.040 0.176 0.324 0.332 NA
#> ERR978254 4 0.8878 0.316 0.040 0.176 0.324 0.332 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.5762 0.806 0.000 0.580 0.044 0.020 0.044 NA
#> ERR978108 2 0.5762 0.806 0.000 0.580 0.044 0.020 0.044 NA
#> ERR978109 2 0.5762 0.806 0.000 0.580 0.044 0.020 0.044 NA
#> ERR978110 2 0.5762 0.806 0.000 0.580 0.044 0.020 0.044 NA
#> ERR978111 2 0.5762 0.806 0.000 0.580 0.044 0.020 0.044 NA
#> ERR978112 2 0.5762 0.806 0.000 0.580 0.044 0.020 0.044 NA
#> ERR978113 2 0.5762 0.806 0.000 0.580 0.044 0.020 0.044 NA
#> ERR978114 2 0.5762 0.806 0.000 0.580 0.044 0.020 0.044 NA
#> ERR978115 2 0.5306 0.806 0.000 0.576 0.044 0.008 0.024 NA
#> ERR978116 2 0.5306 0.806 0.000 0.576 0.044 0.008 0.024 NA
#> ERR978117 2 0.5306 0.806 0.000 0.576 0.044 0.008 0.024 NA
#> ERR978118 2 0.5306 0.806 0.000 0.576 0.044 0.008 0.024 NA
#> ERR978119 2 0.5306 0.806 0.000 0.576 0.044 0.008 0.024 NA
#> ERR978120 2 0.5306 0.806 0.000 0.576 0.044 0.008 0.024 NA
#> ERR978121 2 0.5306 0.806 0.000 0.576 0.044 0.008 0.024 NA
#> ERR978122 2 0.5306 0.806 0.000 0.576 0.044 0.008 0.024 NA
#> ERR978123 3 0.6516 0.619 0.000 0.004 0.572 0.116 0.160 NA
#> ERR978124 3 0.6516 0.619 0.000 0.004 0.572 0.116 0.160 NA
#> ERR978125 3 0.6516 0.619 0.000 0.004 0.572 0.116 0.160 NA
#> ERR978126 3 0.6516 0.619 0.000 0.004 0.572 0.116 0.160 NA
#> ERR978127 3 0.6516 0.619 0.000 0.004 0.572 0.116 0.160 NA
#> ERR978128 3 0.6516 0.619 0.000 0.004 0.572 0.116 0.160 NA
#> ERR978129 3 0.6516 0.619 0.000 0.004 0.572 0.116 0.160 NA
#> ERR978130 3 0.6516 0.619 0.000 0.004 0.572 0.116 0.160 NA
#> ERR978131 3 0.6531 0.621 0.000 0.004 0.568 0.108 0.160 NA
#> ERR978132 3 0.6531 0.621 0.000 0.004 0.568 0.108 0.160 NA
#> ERR978133 3 0.6531 0.621 0.000 0.004 0.568 0.108 0.160 NA
#> ERR978134 3 0.6531 0.621 0.000 0.004 0.568 0.108 0.160 NA
#> ERR978135 3 0.6531 0.621 0.000 0.004 0.568 0.108 0.160 NA
#> ERR978136 3 0.6531 0.621 0.000 0.004 0.568 0.108 0.160 NA
#> ERR978137 3 0.6531 0.621 0.000 0.004 0.568 0.108 0.160 NA
#> ERR978138 5 0.7314 0.536 0.000 0.024 0.264 0.196 0.444 NA
#> ERR978139 5 0.7314 0.536 0.000 0.024 0.264 0.196 0.444 NA
#> ERR978140 5 0.7314 0.536 0.000 0.024 0.264 0.196 0.444 NA
#> ERR978141 5 0.7314 0.536 0.000 0.024 0.264 0.196 0.444 NA
#> ERR978142 5 0.7314 0.536 0.000 0.024 0.264 0.196 0.444 NA
#> ERR978143 5 0.7314 0.536 0.000 0.024 0.264 0.196 0.444 NA
#> ERR978144 5 0.7314 0.536 0.000 0.024 0.264 0.196 0.444 NA
#> ERR978145 5 0.7314 0.536 0.000 0.024 0.264 0.196 0.444 NA
#> ERR978146 5 0.7478 0.500 0.000 0.020 0.276 0.208 0.408 NA
#> ERR978147 5 0.7478 0.500 0.000 0.020 0.276 0.208 0.408 NA
#> ERR978148 5 0.7478 0.500 0.000 0.020 0.276 0.208 0.408 NA
#> ERR978149 5 0.7478 0.500 0.000 0.020 0.276 0.208 0.408 NA
#> ERR978150 5 0.7478 0.500 0.000 0.020 0.276 0.208 0.408 NA
#> ERR978151 5 0.7478 0.500 0.000 0.020 0.276 0.208 0.408 NA
#> ERR978152 5 0.7478 0.500 0.000 0.020 0.276 0.208 0.408 NA
#> ERR978153 1 0.0777 0.849 0.972 0.000 0.000 0.004 0.024 NA
#> ERR978154 1 0.0777 0.849 0.972 0.000 0.000 0.004 0.024 NA
#> ERR978155 1 0.0777 0.849 0.972 0.000 0.000 0.004 0.024 NA
#> ERR978156 1 0.0777 0.849 0.972 0.000 0.000 0.004 0.024 NA
#> ERR978157 1 0.0777 0.849 0.972 0.000 0.000 0.004 0.024 NA
#> ERR978158 1 0.0777 0.849 0.972 0.000 0.000 0.004 0.024 NA
#> ERR978159 1 0.0777 0.849 0.972 0.000 0.000 0.004 0.024 NA
#> ERR978160 1 0.0777 0.849 0.972 0.000 0.000 0.004 0.024 NA
#> ERR978161 1 0.0146 0.849 0.996 0.004 0.000 0.000 0.000 NA
#> ERR978162 1 0.0146 0.849 0.996 0.004 0.000 0.000 0.000 NA
#> ERR978163 1 0.0146 0.849 0.996 0.000 0.000 0.004 0.000 NA
#> ERR978164 1 0.0146 0.849 0.996 0.000 0.000 0.004 0.000 NA
#> ERR978165 1 0.0146 0.849 0.996 0.000 0.000 0.004 0.000 NA
#> ERR978166 1 0.0146 0.849 0.996 0.000 0.000 0.004 0.000 NA
#> ERR978167 1 0.0146 0.849 0.996 0.004 0.000 0.000 0.000 NA
#> ERR978168 1 0.0146 0.849 0.996 0.004 0.000 0.000 0.000 NA
#> ERR978169 4 0.1880 0.773 0.032 0.004 0.020 0.932 0.004 NA
#> ERR978170 4 0.1880 0.773 0.032 0.004 0.020 0.932 0.004 NA
#> ERR978171 4 0.1880 0.773 0.032 0.004 0.020 0.932 0.004 NA
#> ERR978172 4 0.1880 0.773 0.032 0.004 0.020 0.932 0.004 NA
#> ERR978173 4 0.1880 0.773 0.032 0.004 0.020 0.932 0.004 NA
#> ERR978174 4 0.1880 0.773 0.032 0.004 0.020 0.932 0.004 NA
#> ERR978175 4 0.1880 0.773 0.032 0.004 0.020 0.932 0.004 NA
#> ERR978176 4 0.3273 0.773 0.032 0.004 0.012 0.864 0.044 NA
#> ERR978177 4 0.3273 0.773 0.032 0.004 0.012 0.864 0.044 NA
#> ERR978178 4 0.3273 0.773 0.032 0.004 0.012 0.864 0.044 NA
#> ERR978179 4 0.3273 0.773 0.032 0.004 0.012 0.864 0.044 NA
#> ERR978180 4 0.3273 0.773 0.032 0.004 0.012 0.864 0.044 NA
#> ERR978181 4 0.3273 0.773 0.032 0.004 0.012 0.864 0.044 NA
#> ERR978182 4 0.3273 0.773 0.032 0.004 0.012 0.864 0.044 NA
#> ERR978183 2 0.0972 0.780 0.000 0.964 0.008 0.000 0.028 NA
#> ERR978184 2 0.0972 0.780 0.000 0.964 0.008 0.000 0.028 NA
#> ERR978185 2 0.0972 0.780 0.000 0.964 0.008 0.000 0.028 NA
#> ERR978186 2 0.0972 0.780 0.000 0.964 0.008 0.000 0.028 NA
#> ERR978187 2 0.0972 0.780 0.000 0.964 0.008 0.000 0.028 NA
#> ERR978188 2 0.0972 0.780 0.000 0.964 0.008 0.000 0.028 NA
#> ERR978189 2 0.1049 0.780 0.000 0.960 0.008 0.000 0.032 NA
#> ERR978190 2 0.0520 0.780 0.000 0.984 0.008 0.000 0.000 NA
#> ERR978191 2 0.0520 0.780 0.000 0.984 0.008 0.000 0.000 NA
#> ERR978192 2 0.0520 0.780 0.000 0.984 0.008 0.000 0.000 NA
#> ERR978193 2 0.0520 0.780 0.000 0.984 0.008 0.000 0.000 NA
#> ERR978194 2 0.0520 0.780 0.000 0.984 0.008 0.000 0.000 NA
#> ERR978195 2 0.0520 0.780 0.000 0.984 0.008 0.000 0.000 NA
#> ERR978196 2 0.0520 0.780 0.000 0.984 0.008 0.000 0.000 NA
#> ERR978197 3 0.1477 0.586 0.000 0.048 0.940 0.008 0.004 NA
#> ERR978198 3 0.1477 0.586 0.000 0.048 0.940 0.008 0.004 NA
#> ERR978199 3 0.1477 0.586 0.000 0.048 0.940 0.008 0.004 NA
#> ERR978200 3 0.1477 0.586 0.000 0.048 0.940 0.008 0.004 NA
#> ERR978201 3 0.1477 0.586 0.000 0.048 0.940 0.008 0.004 NA
#> ERR978202 3 0.1477 0.586 0.000 0.048 0.940 0.008 0.004 NA
#> ERR978203 3 0.1477 0.586 0.000 0.048 0.940 0.008 0.004 NA
#> ERR978204 3 0.2649 0.541 0.000 0.072 0.880 0.000 0.036 NA
#> ERR978205 3 0.2649 0.541 0.000 0.072 0.880 0.000 0.036 NA
#> ERR978206 3 0.2649 0.541 0.000 0.072 0.880 0.000 0.036 NA
#> ERR978207 3 0.2649 0.541 0.000 0.072 0.880 0.000 0.036 NA
#> ERR978208 3 0.2649 0.541 0.000 0.072 0.880 0.000 0.036 NA
#> ERR978209 3 0.2649 0.541 0.000 0.072 0.880 0.000 0.036 NA
#> ERR978210 3 0.2649 0.541 0.000 0.072 0.880 0.000 0.036 NA
#> ERR978211 3 0.2649 0.541 0.000 0.072 0.880 0.000 0.036 NA
#> ERR978212 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978213 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978214 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978215 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978216 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978217 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978218 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978219 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978220 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978221 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978222 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978223 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978224 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978225 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978226 5 0.6657 0.581 0.000 0.116 0.372 0.056 0.444 NA
#> ERR978227 1 0.4841 0.823 0.648 0.000 0.004 0.000 0.088 NA
#> ERR978228 1 0.4841 0.823 0.648 0.000 0.004 0.000 0.088 NA
#> ERR978229 1 0.4841 0.823 0.648 0.000 0.004 0.000 0.088 NA
#> ERR978230 1 0.4841 0.823 0.648 0.000 0.004 0.000 0.088 NA
#> ERR978231 1 0.4841 0.823 0.648 0.000 0.004 0.000 0.088 NA
#> ERR978232 1 0.4841 0.823 0.648 0.000 0.004 0.000 0.088 NA
#> ERR978233 1 0.4841 0.823 0.648 0.000 0.004 0.000 0.088 NA
#> ERR978234 1 0.4269 0.823 0.648 0.000 0.000 0.000 0.036 NA
#> ERR978235 1 0.4269 0.823 0.648 0.000 0.000 0.000 0.036 NA
#> ERR978236 1 0.4269 0.823 0.648 0.000 0.000 0.000 0.036 NA
#> ERR978237 1 0.4269 0.823 0.648 0.000 0.000 0.000 0.036 NA
#> ERR978238 1 0.4269 0.823 0.648 0.000 0.000 0.000 0.036 NA
#> ERR978239 1 0.4269 0.823 0.648 0.000 0.000 0.000 0.036 NA
#> ERR978240 1 0.4388 0.823 0.648 0.000 0.000 0.004 0.036 NA
#> ERR978241 4 0.4470 0.761 0.032 0.008 0.036 0.796 0.056 NA
#> ERR978242 4 0.4470 0.761 0.032 0.008 0.036 0.796 0.056 NA
#> ERR978243 4 0.4470 0.761 0.032 0.008 0.036 0.796 0.056 NA
#> ERR978244 4 0.4470 0.761 0.032 0.008 0.036 0.796 0.056 NA
#> ERR978245 4 0.4470 0.761 0.032 0.008 0.036 0.796 0.056 NA
#> ERR978246 4 0.4470 0.761 0.032 0.008 0.036 0.796 0.056 NA
#> ERR978247 4 0.4470 0.761 0.032 0.008 0.036 0.796 0.056 NA
#> ERR978248 4 0.8566 0.369 0.020 0.120 0.124 0.340 0.308 NA
#> ERR978249 4 0.8538 0.370 0.020 0.120 0.124 0.340 0.312 NA
#> ERR978250 4 0.8538 0.370 0.020 0.120 0.124 0.340 0.312 NA
#> ERR978251 4 0.8538 0.370 0.020 0.120 0.124 0.340 0.312 NA
#> ERR978252 4 0.8538 0.370 0.020 0.120 0.124 0.340 0.312 NA
#> ERR978253 4 0.8538 0.370 0.020 0.120 0.124 0.340 0.312 NA
#> ERR978254 4 0.8566 0.369 0.020 0.120 0.124 0.340 0.308 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", "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 14049 rows and 148 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 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 0.640 0.738 0.900 0.4902 0.502 0.502
#> 3 3 0.701 0.867 0.904 0.3133 0.759 0.563
#> 4 4 0.724 0.659 0.804 0.1407 0.726 0.383
#> 5 5 0.865 0.864 0.862 0.0941 0.899 0.634
#> 6 6 0.901 0.902 0.869 0.0274 0.979 0.894
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
#> ERR978107 2 0.0000 0.873 0.000 1.000
#> ERR978108 2 0.0000 0.873 0.000 1.000
#> ERR978109 2 0.0000 0.873 0.000 1.000
#> ERR978110 2 0.0000 0.873 0.000 1.000
#> ERR978111 2 0.0000 0.873 0.000 1.000
#> ERR978112 2 0.0000 0.873 0.000 1.000
#> ERR978113 2 0.0000 0.873 0.000 1.000
#> ERR978114 2 0.0000 0.873 0.000 1.000
#> ERR978115 2 0.0000 0.873 0.000 1.000
#> ERR978116 2 0.0000 0.873 0.000 1.000
#> ERR978117 2 0.0000 0.873 0.000 1.000
#> ERR978118 2 0.0000 0.873 0.000 1.000
#> ERR978119 2 0.0000 0.873 0.000 1.000
#> ERR978120 2 0.0000 0.873 0.000 1.000
#> ERR978121 2 0.0000 0.873 0.000 1.000
#> ERR978122 2 0.0000 0.873 0.000 1.000
#> ERR978123 1 0.9896 0.265 0.560 0.440
#> ERR978124 1 0.9896 0.265 0.560 0.440
#> ERR978125 1 0.9896 0.265 0.560 0.440
#> ERR978126 1 0.9896 0.265 0.560 0.440
#> ERR978127 1 0.9896 0.265 0.560 0.440
#> ERR978128 1 0.9896 0.265 0.560 0.440
#> ERR978129 1 0.9896 0.265 0.560 0.440
#> ERR978130 1 0.9896 0.265 0.560 0.440
#> ERR978131 1 0.9896 0.265 0.560 0.440
#> ERR978132 1 0.9896 0.265 0.560 0.440
#> ERR978133 1 0.9896 0.265 0.560 0.440
#> ERR978134 1 0.9896 0.265 0.560 0.440
#> ERR978135 1 0.9896 0.265 0.560 0.440
#> ERR978136 1 0.9896 0.265 0.560 0.440
#> ERR978137 1 0.9896 0.265 0.560 0.440
#> ERR978138 2 0.9248 0.453 0.340 0.660
#> ERR978139 2 0.9248 0.453 0.340 0.660
#> ERR978140 2 0.9248 0.453 0.340 0.660
#> ERR978141 2 0.9248 0.453 0.340 0.660
#> ERR978142 2 0.9248 0.453 0.340 0.660
#> ERR978143 2 0.9248 0.453 0.340 0.660
#> ERR978144 2 0.9248 0.453 0.340 0.660
#> ERR978145 2 0.9248 0.453 0.340 0.660
#> ERR978146 2 0.9248 0.453 0.340 0.660
#> ERR978147 2 0.9248 0.453 0.340 0.660
#> ERR978148 2 0.9248 0.453 0.340 0.660
#> ERR978149 2 0.9248 0.453 0.340 0.660
#> ERR978150 2 0.9248 0.453 0.340 0.660
#> ERR978151 2 0.9248 0.453 0.340 0.660
#> ERR978152 2 0.9248 0.453 0.340 0.660
#> ERR978153 1 0.0000 0.871 1.000 0.000
#> ERR978154 1 0.0000 0.871 1.000 0.000
#> ERR978155 1 0.0000 0.871 1.000 0.000
#> ERR978156 1 0.0000 0.871 1.000 0.000
#> ERR978157 1 0.0000 0.871 1.000 0.000
#> ERR978158 1 0.0000 0.871 1.000 0.000
#> ERR978159 1 0.0000 0.871 1.000 0.000
#> ERR978160 1 0.0000 0.871 1.000 0.000
#> ERR978161 1 0.0000 0.871 1.000 0.000
#> ERR978162 1 0.0000 0.871 1.000 0.000
#> ERR978163 1 0.0000 0.871 1.000 0.000
#> ERR978164 1 0.0000 0.871 1.000 0.000
#> ERR978165 1 0.0000 0.871 1.000 0.000
#> ERR978166 1 0.0000 0.871 1.000 0.000
#> ERR978167 1 0.0000 0.871 1.000 0.000
#> ERR978168 1 0.0000 0.871 1.000 0.000
#> ERR978169 1 0.0376 0.871 0.996 0.004
#> ERR978170 1 0.0376 0.871 0.996 0.004
#> ERR978171 1 0.0376 0.871 0.996 0.004
#> ERR978172 1 0.0376 0.871 0.996 0.004
#> ERR978173 1 0.0376 0.871 0.996 0.004
#> ERR978174 1 0.0376 0.871 0.996 0.004
#> ERR978175 1 0.0376 0.871 0.996 0.004
#> ERR978176 1 0.0376 0.871 0.996 0.004
#> ERR978177 1 0.0376 0.871 0.996 0.004
#> ERR978178 1 0.0376 0.871 0.996 0.004
#> ERR978179 1 0.0376 0.871 0.996 0.004
#> ERR978180 1 0.0376 0.871 0.996 0.004
#> ERR978181 1 0.0376 0.871 0.996 0.004
#> ERR978182 1 0.0376 0.871 0.996 0.004
#> ERR978183 2 0.0000 0.873 0.000 1.000
#> ERR978184 2 0.0000 0.873 0.000 1.000
#> ERR978185 2 0.0000 0.873 0.000 1.000
#> ERR978186 2 0.0000 0.873 0.000 1.000
#> ERR978187 2 0.0000 0.873 0.000 1.000
#> ERR978188 2 0.0000 0.873 0.000 1.000
#> ERR978189 2 0.0000 0.873 0.000 1.000
#> ERR978190 2 0.0000 0.873 0.000 1.000
#> ERR978191 2 0.0000 0.873 0.000 1.000
#> ERR978192 2 0.0000 0.873 0.000 1.000
#> ERR978193 2 0.0000 0.873 0.000 1.000
#> ERR978194 2 0.0000 0.873 0.000 1.000
#> ERR978195 2 0.0000 0.873 0.000 1.000
#> ERR978196 2 0.0000 0.873 0.000 1.000
#> ERR978197 2 0.0000 0.873 0.000 1.000
#> ERR978198 2 0.0000 0.873 0.000 1.000
#> ERR978199 2 0.0000 0.873 0.000 1.000
#> ERR978200 2 0.0000 0.873 0.000 1.000
#> ERR978201 2 0.0000 0.873 0.000 1.000
#> ERR978202 2 0.0000 0.873 0.000 1.000
#> ERR978203 2 0.0000 0.873 0.000 1.000
#> ERR978204 2 0.0000 0.873 0.000 1.000
#> ERR978205 2 0.0000 0.873 0.000 1.000
#> ERR978206 2 0.0000 0.873 0.000 1.000
#> ERR978207 2 0.0000 0.873 0.000 1.000
#> ERR978208 2 0.0000 0.873 0.000 1.000
#> ERR978209 2 0.0000 0.873 0.000 1.000
#> ERR978210 2 0.0000 0.873 0.000 1.000
#> ERR978211 2 0.0000 0.873 0.000 1.000
#> ERR978212 2 0.0000 0.873 0.000 1.000
#> ERR978213 2 0.0000 0.873 0.000 1.000
#> ERR978214 2 0.0000 0.873 0.000 1.000
#> ERR978215 2 0.0000 0.873 0.000 1.000
#> ERR978216 2 0.0000 0.873 0.000 1.000
#> ERR978217 2 0.0000 0.873 0.000 1.000
#> ERR978218 2 0.0000 0.873 0.000 1.000
#> ERR978219 2 0.0000 0.873 0.000 1.000
#> ERR978220 2 0.0000 0.873 0.000 1.000
#> ERR978221 2 0.0000 0.873 0.000 1.000
#> ERR978222 2 0.0000 0.873 0.000 1.000
#> ERR978223 2 0.0000 0.873 0.000 1.000
#> ERR978224 2 0.0000 0.873 0.000 1.000
#> ERR978225 2 0.0000 0.873 0.000 1.000
#> ERR978226 2 0.0000 0.873 0.000 1.000
#> ERR978227 1 0.0000 0.871 1.000 0.000
#> ERR978228 1 0.0000 0.871 1.000 0.000
#> ERR978229 1 0.0000 0.871 1.000 0.000
#> ERR978230 1 0.0000 0.871 1.000 0.000
#> ERR978231 1 0.0000 0.871 1.000 0.000
#> ERR978232 1 0.0000 0.871 1.000 0.000
#> ERR978233 1 0.0000 0.871 1.000 0.000
#> ERR978234 1 0.0000 0.871 1.000 0.000
#> ERR978235 1 0.0000 0.871 1.000 0.000
#> ERR978236 1 0.0000 0.871 1.000 0.000
#> ERR978237 1 0.0000 0.871 1.000 0.000
#> ERR978238 1 0.0000 0.871 1.000 0.000
#> ERR978239 1 0.0000 0.871 1.000 0.000
#> ERR978240 1 0.0000 0.871 1.000 0.000
#> ERR978241 1 0.0376 0.871 0.996 0.004
#> ERR978242 1 0.0376 0.871 0.996 0.004
#> ERR978243 1 0.0376 0.871 0.996 0.004
#> ERR978244 1 0.0376 0.871 0.996 0.004
#> ERR978245 1 0.0376 0.871 0.996 0.004
#> ERR978246 1 0.0376 0.871 0.996 0.004
#> ERR978247 1 0.0376 0.871 0.996 0.004
#> ERR978248 2 0.9881 0.224 0.436 0.564
#> ERR978249 2 0.9881 0.224 0.436 0.564
#> ERR978250 2 0.9881 0.224 0.436 0.564
#> ERR978251 2 0.9881 0.224 0.436 0.564
#> ERR978252 2 0.9881 0.224 0.436 0.564
#> ERR978253 2 0.9881 0.224 0.436 0.564
#> ERR978254 2 0.9881 0.224 0.436 0.564
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978108 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978109 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978110 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978111 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978112 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978113 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978114 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978115 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978116 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978117 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978118 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978119 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978120 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978121 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978122 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978123 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978124 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978125 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978126 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978127 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978128 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978129 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978130 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978131 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978132 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978133 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978134 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978135 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978136 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978137 3 0.0829 0.861 0.004 0.012 0.984
#> ERR978138 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978139 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978140 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978141 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978142 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978143 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978144 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978145 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978146 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978147 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978148 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978149 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978150 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978151 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978152 3 0.0000 0.863 0.000 0.000 1.000
#> ERR978153 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978169 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978170 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978171 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978172 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978173 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978174 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978175 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978176 3 0.6746 0.775 0.192 0.076 0.732
#> ERR978177 3 0.6746 0.775 0.192 0.076 0.732
#> ERR978178 3 0.6746 0.775 0.192 0.076 0.732
#> ERR978179 3 0.6746 0.775 0.192 0.076 0.732
#> ERR978180 3 0.6746 0.775 0.192 0.076 0.732
#> ERR978181 3 0.6746 0.775 0.192 0.076 0.732
#> ERR978182 3 0.6746 0.775 0.192 0.076 0.732
#> ERR978183 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978184 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978185 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978186 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978187 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978188 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978189 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978190 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978191 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978192 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978193 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978194 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978195 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978196 2 0.0000 0.866 0.000 1.000 0.000
#> ERR978197 2 0.5465 0.792 0.000 0.712 0.288
#> ERR978198 2 0.5465 0.792 0.000 0.712 0.288
#> ERR978199 2 0.5465 0.792 0.000 0.712 0.288
#> ERR978200 2 0.5465 0.792 0.000 0.712 0.288
#> ERR978201 2 0.5465 0.792 0.000 0.712 0.288
#> ERR978202 2 0.5465 0.792 0.000 0.712 0.288
#> ERR978203 2 0.5465 0.792 0.000 0.712 0.288
#> ERR978204 2 0.5397 0.799 0.000 0.720 0.280
#> ERR978205 2 0.5397 0.799 0.000 0.720 0.280
#> ERR978206 2 0.5397 0.799 0.000 0.720 0.280
#> ERR978207 2 0.5397 0.799 0.000 0.720 0.280
#> ERR978208 2 0.5397 0.799 0.000 0.720 0.280
#> ERR978209 2 0.5397 0.799 0.000 0.720 0.280
#> ERR978210 2 0.5397 0.799 0.000 0.720 0.280
#> ERR978211 2 0.5397 0.799 0.000 0.720 0.280
#> ERR978212 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978213 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978214 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978215 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978216 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978217 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978218 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978219 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978220 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978221 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978222 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978223 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978224 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978225 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978226 2 0.5138 0.822 0.000 0.748 0.252
#> ERR978227 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1.000 0.000 0.000
#> ERR978241 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978242 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978243 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978244 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978245 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978246 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978247 3 0.6662 0.779 0.192 0.072 0.736
#> ERR978248 2 0.2297 0.842 0.036 0.944 0.020
#> ERR978249 2 0.2297 0.842 0.036 0.944 0.020
#> ERR978250 2 0.2297 0.842 0.036 0.944 0.020
#> ERR978251 2 0.2297 0.842 0.036 0.944 0.020
#> ERR978252 2 0.2297 0.842 0.036 0.944 0.020
#> ERR978253 2 0.2297 0.842 0.036 0.944 0.020
#> ERR978254 2 0.2297 0.842 0.036 0.944 0.020
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978123 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978124 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978125 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978126 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978127 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978128 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978129 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978130 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978131 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978132 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978133 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978134 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978135 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978136 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978137 3 0.130 0.673 0.000 0.000 0.956 0.044
#> ERR978138 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978139 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978140 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978141 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978142 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978143 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978144 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978145 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978146 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978147 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978148 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978149 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978150 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978151 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978152 3 0.307 0.634 0.000 0.000 0.848 0.152
#> ERR978153 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978169 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978170 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978171 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978172 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978173 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978174 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978175 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978176 4 0.488 0.484 0.016 0.000 0.288 0.696
#> ERR978177 4 0.488 0.484 0.016 0.000 0.288 0.696
#> ERR978178 4 0.488 0.484 0.016 0.000 0.288 0.696
#> ERR978179 4 0.488 0.484 0.016 0.000 0.288 0.696
#> ERR978180 4 0.488 0.484 0.016 0.000 0.288 0.696
#> ERR978181 4 0.488 0.484 0.016 0.000 0.288 0.696
#> ERR978182 4 0.488 0.484 0.016 0.000 0.288 0.696
#> ERR978183 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0.000 1.000 0.000 0.000
#> ERR978197 3 0.601 0.493 0.000 0.072 0.640 0.288
#> ERR978198 3 0.601 0.493 0.000 0.072 0.640 0.288
#> ERR978199 3 0.601 0.493 0.000 0.072 0.640 0.288
#> ERR978200 3 0.601 0.493 0.000 0.072 0.640 0.288
#> ERR978201 3 0.601 0.493 0.000 0.072 0.640 0.288
#> ERR978202 3 0.601 0.493 0.000 0.072 0.640 0.288
#> ERR978203 3 0.601 0.493 0.000 0.072 0.640 0.288
#> ERR978204 3 0.631 0.475 0.000 0.092 0.620 0.288
#> ERR978205 3 0.631 0.475 0.000 0.092 0.620 0.288
#> ERR978206 3 0.631 0.475 0.000 0.092 0.620 0.288
#> ERR978207 3 0.631 0.475 0.000 0.092 0.620 0.288
#> ERR978208 3 0.631 0.475 0.000 0.092 0.620 0.288
#> ERR978209 3 0.631 0.475 0.000 0.092 0.620 0.288
#> ERR978210 3 0.631 0.475 0.000 0.092 0.620 0.288
#> ERR978211 3 0.631 0.475 0.000 0.092 0.620 0.288
#> ERR978212 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978213 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978214 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978215 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978216 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978217 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978218 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978219 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978220 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978221 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978222 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978223 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978224 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978225 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978226 4 0.767 -0.147 0.000 0.212 0.392 0.396
#> ERR978227 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1.000 0.000 0.000 0.000
#> ERR978241 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978242 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978243 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978244 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978245 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978246 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978247 4 0.504 0.485 0.020 0.000 0.296 0.684
#> ERR978248 4 0.391 0.384 0.004 0.212 0.000 0.784
#> ERR978249 4 0.391 0.384 0.004 0.212 0.000 0.784
#> ERR978250 4 0.391 0.384 0.004 0.212 0.000 0.784
#> ERR978251 4 0.391 0.384 0.004 0.212 0.000 0.784
#> ERR978252 4 0.391 0.384 0.004 0.212 0.000 0.784
#> ERR978253 4 0.391 0.384 0.004 0.212 0.000 0.784
#> ERR978254 4 0.391 0.384 0.004 0.212 0.000 0.784
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978123 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978124 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978125 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978126 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978127 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978128 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978129 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978130 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978131 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978132 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978133 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978134 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978135 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978136 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978137 3 0.4268 0.724 0 0.000 0.648 0.008 0.344
#> ERR978138 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978139 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978140 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978141 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978142 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978143 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978144 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978145 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978146 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978147 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978148 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978149 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978150 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978151 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978152 5 0.2722 0.674 0 0.000 0.020 0.108 0.872
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978170 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978171 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978172 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978173 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978174 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978175 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978176 4 0.0324 0.954 0 0.000 0.004 0.992 0.004
#> ERR978177 4 0.0324 0.954 0 0.000 0.004 0.992 0.004
#> ERR978178 4 0.0324 0.954 0 0.000 0.004 0.992 0.004
#> ERR978179 4 0.0324 0.954 0 0.000 0.004 0.992 0.004
#> ERR978180 4 0.0324 0.954 0 0.000 0.004 0.992 0.004
#> ERR978181 4 0.0324 0.954 0 0.000 0.004 0.992 0.004
#> ERR978182 4 0.0324 0.954 0 0.000 0.004 0.992 0.004
#> ERR978183 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978184 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978185 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978186 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978187 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978188 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978189 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978190 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978191 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978192 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978193 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978194 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978195 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978196 2 0.0000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978197 3 0.0912 0.715 0 0.012 0.972 0.000 0.016
#> ERR978198 3 0.0912 0.715 0 0.012 0.972 0.000 0.016
#> ERR978199 3 0.0912 0.715 0 0.012 0.972 0.000 0.016
#> ERR978200 3 0.0912 0.715 0 0.012 0.972 0.000 0.016
#> ERR978201 3 0.0912 0.715 0 0.012 0.972 0.000 0.016
#> ERR978202 3 0.0912 0.715 0 0.012 0.972 0.000 0.016
#> ERR978203 3 0.0912 0.715 0 0.012 0.972 0.000 0.016
#> ERR978204 3 0.1281 0.706 0 0.012 0.956 0.000 0.032
#> ERR978205 3 0.1281 0.706 0 0.012 0.956 0.000 0.032
#> ERR978206 3 0.1281 0.706 0 0.012 0.956 0.000 0.032
#> ERR978207 3 0.1281 0.706 0 0.012 0.956 0.000 0.032
#> ERR978208 3 0.1281 0.706 0 0.012 0.956 0.000 0.032
#> ERR978209 3 0.1281 0.706 0 0.012 0.956 0.000 0.032
#> ERR978210 3 0.1281 0.706 0 0.012 0.956 0.000 0.032
#> ERR978211 3 0.1281 0.706 0 0.012 0.956 0.000 0.032
#> ERR978212 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978213 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978214 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978215 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978216 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978217 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978218 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978219 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978220 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978221 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978222 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978223 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978224 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978225 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978226 5 0.4387 0.676 0 0.012 0.348 0.000 0.640
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978241 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978242 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978243 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978244 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978245 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978246 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978247 4 0.0000 0.956 0 0.000 0.000 1.000 0.000
#> ERR978248 4 0.3579 0.867 0 0.032 0.016 0.836 0.116
#> ERR978249 4 0.3579 0.867 0 0.032 0.016 0.836 0.116
#> ERR978250 4 0.3579 0.867 0 0.032 0.016 0.836 0.116
#> ERR978251 4 0.3579 0.867 0 0.032 0.016 0.836 0.116
#> ERR978252 4 0.3579 0.867 0 0.032 0.016 0.836 0.116
#> ERR978253 4 0.3579 0.867 0 0.032 0.016 0.836 0.116
#> ERR978254 4 0.3579 0.867 0 0.032 0.016 0.836 0.116
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 0.988 0.000 1.000 0.000 0.000 0.000 0.000
#> ERR978123 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978124 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978125 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978126 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978127 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978128 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978129 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978130 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978131 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978132 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978133 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978134 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978135 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978136 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978137 3 0.3244 0.702 0.000 0.000 0.732 0.000 0.268 0.000
#> ERR978138 5 0.5272 0.996 0.000 0.000 0.052 0.028 0.564 0.356
#> ERR978139 5 0.5272 0.996 0.000 0.000 0.052 0.028 0.564 0.356
#> ERR978140 5 0.5272 0.996 0.000 0.000 0.052 0.028 0.564 0.356
#> ERR978141 5 0.5272 0.996 0.000 0.000 0.052 0.028 0.564 0.356
#> ERR978142 5 0.5272 0.996 0.000 0.000 0.052 0.028 0.564 0.356
#> ERR978143 5 0.5272 0.996 0.000 0.000 0.052 0.028 0.564 0.356
#> ERR978144 5 0.5272 0.996 0.000 0.000 0.052 0.028 0.564 0.356
#> ERR978145 5 0.5272 0.996 0.000 0.000 0.052 0.028 0.564 0.356
#> ERR978146 5 0.5261 0.996 0.000 0.000 0.052 0.028 0.568 0.352
#> ERR978147 5 0.5261 0.996 0.000 0.000 0.052 0.028 0.568 0.352
#> ERR978148 5 0.5261 0.996 0.000 0.000 0.052 0.028 0.568 0.352
#> ERR978149 5 0.5261 0.996 0.000 0.000 0.052 0.028 0.568 0.352
#> ERR978150 5 0.5261 0.996 0.000 0.000 0.052 0.028 0.568 0.352
#> ERR978151 5 0.5261 0.996 0.000 0.000 0.052 0.028 0.568 0.352
#> ERR978152 5 0.5261 0.996 0.000 0.000 0.052 0.028 0.568 0.352
#> ERR978153 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0260 0.902 0.000 0.000 0.000 0.992 0.008 0.000
#> ERR978170 4 0.0260 0.902 0.000 0.000 0.000 0.992 0.008 0.000
#> ERR978171 4 0.0260 0.902 0.000 0.000 0.000 0.992 0.008 0.000
#> ERR978172 4 0.0260 0.902 0.000 0.000 0.000 0.992 0.008 0.000
#> ERR978173 4 0.0260 0.902 0.000 0.000 0.000 0.992 0.008 0.000
#> ERR978174 4 0.0260 0.902 0.000 0.000 0.000 0.992 0.008 0.000
#> ERR978175 4 0.0260 0.902 0.000 0.000 0.000 0.992 0.008 0.000
#> ERR978176 4 0.1663 0.898 0.000 0.000 0.000 0.912 0.088 0.000
#> ERR978177 4 0.1663 0.898 0.000 0.000 0.000 0.912 0.088 0.000
#> ERR978178 4 0.1663 0.898 0.000 0.000 0.000 0.912 0.088 0.000
#> ERR978179 4 0.1663 0.898 0.000 0.000 0.000 0.912 0.088 0.000
#> ERR978180 4 0.1663 0.898 0.000 0.000 0.000 0.912 0.088 0.000
#> ERR978181 4 0.1663 0.898 0.000 0.000 0.000 0.912 0.088 0.000
#> ERR978182 4 0.1663 0.898 0.000 0.000 0.000 0.912 0.088 0.000
#> ERR978183 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978184 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978185 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978186 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978187 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978188 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978189 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978190 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978191 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978192 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978193 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978194 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978195 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978196 2 0.0914 0.986 0.000 0.968 0.016 0.000 0.016 0.000
#> ERR978197 3 0.3050 0.649 0.000 0.000 0.764 0.000 0.000 0.236
#> ERR978198 3 0.3050 0.649 0.000 0.000 0.764 0.000 0.000 0.236
#> ERR978199 3 0.3050 0.649 0.000 0.000 0.764 0.000 0.000 0.236
#> ERR978200 3 0.3050 0.649 0.000 0.000 0.764 0.000 0.000 0.236
#> ERR978201 3 0.3050 0.649 0.000 0.000 0.764 0.000 0.000 0.236
#> ERR978202 3 0.3050 0.649 0.000 0.000 0.764 0.000 0.000 0.236
#> ERR978203 3 0.3050 0.649 0.000 0.000 0.764 0.000 0.000 0.236
#> ERR978204 3 0.3482 0.586 0.000 0.000 0.684 0.000 0.000 0.316
#> ERR978205 3 0.3482 0.586 0.000 0.000 0.684 0.000 0.000 0.316
#> ERR978206 3 0.3482 0.586 0.000 0.000 0.684 0.000 0.000 0.316
#> ERR978207 3 0.3482 0.586 0.000 0.000 0.684 0.000 0.000 0.316
#> ERR978208 3 0.3482 0.586 0.000 0.000 0.684 0.000 0.000 0.316
#> ERR978209 3 0.3482 0.586 0.000 0.000 0.684 0.000 0.000 0.316
#> ERR978210 3 0.3482 0.586 0.000 0.000 0.684 0.000 0.000 0.316
#> ERR978211 3 0.3482 0.586 0.000 0.000 0.684 0.000 0.000 0.316
#> ERR978212 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978213 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978214 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978215 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978216 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978217 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978218 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978219 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978220 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978221 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978222 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978223 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978224 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978225 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978226 6 0.0000 1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> ERR978227 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978228 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978229 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978230 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978231 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978232 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978233 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978234 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978235 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978236 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978237 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978238 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978239 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978240 1 0.0458 0.993 0.984 0.000 0.000 0.000 0.016 0.000
#> ERR978241 4 0.0790 0.902 0.000 0.000 0.000 0.968 0.032 0.000
#> ERR978242 4 0.0790 0.902 0.000 0.000 0.000 0.968 0.032 0.000
#> ERR978243 4 0.0790 0.902 0.000 0.000 0.000 0.968 0.032 0.000
#> ERR978244 4 0.0790 0.902 0.000 0.000 0.000 0.968 0.032 0.000
#> ERR978245 4 0.0790 0.902 0.000 0.000 0.000 0.968 0.032 0.000
#> ERR978246 4 0.0790 0.902 0.000 0.000 0.000 0.968 0.032 0.000
#> ERR978247 4 0.0790 0.902 0.000 0.000 0.000 0.968 0.032 0.000
#> ERR978248 4 0.5228 0.773 0.000 0.004 0.012 0.656 0.200 0.128
#> ERR978249 4 0.5228 0.773 0.000 0.004 0.012 0.656 0.200 0.128
#> ERR978250 4 0.5228 0.773 0.000 0.004 0.012 0.656 0.200 0.128
#> ERR978251 4 0.5228 0.773 0.000 0.004 0.012 0.656 0.200 0.128
#> ERR978252 4 0.5228 0.773 0.000 0.004 0.012 0.656 0.200 0.128
#> ERR978253 4 0.5228 0.773 0.000 0.004 0.012 0.656 0.200 0.128
#> ERR978254 4 0.5228 0.773 0.000 0.004 0.012 0.656 0.200 0.128
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 14049 rows and 148 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.3261 0.675 0.675
#> 3 3 1.000 0.978 0.990 0.7654 0.757 0.640
#> 4 4 0.735 0.678 0.869 0.2602 0.822 0.588
#> 5 5 1.000 0.987 0.992 0.0996 0.855 0.518
#> 6 6 1.000 1.000 1.000 0.0403 0.959 0.800
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 5
There is also optional best \(k\) = 2 3 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
#> ERR978109 2 0 1 0 1
#> ERR978110 2 0 1 0 1
#> ERR978111 2 0 1 0 1
#> ERR978112 2 0 1 0 1
#> ERR978113 2 0 1 0 1
#> ERR978114 2 0 1 0 1
#> ERR978115 2 0 1 0 1
#> ERR978116 2 0 1 0 1
#> ERR978117 2 0 1 0 1
#> ERR978118 2 0 1 0 1
#> ERR978119 2 0 1 0 1
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#> ERR978228 1 0 1 1 0
#> ERR978229 1 0 1 1 0
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#> ERR978240 1 0 1 1 0
#> ERR978241 2 0 1 0 1
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#> ERR978252 2 0 1 0 1
#> ERR978253 2 0 1 0 1
#> ERR978254 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.000 1.000 0 1.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000
#> ERR978123 3 0.000 0.983 0 0.000 1.000
#> ERR978124 3 0.000 0.983 0 0.000 1.000
#> ERR978125 3 0.000 0.983 0 0.000 1.000
#> ERR978126 3 0.000 0.983 0 0.000 1.000
#> ERR978127 3 0.000 0.983 0 0.000 1.000
#> ERR978128 3 0.000 0.983 0 0.000 1.000
#> ERR978129 3 0.000 0.983 0 0.000 1.000
#> ERR978130 3 0.000 0.983 0 0.000 1.000
#> ERR978131 3 0.000 0.983 0 0.000 1.000
#> ERR978132 3 0.000 0.983 0 0.000 1.000
#> ERR978133 3 0.000 0.983 0 0.000 1.000
#> ERR978134 3 0.000 0.983 0 0.000 1.000
#> ERR978135 3 0.000 0.983 0 0.000 1.000
#> ERR978136 3 0.000 0.983 0 0.000 1.000
#> ERR978137 3 0.000 0.983 0 0.000 1.000
#> ERR978138 3 0.000 0.983 0 0.000 1.000
#> ERR978139 3 0.000 0.983 0 0.000 1.000
#> ERR978140 3 0.000 0.983 0 0.000 1.000
#> ERR978141 3 0.000 0.983 0 0.000 1.000
#> ERR978142 3 0.000 0.983 0 0.000 1.000
#> ERR978143 3 0.000 0.983 0 0.000 1.000
#> ERR978144 3 0.000 0.983 0 0.000 1.000
#> ERR978145 3 0.000 0.983 0 0.000 1.000
#> ERR978146 3 0.000 0.983 0 0.000 1.000
#> ERR978147 3 0.000 0.983 0 0.000 1.000
#> ERR978148 3 0.000 0.983 0 0.000 1.000
#> ERR978149 3 0.000 0.983 0 0.000 1.000
#> ERR978150 3 0.000 0.983 0 0.000 1.000
#> ERR978151 3 0.000 0.983 0 0.000 1.000
#> ERR978152 3 0.000 0.983 0 0.000 1.000
#> ERR978153 1 0.000 1.000 1 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000
#> ERR978169 3 0.000 0.983 0 0.000 1.000
#> ERR978170 3 0.000 0.983 0 0.000 1.000
#> ERR978171 3 0.000 0.983 0 0.000 1.000
#> ERR978172 3 0.000 0.983 0 0.000 1.000
#> ERR978173 3 0.000 0.983 0 0.000 1.000
#> ERR978174 3 0.000 0.983 0 0.000 1.000
#> ERR978175 3 0.000 0.983 0 0.000 1.000
#> ERR978176 3 0.000 0.983 0 0.000 1.000
#> ERR978177 3 0.000 0.983 0 0.000 1.000
#> ERR978178 3 0.000 0.983 0 0.000 1.000
#> ERR978179 3 0.000 0.983 0 0.000 1.000
#> ERR978180 3 0.000 0.983 0 0.000 1.000
#> ERR978181 3 0.000 0.983 0 0.000 1.000
#> ERR978182 3 0.000 0.983 0 0.000 1.000
#> ERR978183 2 0.000 1.000 0 1.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000
#> ERR978197 3 0.000 0.983 0 0.000 1.000
#> ERR978198 3 0.000 0.983 0 0.000 1.000
#> ERR978199 3 0.000 0.983 0 0.000 1.000
#> ERR978200 3 0.000 0.983 0 0.000 1.000
#> ERR978201 3 0.000 0.983 0 0.000 1.000
#> ERR978202 3 0.000 0.983 0 0.000 1.000
#> ERR978203 3 0.000 0.983 0 0.000 1.000
#> ERR978204 3 0.000 0.983 0 0.000 1.000
#> ERR978205 3 0.000 0.983 0 0.000 1.000
#> ERR978206 3 0.000 0.983 0 0.000 1.000
#> ERR978207 3 0.000 0.983 0 0.000 1.000
#> ERR978208 3 0.000 0.983 0 0.000 1.000
#> ERR978209 3 0.000 0.983 0 0.000 1.000
#> ERR978210 3 0.000 0.983 0 0.000 1.000
#> ERR978211 3 0.000 0.983 0 0.000 1.000
#> ERR978212 3 0.000 0.983 0 0.000 1.000
#> ERR978213 3 0.000 0.983 0 0.000 1.000
#> ERR978214 3 0.000 0.983 0 0.000 1.000
#> ERR978215 3 0.000 0.983 0 0.000 1.000
#> ERR978216 3 0.000 0.983 0 0.000 1.000
#> ERR978217 3 0.000 0.983 0 0.000 1.000
#> ERR978218 3 0.000 0.983 0 0.000 1.000
#> ERR978219 3 0.000 0.983 0 0.000 1.000
#> ERR978220 3 0.000 0.983 0 0.000 1.000
#> ERR978221 3 0.000 0.983 0 0.000 1.000
#> ERR978222 3 0.000 0.983 0 0.000 1.000
#> ERR978223 3 0.000 0.983 0 0.000 1.000
#> ERR978224 3 0.000 0.983 0 0.000 1.000
#> ERR978225 3 0.000 0.983 0 0.000 1.000
#> ERR978226 3 0.000 0.983 0 0.000 1.000
#> ERR978227 1 0.000 1.000 1 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000
#> ERR978241 3 0.000 0.983 0 0.000 1.000
#> ERR978242 3 0.000 0.983 0 0.000 1.000
#> ERR978243 3 0.000 0.983 0 0.000 1.000
#> ERR978244 3 0.000 0.983 0 0.000 1.000
#> ERR978245 3 0.000 0.983 0 0.000 1.000
#> ERR978246 3 0.000 0.983 0 0.000 1.000
#> ERR978247 3 0.000 0.983 0 0.000 1.000
#> ERR978248 3 0.559 0.589 0 0.304 0.696
#> ERR978249 3 0.510 0.688 0 0.248 0.752
#> ERR978250 3 0.400 0.814 0 0.160 0.840
#> ERR978251 3 0.207 0.927 0 0.060 0.940
#> ERR978252 3 0.388 0.824 0 0.152 0.848
#> ERR978253 3 0.497 0.707 0 0.236 0.764
#> ERR978254 3 0.573 0.549 0 0.324 0.676
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978108 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978109 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978110 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978111 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978112 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978113 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978114 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978115 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978116 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978117 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978118 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978119 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978120 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978121 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978122 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978123 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978124 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978125 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978126 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978127 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978128 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978129 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978130 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978131 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978132 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978133 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978134 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978135 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978136 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978137 3 0.4454 0.4439 0 0.000 0.692 0.308
#> ERR978138 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978139 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978140 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978141 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978142 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978143 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978144 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978145 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978146 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978147 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978148 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978149 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978150 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978151 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978152 4 0.3569 0.6708 0 0.000 0.196 0.804
#> ERR978153 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978170 4 0.0336 0.7395 0 0.000 0.008 0.992
#> ERR978171 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978172 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978173 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978174 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978175 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978176 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978177 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978178 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978179 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978180 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978181 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978182 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978183 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978184 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978185 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978186 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978187 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978188 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978189 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978190 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978191 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978192 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978193 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978194 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978195 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978196 2 0.0000 1.0000 0 1.000 0.000 0.000
#> ERR978197 3 0.0000 0.5422 0 0.000 1.000 0.000
#> ERR978198 3 0.0000 0.5422 0 0.000 1.000 0.000
#> ERR978199 3 0.0000 0.5422 0 0.000 1.000 0.000
#> ERR978200 3 0.0000 0.5422 0 0.000 1.000 0.000
#> ERR978201 3 0.0000 0.5422 0 0.000 1.000 0.000
#> ERR978202 3 0.0000 0.5422 0 0.000 1.000 0.000
#> ERR978203 3 0.0000 0.5422 0 0.000 1.000 0.000
#> ERR978204 3 0.1940 0.5386 0 0.000 0.924 0.076
#> ERR978205 3 0.2081 0.5368 0 0.000 0.916 0.084
#> ERR978206 3 0.2281 0.5319 0 0.000 0.904 0.096
#> ERR978207 3 0.2281 0.5319 0 0.000 0.904 0.096
#> ERR978208 3 0.2408 0.5268 0 0.000 0.896 0.104
#> ERR978209 3 0.2281 0.5319 0 0.000 0.904 0.096
#> ERR978210 3 0.2081 0.5368 0 0.000 0.916 0.084
#> ERR978211 3 0.2011 0.5379 0 0.000 0.920 0.080
#> ERR978212 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978213 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978214 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978215 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978216 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978217 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978218 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978219 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978220 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978221 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978222 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978223 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978224 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978225 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978226 3 0.5000 -0.0457 0 0.000 0.504 0.496
#> ERR978227 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.0000 1 0.000 0.000 0.000
#> ERR978241 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978242 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978243 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978244 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978245 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978246 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978247 4 0.0000 0.7474 0 0.000 0.000 1.000
#> ERR978248 4 0.6918 0.0982 0 0.108 0.420 0.472
#> ERR978249 4 0.6599 0.1025 0 0.080 0.432 0.488
#> ERR978250 4 0.5859 0.0586 0 0.032 0.472 0.496
#> ERR978251 4 0.5409 0.0245 0 0.012 0.492 0.496
#> ERR978252 4 0.6009 0.0621 0 0.040 0.468 0.492
#> ERR978253 4 0.6447 0.0845 0 0.068 0.448 0.484
#> ERR978254 4 0.6994 0.1032 0 0.116 0.412 0.472
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978123 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978124 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978125 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978126 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978127 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978128 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978129 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978130 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978131 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978132 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978133 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978134 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978135 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978136 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978137 3 0.0000 0.962 0 0 1.000 0.000 0.000
#> ERR978138 5 0.0000 0.995 0 0 0.000 0.000 1.000
#> ERR978139 5 0.0000 0.995 0 0 0.000 0.000 1.000
#> ERR978140 5 0.0000 0.995 0 0 0.000 0.000 1.000
#> ERR978141 5 0.0000 0.995 0 0 0.000 0.000 1.000
#> ERR978142 5 0.0000 0.995 0 0 0.000 0.000 1.000
#> ERR978143 5 0.0000 0.995 0 0 0.000 0.000 1.000
#> ERR978144 5 0.0000 0.995 0 0 0.000 0.000 1.000
#> ERR978145 5 0.0000 0.995 0 0 0.000 0.000 1.000
#> ERR978146 3 0.2561 0.864 0 0 0.856 0.000 0.144
#> ERR978147 3 0.2561 0.864 0 0 0.856 0.000 0.144
#> ERR978148 3 0.2377 0.878 0 0 0.872 0.000 0.128
#> ERR978149 3 0.2561 0.864 0 0 0.856 0.000 0.144
#> ERR978150 3 0.2561 0.864 0 0 0.856 0.000 0.144
#> ERR978151 3 0.2377 0.878 0 0 0.872 0.000 0.128
#> ERR978152 3 0.2179 0.890 0 0 0.888 0.000 0.112
#> ERR978153 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0 0.000 0.000 0.000
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cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0 1 0 1 0 0 0 0
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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 14049 rows and 148 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.3261 0.675 0.675
#> 3 3 0.809 0.844 0.908 0.8359 0.757 0.640
#> 4 4 0.763 0.695 0.841 0.2109 0.822 0.588
#> 5 5 1.000 0.990 0.993 0.1100 0.899 0.634
#> 6 6 0.956 0.969 0.952 0.0231 0.982 0.907
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 5
There is also optional best \(k\) = 2 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
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#> ERR978224 2 0 1 0 1
#> ERR978225 2 0 1 0 1
#> ERR978226 2 0 1 0 1
#> ERR978227 1 0 1 1 0
#> ERR978228 1 0 1 1 0
#> ERR978229 1 0 1 1 0
#> ERR978230 1 0 1 1 0
#> ERR978231 1 0 1 1 0
#> ERR978232 1 0 1 1 0
#> ERR978233 1 0 1 1 0
#> ERR978234 1 0 1 1 0
#> ERR978235 1 0 1 1 0
#> ERR978236 1 0 1 1 0
#> ERR978237 1 0 1 1 0
#> ERR978238 1 0 1 1 0
#> ERR978239 1 0 1 1 0
#> ERR978240 1 0 1 1 0
#> ERR978241 2 0 1 0 1
#> ERR978242 2 0 1 0 1
#> ERR978243 2 0 1 0 1
#> ERR978244 2 0 1 0 1
#> ERR978245 2 0 1 0 1
#> ERR978246 2 0 1 0 1
#> ERR978247 2 0 1 0 1
#> ERR978248 2 0 1 0 1
#> ERR978249 2 0 1 0 1
#> ERR978250 2 0 1 0 1
#> ERR978251 2 0 1 0 1
#> ERR978252 2 0 1 0 1
#> ERR978253 2 0 1 0 1
#> ERR978254 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.604 1.000 0.000 0.620 0.380
#> ERR978108 2 0.604 1.000 0.000 0.620 0.380
#> ERR978109 2 0.604 1.000 0.000 0.620 0.380
#> ERR978110 2 0.604 1.000 0.000 0.620 0.380
#> ERR978111 2 0.604 1.000 0.000 0.620 0.380
#> ERR978112 2 0.604 1.000 0.000 0.620 0.380
#> ERR978113 2 0.604 1.000 0.000 0.620 0.380
#> ERR978114 2 0.604 1.000 0.000 0.620 0.380
#> ERR978115 2 0.604 1.000 0.000 0.620 0.380
#> ERR978116 2 0.604 1.000 0.000 0.620 0.380
#> ERR978117 2 0.604 1.000 0.000 0.620 0.380
#> ERR978118 2 0.604 1.000 0.000 0.620 0.380
#> ERR978119 2 0.604 1.000 0.000 0.620 0.380
#> ERR978120 2 0.604 1.000 0.000 0.620 0.380
#> ERR978121 2 0.604 1.000 0.000 0.620 0.380
#> ERR978122 2 0.604 1.000 0.000 0.620 0.380
#> ERR978123 3 0.576 0.837 0.000 0.328 0.672
#> ERR978124 3 0.576 0.837 0.000 0.328 0.672
#> ERR978125 3 0.576 0.837 0.000 0.328 0.672
#> ERR978126 3 0.576 0.837 0.000 0.328 0.672
#> ERR978127 3 0.576 0.837 0.000 0.328 0.672
#> ERR978128 3 0.576 0.837 0.000 0.328 0.672
#> ERR978129 3 0.576 0.837 0.000 0.328 0.672
#> ERR978130 3 0.576 0.837 0.000 0.328 0.672
#> ERR978131 3 0.576 0.837 0.000 0.328 0.672
#> ERR978132 3 0.576 0.837 0.000 0.328 0.672
#> ERR978133 3 0.576 0.837 0.000 0.328 0.672
#> ERR978134 3 0.576 0.837 0.000 0.328 0.672
#> ERR978135 3 0.576 0.837 0.000 0.328 0.672
#> ERR978136 3 0.576 0.837 0.000 0.328 0.672
#> ERR978137 3 0.576 0.837 0.000 0.328 0.672
#> ERR978138 3 0.576 0.837 0.000 0.328 0.672
#> ERR978139 3 0.576 0.837 0.000 0.328 0.672
#> ERR978140 3 0.576 0.837 0.000 0.328 0.672
#> ERR978141 3 0.576 0.837 0.000 0.328 0.672
#> ERR978142 3 0.576 0.837 0.000 0.328 0.672
#> ERR978143 3 0.576 0.837 0.000 0.328 0.672
#> ERR978144 3 0.576 0.837 0.000 0.328 0.672
#> ERR978145 3 0.576 0.837 0.000 0.328 0.672
#> ERR978146 3 0.576 0.837 0.000 0.328 0.672
#> ERR978147 3 0.576 0.837 0.000 0.328 0.672
#> ERR978148 3 0.576 0.837 0.000 0.328 0.672
#> ERR978149 3 0.576 0.837 0.000 0.328 0.672
#> ERR978150 3 0.576 0.837 0.000 0.328 0.672
#> ERR978151 3 0.576 0.837 0.000 0.328 0.672
#> ERR978152 3 0.576 0.837 0.000 0.328 0.672
#> ERR978153 1 0.000 1.000 1.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1.000 0.000 0.000
#> ERR978169 3 0.199 0.543 0.048 0.004 0.948
#> ERR978170 3 0.199 0.543 0.048 0.004 0.948
#> ERR978171 3 0.199 0.543 0.048 0.004 0.948
#> ERR978172 3 0.199 0.543 0.048 0.004 0.948
#> ERR978173 3 0.199 0.543 0.048 0.004 0.948
#> ERR978174 3 0.199 0.543 0.048 0.004 0.948
#> ERR978175 3 0.199 0.543 0.048 0.004 0.948
#> ERR978176 3 0.218 0.538 0.032 0.020 0.948
#> ERR978177 3 0.218 0.538 0.032 0.020 0.948
#> ERR978178 3 0.218 0.538 0.032 0.020 0.948
#> ERR978179 3 0.218 0.538 0.032 0.020 0.948
#> ERR978180 3 0.218 0.538 0.032 0.020 0.948
#> ERR978181 3 0.218 0.538 0.032 0.020 0.948
#> ERR978182 3 0.218 0.538 0.032 0.020 0.948
#> ERR978183 2 0.604 1.000 0.000 0.620 0.380
#> ERR978184 2 0.604 1.000 0.000 0.620 0.380
#> ERR978185 2 0.604 1.000 0.000 0.620 0.380
#> ERR978186 2 0.604 1.000 0.000 0.620 0.380
#> ERR978187 2 0.604 1.000 0.000 0.620 0.380
#> ERR978188 2 0.604 1.000 0.000 0.620 0.380
#> ERR978189 2 0.604 1.000 0.000 0.620 0.380
#> ERR978190 2 0.604 1.000 0.000 0.620 0.380
#> ERR978191 2 0.604 1.000 0.000 0.620 0.380
#> ERR978192 2 0.604 1.000 0.000 0.620 0.380
#> ERR978193 2 0.604 1.000 0.000 0.620 0.380
#> ERR978194 2 0.604 1.000 0.000 0.620 0.380
#> ERR978195 2 0.604 1.000 0.000 0.620 0.380
#> ERR978196 2 0.604 1.000 0.000 0.620 0.380
#> ERR978197 3 0.604 0.829 0.000 0.380 0.620
#> ERR978198 3 0.604 0.829 0.000 0.380 0.620
#> ERR978199 3 0.604 0.829 0.000 0.380 0.620
#> ERR978200 3 0.604 0.829 0.000 0.380 0.620
#> ERR978201 3 0.604 0.829 0.000 0.380 0.620
#> ERR978202 3 0.604 0.829 0.000 0.380 0.620
#> ERR978203 3 0.604 0.829 0.000 0.380 0.620
#> ERR978204 3 0.604 0.829 0.000 0.380 0.620
#> ERR978205 3 0.604 0.829 0.000 0.380 0.620
#> ERR978206 3 0.604 0.829 0.000 0.380 0.620
#> ERR978207 3 0.604 0.829 0.000 0.380 0.620
#> ERR978208 3 0.604 0.829 0.000 0.380 0.620
#> ERR978209 3 0.604 0.829 0.000 0.380 0.620
#> ERR978210 3 0.604 0.829 0.000 0.380 0.620
#> ERR978211 3 0.604 0.829 0.000 0.380 0.620
#> ERR978212 3 0.604 0.829 0.000 0.380 0.620
#> ERR978213 3 0.604 0.829 0.000 0.380 0.620
#> ERR978214 3 0.604 0.829 0.000 0.380 0.620
#> ERR978215 3 0.604 0.829 0.000 0.380 0.620
#> ERR978216 3 0.604 0.829 0.000 0.380 0.620
#> ERR978217 3 0.604 0.829 0.000 0.380 0.620
#> ERR978218 3 0.604 0.829 0.000 0.380 0.620
#> ERR978219 3 0.604 0.829 0.000 0.380 0.620
#> ERR978220 3 0.604 0.829 0.000 0.380 0.620
#> ERR978221 3 0.604 0.829 0.000 0.380 0.620
#> ERR978222 3 0.604 0.829 0.000 0.380 0.620
#> ERR978223 3 0.604 0.829 0.000 0.380 0.620
#> ERR978224 3 0.604 0.829 0.000 0.380 0.620
#> ERR978225 3 0.604 0.829 0.000 0.380 0.620
#> ERR978226 3 0.590 0.834 0.000 0.352 0.648
#> ERR978227 1 0.000 1.000 1.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1.000 0.000 0.000
#> ERR978241 3 0.199 0.521 0.004 0.048 0.948
#> ERR978242 3 0.199 0.521 0.004 0.048 0.948
#> ERR978243 3 0.199 0.521 0.004 0.048 0.948
#> ERR978244 3 0.199 0.521 0.004 0.048 0.948
#> ERR978245 3 0.199 0.521 0.004 0.048 0.948
#> ERR978246 3 0.199 0.521 0.004 0.048 0.948
#> ERR978247 3 0.199 0.521 0.004 0.048 0.948
#> ERR978248 3 0.218 0.531 0.020 0.032 0.948
#> ERR978249 3 0.218 0.531 0.020 0.032 0.948
#> ERR978250 3 0.218 0.531 0.020 0.032 0.948
#> ERR978251 3 0.218 0.531 0.020 0.032 0.948
#> ERR978252 3 0.218 0.531 0.020 0.032 0.948
#> ERR978253 3 0.218 0.531 0.020 0.032 0.948
#> ERR978254 3 0.218 0.531 0.020 0.032 0.948
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978123 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978124 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978125 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978126 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978127 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978128 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978129 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978130 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978131 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978132 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978133 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978134 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978135 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978136 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978137 4 0.500 -0.266 0 0.000 0.496 0.504
#> ERR978138 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978139 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978140 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978141 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978142 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978143 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978144 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978145 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978146 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978147 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978148 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978149 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978150 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978151 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978152 3 0.500 0.282 0 0.000 0.512 0.488
#> ERR978153 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978169 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978170 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978171 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978172 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978173 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978174 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978175 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978176 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978177 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978178 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978179 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978180 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978181 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978182 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978183 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978197 3 0.172 0.752 0 0.000 0.936 0.064
#> ERR978198 3 0.172 0.752 0 0.000 0.936 0.064
#> ERR978199 3 0.172 0.752 0 0.000 0.936 0.064
#> ERR978200 3 0.172 0.752 0 0.000 0.936 0.064
#> ERR978201 3 0.172 0.752 0 0.000 0.936 0.064
#> ERR978202 3 0.172 0.752 0 0.000 0.936 0.064
#> ERR978203 3 0.172 0.752 0 0.000 0.936 0.064
#> ERR978204 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978205 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978206 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978207 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978208 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978209 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978210 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978211 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978212 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978213 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978214 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978215 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978216 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978217 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978218 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978219 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978220 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978221 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978222 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978223 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978224 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978225 3 0.000 0.768 0 0.000 1.000 0.000
#> ERR978226 3 0.112 0.757 0 0.000 0.964 0.036
#> ERR978227 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978241 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978242 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978243 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978244 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978245 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978246 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978247 4 0.297 0.702 0 0.144 0.000 0.856
#> ERR978248 4 0.316 0.701 0 0.144 0.004 0.852
#> ERR978249 4 0.316 0.701 0 0.144 0.004 0.852
#> ERR978250 4 0.316 0.701 0 0.144 0.004 0.852
#> ERR978251 4 0.316 0.701 0 0.144 0.004 0.852
#> ERR978252 4 0.316 0.701 0 0.144 0.004 0.852
#> ERR978253 4 0.316 0.701 0 0.144 0.004 0.852
#> ERR978254 4 0.316 0.701 0 0.144 0.004 0.852
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978123 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978124 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978125 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978126 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978127 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978128 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978129 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978130 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978131 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978132 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978133 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978134 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978135 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978136 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978137 3 0.0290 0.993 0 0 0.992 0.008 0.000
#> ERR978138 3 0.0510 0.983 0 0 0.984 0.000 0.016
#> ERR978139 3 0.0510 0.983 0 0 0.984 0.000 0.016
#> ERR978140 3 0.0510 0.983 0 0 0.984 0.000 0.016
#> ERR978141 3 0.0510 0.983 0 0 0.984 0.000 0.016
#> ERR978142 3 0.0510 0.983 0 0 0.984 0.000 0.016
#> ERR978143 3 0.0510 0.983 0 0 0.984 0.000 0.016
#> ERR978144 3 0.0510 0.983 0 0 0.984 0.000 0.016
#> ERR978145 3 0.0510 0.983 0 0 0.984 0.000 0.016
#> ERR978146 3 0.0162 0.992 0 0 0.996 0.004 0.000
#> ERR978147 3 0.0162 0.992 0 0 0.996 0.004 0.000
#> ERR978148 3 0.0162 0.992 0 0 0.996 0.004 0.000
#> ERR978149 3 0.0162 0.992 0 0 0.996 0.004 0.000
#> ERR978150 3 0.0162 0.992 0 0 0.996 0.004 0.000
#> ERR978151 3 0.0162 0.992 0 0 0.996 0.004 0.000
#> ERR978152 3 0.0162 0.992 0 0 0.996 0.004 0.000
#> ERR978153 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978170 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978171 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978172 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978173 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978174 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978175 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978176 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978177 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978178 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978179 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978180 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978181 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978182 4 0.0000 0.997 0 0 0.000 1.000 0.000
#> ERR978183 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978184 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978185 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978186 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978187 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978188 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978189 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978190 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978191 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978192 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978193 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978194 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978195 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978196 2 0.0000 1.000 0 1 0.000 0.000 0.000
#> ERR978197 5 0.1908 0.926 0 0 0.092 0.000 0.908
#> ERR978198 5 0.1908 0.926 0 0 0.092 0.000 0.908
#> ERR978199 5 0.1908 0.926 0 0 0.092 0.000 0.908
#> ERR978200 5 0.1908 0.926 0 0 0.092 0.000 0.908
#> ERR978201 5 0.1908 0.926 0 0 0.092 0.000 0.908
#> ERR978202 5 0.1908 0.926 0 0 0.092 0.000 0.908
#> ERR978203 5 0.1908 0.926 0 0 0.092 0.000 0.908
#> ERR978204 5 0.0404 0.975 0 0 0.012 0.000 0.988
#> ERR978205 5 0.0404 0.975 0 0 0.012 0.000 0.988
#> ERR978206 5 0.0404 0.975 0 0 0.012 0.000 0.988
#> ERR978207 5 0.0404 0.975 0 0 0.012 0.000 0.988
#> ERR978208 5 0.0404 0.975 0 0 0.012 0.000 0.988
#> ERR978209 5 0.0404 0.975 0 0 0.012 0.000 0.988
#> ERR978210 5 0.0404 0.975 0 0 0.012 0.000 0.988
#> ERR978211 5 0.0404 0.975 0 0 0.012 0.000 0.988
#> ERR978212 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978213 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978214 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978215 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978216 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978217 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978218 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978219 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978220 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978221 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978222 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978223 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978224 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978225 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978226 5 0.0000 0.975 0 0 0.000 0.000 1.000
#> ERR978227 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0 0.000 0.000 0.000
#> ERR978241 4 0.0162 0.997 0 0 0.004 0.996 0.000
#> ERR978242 4 0.0162 0.997 0 0 0.004 0.996 0.000
#> ERR978243 4 0.0162 0.997 0 0 0.004 0.996 0.000
#> ERR978244 4 0.0162 0.997 0 0 0.004 0.996 0.000
#> ERR978245 4 0.0162 0.997 0 0 0.004 0.996 0.000
#> ERR978246 4 0.0162 0.997 0 0 0.004 0.996 0.000
#> ERR978247 4 0.0162 0.997 0 0 0.004 0.996 0.000
#> ERR978248 4 0.0290 0.995 0 0 0.008 0.992 0.000
#> ERR978249 4 0.0290 0.995 0 0 0.008 0.992 0.000
#> ERR978250 4 0.0290 0.995 0 0 0.008 0.992 0.000
#> ERR978251 4 0.0290 0.995 0 0 0.008 0.992 0.000
#> ERR978252 4 0.0290 0.995 0 0 0.008 0.992 0.000
#> ERR978253 4 0.0290 0.995 0 0 0.008 0.992 0.000
#> ERR978254 4 0.0290 0.995 0 0 0.008 0.992 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 0.909 0 1.000 0.000 0.000 0.000 0.000
#> ERR978123 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978124 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978125 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978126 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978127 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978128 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978129 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978130 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978131 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978132 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978133 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978134 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978135 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978136 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978137 3 0.0260 0.992 0 0.000 0.992 0.008 0.000 0.000
#> ERR978138 3 0.0458 0.981 0 0.000 0.984 0.000 0.016 0.000
#> ERR978139 3 0.0458 0.981 0 0.000 0.984 0.000 0.016 0.000
#> ERR978140 3 0.0458 0.981 0 0.000 0.984 0.000 0.016 0.000
#> ERR978141 3 0.0458 0.981 0 0.000 0.984 0.000 0.016 0.000
#> ERR978142 3 0.0458 0.981 0 0.000 0.984 0.000 0.016 0.000
#> ERR978143 3 0.0458 0.981 0 0.000 0.984 0.000 0.016 0.000
#> ERR978144 3 0.0458 0.981 0 0.000 0.984 0.000 0.016 0.000
#> ERR978145 3 0.0458 0.981 0 0.000 0.984 0.000 0.016 0.000
#> ERR978146 3 0.0146 0.991 0 0.000 0.996 0.004 0.000 0.000
#> ERR978147 3 0.0146 0.991 0 0.000 0.996 0.004 0.000 0.000
#> ERR978148 3 0.0146 0.991 0 0.000 0.996 0.004 0.000 0.000
#> ERR978149 3 0.0146 0.991 0 0.000 0.996 0.004 0.000 0.000
#> ERR978150 3 0.0146 0.991 0 0.000 0.996 0.004 0.000 0.000
#> ERR978151 3 0.0146 0.991 0 0.000 0.996 0.004 0.000 0.000
#> ERR978152 3 0.0146 0.991 0 0.000 0.996 0.004 0.000 0.000
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978170 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978171 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978172 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978173 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978174 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978175 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978176 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978177 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978178 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978179 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978180 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978181 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978182 4 0.0000 1.000 0 0.000 0.000 1.000 0.000 0.000
#> ERR978183 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978184 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978185 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978186 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978187 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978188 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978189 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978190 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978191 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978192 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978193 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978194 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978195 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978196 2 0.2912 0.895 0 0.784 0.000 0.000 0.000 0.216
#> ERR978197 5 0.1714 0.919 0 0.000 0.092 0.000 0.908 0.000
#> ERR978198 5 0.1714 0.919 0 0.000 0.092 0.000 0.908 0.000
#> ERR978199 5 0.1714 0.919 0 0.000 0.092 0.000 0.908 0.000
#> ERR978200 5 0.1714 0.919 0 0.000 0.092 0.000 0.908 0.000
#> ERR978201 5 0.1714 0.919 0 0.000 0.092 0.000 0.908 0.000
#> ERR978202 5 0.1714 0.919 0 0.000 0.092 0.000 0.908 0.000
#> ERR978203 5 0.1714 0.919 0 0.000 0.092 0.000 0.908 0.000
#> ERR978204 5 0.0363 0.971 0 0.000 0.012 0.000 0.988 0.000
#> ERR978205 5 0.0363 0.971 0 0.000 0.012 0.000 0.988 0.000
#> ERR978206 5 0.0363 0.971 0 0.000 0.012 0.000 0.988 0.000
#> ERR978207 5 0.0363 0.971 0 0.000 0.012 0.000 0.988 0.000
#> ERR978208 5 0.0363 0.971 0 0.000 0.012 0.000 0.988 0.000
#> ERR978209 5 0.0363 0.971 0 0.000 0.012 0.000 0.988 0.000
#> ERR978210 5 0.0363 0.971 0 0.000 0.012 0.000 0.988 0.000
#> ERR978211 5 0.0363 0.971 0 0.000 0.012 0.000 0.988 0.000
#> ERR978212 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978213 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978214 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978215 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978216 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978217 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978218 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978219 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978220 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978221 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978222 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978223 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978224 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978225 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978226 5 0.0000 0.972 0 0.000 0.000 0.000 1.000 0.000
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000 0.000 0.000
#> ERR978241 6 0.3023 0.996 0 0.000 0.004 0.212 0.000 0.784
#> ERR978242 6 0.3023 0.996 0 0.000 0.004 0.212 0.000 0.784
#> ERR978243 6 0.3023 0.996 0 0.000 0.004 0.212 0.000 0.784
#> ERR978244 6 0.3023 0.996 0 0.000 0.004 0.212 0.000 0.784
#> ERR978245 6 0.3023 0.996 0 0.000 0.004 0.212 0.000 0.784
#> ERR978246 6 0.3023 0.996 0 0.000 0.004 0.212 0.000 0.784
#> ERR978247 6 0.3023 0.996 0 0.000 0.004 0.212 0.000 0.784
#> ERR978248 6 0.3103 0.996 0 0.000 0.008 0.208 0.000 0.784
#> ERR978249 6 0.3103 0.996 0 0.000 0.008 0.208 0.000 0.784
#> ERR978250 6 0.3103 0.996 0 0.000 0.008 0.208 0.000 0.784
#> ERR978251 6 0.3103 0.996 0 0.000 0.008 0.208 0.000 0.784
#> ERR978252 6 0.3103 0.996 0 0.000 0.008 0.208 0.000 0.784
#> ERR978253 6 0.3103 0.996 0 0.000 0.008 0.208 0.000 0.784
#> ERR978254 6 0.3103 0.996 0 0.000 0.008 0.208 0.000 0.784
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 14049 rows and 148 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.3261 0.675 0.675
#> 3 3 0.762 0.923 0.947 0.9434 0.686 0.534
#> 4 4 0.974 0.926 0.965 0.1393 0.742 0.426
#> 5 5 0.932 0.898 0.951 0.1101 0.902 0.659
#> 6 6 0.927 0.830 0.899 0.0285 0.972 0.861
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 4 5
There is also optional best \(k\) = 2 4 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
#> ERR978109 2 0 1 0 1
#> ERR978110 2 0 1 0 1
#> ERR978111 2 0 1 0 1
#> ERR978112 2 0 1 0 1
#> ERR978113 2 0 1 0 1
#> ERR978114 2 0 1 0 1
#> ERR978115 2 0 1 0 1
#> ERR978116 2 0 1 0 1
#> ERR978117 2 0 1 0 1
#> ERR978118 2 0 1 0 1
#> ERR978119 2 0 1 0 1
#> ERR978120 2 0 1 0 1
#> ERR978121 2 0 1 0 1
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#> ERR978140 2 0 1 0 1
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#> ERR978153 1 0 1 1 0
#> ERR978154 1 0 1 1 0
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#> ERR978156 1 0 1 1 0
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#> ERR978176 2 0 1 0 1
#> ERR978177 2 0 1 0 1
#> ERR978178 2 0 1 0 1
#> ERR978179 2 0 1 0 1
#> ERR978180 2 0 1 0 1
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#> ERR978200 2 0 1 0 1
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#> ERR978216 2 0 1 0 1
#> ERR978217 2 0 1 0 1
#> ERR978218 2 0 1 0 1
#> ERR978219 2 0 1 0 1
#> ERR978220 2 0 1 0 1
#> ERR978221 2 0 1 0 1
#> ERR978222 2 0 1 0 1
#> ERR978223 2 0 1 0 1
#> ERR978224 2 0 1 0 1
#> ERR978225 2 0 1 0 1
#> ERR978226 2 0 1 0 1
#> ERR978227 1 0 1 1 0
#> ERR978228 1 0 1 1 0
#> ERR978229 1 0 1 1 0
#> ERR978230 1 0 1 1 0
#> ERR978231 1 0 1 1 0
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#> ERR978252 2 0 1 0 1
#> ERR978253 2 0 1 0 1
#> ERR978254 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.0000 0.866 0 1.000 0.000
#> ERR978108 2 0.0000 0.866 0 1.000 0.000
#> ERR978109 2 0.0000 0.866 0 1.000 0.000
#> ERR978110 2 0.0000 0.866 0 1.000 0.000
#> ERR978111 2 0.0000 0.866 0 1.000 0.000
#> ERR978112 2 0.0000 0.866 0 1.000 0.000
#> ERR978113 2 0.0000 0.866 0 1.000 0.000
#> ERR978114 2 0.0000 0.866 0 1.000 0.000
#> ERR978115 2 0.0000 0.866 0 1.000 0.000
#> ERR978116 2 0.0000 0.866 0 1.000 0.000
#> ERR978117 2 0.0000 0.866 0 1.000 0.000
#> ERR978118 2 0.0000 0.866 0 1.000 0.000
#> ERR978119 2 0.0000 0.866 0 1.000 0.000
#> ERR978120 2 0.0000 0.866 0 1.000 0.000
#> ERR978121 2 0.0000 0.866 0 1.000 0.000
#> ERR978122 2 0.0000 0.866 0 1.000 0.000
#> ERR978123 3 0.0000 1.000 0 0.000 1.000
#> ERR978124 3 0.0000 1.000 0 0.000 1.000
#> ERR978125 3 0.0000 1.000 0 0.000 1.000
#> ERR978126 3 0.0000 1.000 0 0.000 1.000
#> ERR978127 3 0.0000 1.000 0 0.000 1.000
#> ERR978128 3 0.0000 1.000 0 0.000 1.000
#> ERR978129 3 0.0000 1.000 0 0.000 1.000
#> ERR978130 3 0.0000 1.000 0 0.000 1.000
#> ERR978131 3 0.0237 0.995 0 0.004 0.996
#> ERR978132 3 0.0237 0.995 0 0.004 0.996
#> ERR978133 3 0.0000 1.000 0 0.000 1.000
#> ERR978134 3 0.0000 1.000 0 0.000 1.000
#> ERR978135 3 0.0000 1.000 0 0.000 1.000
#> ERR978136 3 0.0237 0.995 0 0.004 0.996
#> ERR978137 3 0.0237 0.995 0 0.004 0.996
#> ERR978138 3 0.0000 1.000 0 0.000 1.000
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#> ERR978169 3 0.0000 1.000 0 0.000 1.000
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#> ERR978183 2 0.0000 0.866 0 1.000 0.000
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#> ERR978187 2 0.0000 0.866 0 1.000 0.000
#> ERR978188 2 0.0000 0.866 0 1.000 0.000
#> ERR978189 2 0.0000 0.866 0 1.000 0.000
#> ERR978190 2 0.0000 0.866 0 1.000 0.000
#> ERR978191 2 0.0000 0.866 0 1.000 0.000
#> ERR978192 2 0.0000 0.866 0 1.000 0.000
#> ERR978193 2 0.0000 0.866 0 1.000 0.000
#> ERR978194 2 0.0000 0.866 0 1.000 0.000
#> ERR978195 2 0.0000 0.866 0 1.000 0.000
#> ERR978196 2 0.0000 0.866 0 1.000 0.000
#> ERR978197 2 0.5497 0.748 0 0.708 0.292
#> ERR978198 2 0.5810 0.690 0 0.664 0.336
#> ERR978199 2 0.5968 0.644 0 0.636 0.364
#> ERR978200 2 0.5988 0.637 0 0.632 0.368
#> ERR978201 2 0.5905 0.664 0 0.648 0.352
#> ERR978202 2 0.5785 0.696 0 0.668 0.332
#> ERR978203 2 0.5621 0.729 0 0.692 0.308
#> ERR978204 2 0.4605 0.835 0 0.796 0.204
#> ERR978205 2 0.4605 0.835 0 0.796 0.204
#> ERR978206 2 0.4605 0.835 0 0.796 0.204
#> ERR978207 2 0.4605 0.835 0 0.796 0.204
#> ERR978208 2 0.4605 0.835 0 0.796 0.204
#> ERR978209 2 0.4605 0.835 0 0.796 0.204
#> ERR978210 2 0.4605 0.835 0 0.796 0.204
#> ERR978211 2 0.4605 0.835 0 0.796 0.204
#> ERR978212 2 0.4062 0.854 0 0.836 0.164
#> ERR978213 2 0.4121 0.852 0 0.832 0.168
#> ERR978214 2 0.4121 0.852 0 0.832 0.168
#> ERR978215 2 0.4178 0.851 0 0.828 0.172
#> ERR978216 2 0.4121 0.852 0 0.832 0.168
#> ERR978217 2 0.4121 0.852 0 0.832 0.168
#> ERR978218 2 0.4062 0.854 0 0.836 0.164
#> ERR978219 2 0.4452 0.842 0 0.808 0.192
#> ERR978220 2 0.4605 0.835 0 0.796 0.204
#> ERR978221 2 0.4504 0.839 0 0.804 0.196
#> ERR978222 2 0.4605 0.835 0 0.796 0.204
#> ERR978223 2 0.4555 0.837 0 0.800 0.200
#> ERR978224 2 0.4452 0.842 0 0.808 0.192
#> ERR978225 2 0.4399 0.844 0 0.812 0.188
#> ERR978226 2 0.4235 0.849 0 0.824 0.176
#> ERR978227 1 0.0000 1.000 1 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000
#> ERR978241 3 0.0000 1.000 0 0.000 1.000
#> ERR978242 3 0.0000 1.000 0 0.000 1.000
#> ERR978243 3 0.0000 1.000 0 0.000 1.000
#> ERR978244 3 0.0000 1.000 0 0.000 1.000
#> ERR978245 3 0.0000 1.000 0 0.000 1.000
#> ERR978246 3 0.0000 1.000 0 0.000 1.000
#> ERR978247 3 0.0000 1.000 0 0.000 1.000
#> ERR978248 2 0.2625 0.851 0 0.916 0.084
#> ERR978249 2 0.4062 0.800 0 0.836 0.164
#> ERR978250 2 0.4842 0.735 0 0.776 0.224
#> ERR978251 2 0.5216 0.685 0 0.740 0.260
#> ERR978252 2 0.4750 0.745 0 0.784 0.216
#> ERR978253 2 0.3686 0.819 0 0.860 0.140
#> ERR978254 2 0.2711 0.849 0 0.912 0.088
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978108 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978109 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978110 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978111 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978112 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978113 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978114 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978115 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978116 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978117 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978118 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978119 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978120 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978121 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978122 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978123 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978124 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978125 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978126 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978127 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978128 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978129 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978130 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978131 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978132 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978133 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978134 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978135 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978136 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978137 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978138 3 0.0000 0.96472 0 0.000 1.000 0.000
#> ERR978139 3 0.0000 0.96472 0 0.000 1.000 0.000
#> ERR978140 3 0.0188 0.96433 0 0.000 0.996 0.004
#> ERR978141 3 0.0188 0.96433 0 0.000 0.996 0.004
#> ERR978142 3 0.0188 0.96433 0 0.000 0.996 0.004
#> ERR978143 3 0.0000 0.96472 0 0.000 1.000 0.000
#> ERR978144 3 0.0000 0.96472 0 0.000 1.000 0.000
#> ERR978145 3 0.0000 0.96472 0 0.000 1.000 0.000
#> ERR978146 3 0.0336 0.96745 0 0.000 0.992 0.008
#> ERR978147 3 0.0336 0.96745 0 0.000 0.992 0.008
#> ERR978148 3 0.0469 0.96830 0 0.000 0.988 0.012
#> ERR978149 3 0.0469 0.96830 0 0.000 0.988 0.012
#> ERR978150 3 0.0336 0.96745 0 0.000 0.992 0.008
#> ERR978151 3 0.0336 0.96745 0 0.000 0.992 0.008
#> ERR978152 3 0.0336 0.96745 0 0.000 0.992 0.008
#> ERR978153 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978170 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978171 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978172 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978173 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978174 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978175 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978176 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978177 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978178 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978179 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978180 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978181 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978182 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978183 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978184 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978185 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978186 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978187 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978188 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978189 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978190 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978191 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978192 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978193 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978194 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978195 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978196 2 0.0000 0.94577 0 1.000 0.000 0.000
#> ERR978197 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978198 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978199 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978200 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978201 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978202 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978203 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978204 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978205 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978206 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978207 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978208 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978209 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978210 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978211 3 0.1022 0.97085 0 0.000 0.968 0.032
#> ERR978212 3 0.2401 0.90514 0 0.092 0.904 0.004
#> ERR978213 3 0.2266 0.91280 0 0.084 0.912 0.004
#> ERR978214 3 0.2197 0.91644 0 0.080 0.916 0.004
#> ERR978215 3 0.2197 0.91644 0 0.080 0.916 0.004
#> ERR978216 3 0.2197 0.91644 0 0.080 0.916 0.004
#> ERR978217 3 0.2125 0.91989 0 0.076 0.920 0.004
#> ERR978218 3 0.2401 0.90514 0 0.092 0.904 0.004
#> ERR978219 3 0.1302 0.94568 0 0.044 0.956 0.000
#> ERR978220 3 0.1118 0.95044 0 0.036 0.964 0.000
#> ERR978221 3 0.1211 0.94819 0 0.040 0.960 0.000
#> ERR978222 3 0.1209 0.95148 0 0.032 0.964 0.004
#> ERR978223 3 0.1302 0.94568 0 0.044 0.956 0.000
#> ERR978224 3 0.1211 0.94819 0 0.040 0.960 0.000
#> ERR978225 3 0.1302 0.94568 0 0.044 0.956 0.000
#> ERR978226 3 0.1792 0.92885 0 0.068 0.932 0.000
#> ERR978227 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.00000 1 0.000 0.000 0.000
#> ERR978241 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978242 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978243 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978244 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978245 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978246 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978247 4 0.0000 0.93166 0 0.000 0.000 1.000
#> ERR978248 2 0.5615 0.37790 0 0.612 0.032 0.356
#> ERR978249 2 0.5746 0.27140 0 0.572 0.032 0.396
#> ERR978250 4 0.5859 0.02385 0 0.472 0.032 0.496
#> ERR978251 4 0.5792 0.21001 0 0.416 0.032 0.552
#> ERR978252 4 0.5861 -0.00731 0 0.480 0.032 0.488
#> ERR978253 2 0.5735 0.28311 0 0.576 0.032 0.392
#> ERR978254 2 0.5645 0.35833 0 0.604 0.032 0.364
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978123 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978124 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978125 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978126 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978127 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978128 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978129 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978130 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978131 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978132 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978133 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978134 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978135 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978136 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978137 3 0.000 0.855 0 0.000 1.000 0.000 0.000
#> ERR978138 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978139 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978140 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978141 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978142 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978143 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978144 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978145 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978146 3 0.297 0.757 0 0.000 0.816 0.000 0.184
#> ERR978147 3 0.314 0.740 0 0.000 0.796 0.000 0.204
#> ERR978148 3 0.369 0.734 0 0.000 0.780 0.020 0.200
#> ERR978149 3 0.374 0.733 0 0.000 0.780 0.024 0.196
#> ERR978150 3 0.327 0.742 0 0.000 0.796 0.004 0.200
#> ERR978151 3 0.273 0.775 0 0.000 0.840 0.000 0.160
#> ERR978152 3 0.265 0.781 0 0.000 0.848 0.000 0.152
#> ERR978153 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978169 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978170 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978171 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978172 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978173 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978174 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978175 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978176 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978177 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978178 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978179 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978180 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978181 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978182 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978183 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000 0.000 0.000
#> ERR978197 3 0.029 0.855 0 0.000 0.992 0.000 0.008
#> ERR978198 3 0.029 0.855 0 0.000 0.992 0.000 0.008
#> ERR978199 3 0.029 0.855 0 0.000 0.992 0.000 0.008
#> ERR978200 3 0.029 0.855 0 0.000 0.992 0.000 0.008
#> ERR978201 3 0.029 0.855 0 0.000 0.992 0.000 0.008
#> ERR978202 3 0.029 0.855 0 0.000 0.992 0.000 0.008
#> ERR978203 3 0.029 0.855 0 0.000 0.992 0.000 0.008
#> ERR978204 3 0.426 0.382 0 0.000 0.560 0.000 0.440
#> ERR978205 3 0.423 0.417 0 0.000 0.576 0.000 0.424
#> ERR978206 3 0.423 0.417 0 0.000 0.576 0.000 0.424
#> ERR978207 3 0.424 0.409 0 0.000 0.572 0.000 0.428
#> ERR978208 3 0.424 0.409 0 0.000 0.572 0.000 0.428
#> ERR978209 3 0.424 0.409 0 0.000 0.572 0.000 0.428
#> ERR978210 3 0.423 0.425 0 0.000 0.580 0.000 0.420
#> ERR978211 3 0.423 0.425 0 0.000 0.580 0.000 0.420
#> ERR978212 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978213 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978214 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978215 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978216 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978217 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978218 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978219 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978220 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978221 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978222 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978223 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978224 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978225 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978226 5 0.029 0.971 0 0.000 0.008 0.000 0.992
#> ERR978227 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978241 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978242 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978243 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978244 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978245 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978246 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978247 4 0.000 0.930 0 0.000 0.000 1.000 0.000
#> ERR978248 5 0.404 0.563 0 0.012 0.000 0.276 0.712
#> ERR978249 4 0.422 0.354 0 0.000 0.000 0.584 0.416
#> ERR978250 4 0.364 0.652 0 0.000 0.000 0.728 0.272
#> ERR978251 4 0.342 0.697 0 0.000 0.000 0.760 0.240
#> ERR978252 4 0.366 0.646 0 0.000 0.000 0.724 0.276
#> ERR978253 4 0.427 0.258 0 0.000 0.000 0.552 0.448
#> ERR978254 5 0.413 0.530 0 0.012 0.000 0.292 0.696
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978123 3 0.1501 0.874 0 0 0.924 0.000 0.000 0.076
#> ERR978124 3 0.1765 0.865 0 0 0.904 0.000 0.000 0.096
#> ERR978125 3 0.1910 0.857 0 0 0.892 0.000 0.000 0.108
#> ERR978126 3 0.1957 0.854 0 0 0.888 0.000 0.000 0.112
#> ERR978127 3 0.1910 0.857 0 0 0.892 0.000 0.000 0.108
#> ERR978128 3 0.1863 0.860 0 0 0.896 0.000 0.000 0.104
#> ERR978129 3 0.1610 0.871 0 0 0.916 0.000 0.000 0.084
#> ERR978130 3 0.1714 0.867 0 0 0.908 0.000 0.000 0.092
#> ERR978131 3 0.0363 0.894 0 0 0.988 0.000 0.000 0.012
#> ERR978132 3 0.0363 0.894 0 0 0.988 0.000 0.000 0.012
#> ERR978133 3 0.0363 0.894 0 0 0.988 0.000 0.000 0.012
#> ERR978134 3 0.0363 0.894 0 0 0.988 0.000 0.000 0.012
#> ERR978135 3 0.0363 0.894 0 0 0.988 0.000 0.000 0.012
#> ERR978136 3 0.0363 0.894 0 0 0.988 0.000 0.000 0.012
#> ERR978137 3 0.0363 0.894 0 0 0.988 0.000 0.000 0.012
#> ERR978138 5 0.3995 -0.507 0 0 0.004 0.000 0.516 0.480
#> ERR978139 5 0.3991 -0.478 0 0 0.004 0.000 0.524 0.472
#> ERR978140 5 0.3993 -0.489 0 0 0.004 0.000 0.520 0.476
#> ERR978141 5 0.3993 -0.489 0 0 0.004 0.000 0.520 0.476
#> ERR978142 5 0.3993 -0.489 0 0 0.004 0.000 0.520 0.476
#> ERR978143 5 0.3993 -0.489 0 0 0.004 0.000 0.520 0.476
#> ERR978144 5 0.3989 -0.468 0 0 0.004 0.000 0.528 0.468
#> ERR978145 5 0.3995 -0.509 0 0 0.004 0.000 0.516 0.480
#> ERR978146 6 0.4088 0.976 0 0 0.016 0.000 0.368 0.616
#> ERR978147 6 0.4303 0.983 0 0 0.016 0.008 0.360 0.616
#> ERR978148 6 0.4311 0.983 0 0 0.012 0.012 0.360 0.616
#> ERR978149 6 0.4219 0.976 0 0 0.008 0.012 0.360 0.620
#> ERR978150 6 0.4311 0.983 0 0 0.012 0.012 0.360 0.616
#> ERR978151 6 0.4211 0.983 0 0 0.016 0.004 0.364 0.616
#> ERR978152 6 0.4155 0.976 0 0 0.020 0.000 0.364 0.616
#> ERR978153 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.926 0 0 0.000 1.000 0.000 0.000
#> ERR978170 4 0.0000 0.926 0 0 0.000 1.000 0.000 0.000
#> ERR978171 4 0.0000 0.926 0 0 0.000 1.000 0.000 0.000
#> ERR978172 4 0.0000 0.926 0 0 0.000 1.000 0.000 0.000
#> ERR978173 4 0.0000 0.926 0 0 0.000 1.000 0.000 0.000
#> ERR978174 4 0.0000 0.926 0 0 0.000 1.000 0.000 0.000
#> ERR978175 4 0.0000 0.926 0 0 0.000 1.000 0.000 0.000
#> ERR978176 4 0.0937 0.921 0 0 0.000 0.960 0.000 0.040
#> ERR978177 4 0.0937 0.921 0 0 0.000 0.960 0.000 0.040
#> ERR978178 4 0.0937 0.921 0 0 0.000 0.960 0.000 0.040
#> ERR978179 4 0.0937 0.921 0 0 0.000 0.960 0.000 0.040
#> ERR978180 4 0.0937 0.921 0 0 0.000 0.960 0.000 0.040
#> ERR978181 4 0.0937 0.921 0 0 0.000 0.960 0.000 0.040
#> ERR978182 4 0.0937 0.921 0 0 0.000 0.960 0.000 0.040
#> ERR978183 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978184 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978185 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978186 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978187 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978188 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978189 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978190 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978191 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978192 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978193 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978194 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978195 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978196 2 0.0000 1.000 0 1 0.000 0.000 0.000 0.000
#> ERR978197 3 0.1500 0.889 0 0 0.936 0.000 0.012 0.052
#> ERR978198 3 0.1152 0.892 0 0 0.952 0.000 0.004 0.044
#> ERR978199 3 0.1082 0.893 0 0 0.956 0.000 0.004 0.040
#> ERR978200 3 0.1082 0.893 0 0 0.956 0.000 0.004 0.040
#> ERR978201 3 0.1152 0.892 0 0 0.952 0.000 0.004 0.044
#> ERR978202 3 0.1152 0.892 0 0 0.952 0.000 0.004 0.044
#> ERR978203 3 0.1333 0.891 0 0 0.944 0.000 0.008 0.048
#> ERR978204 3 0.4226 0.796 0 0 0.736 0.000 0.112 0.152
#> ERR978205 3 0.4226 0.796 0 0 0.736 0.000 0.112 0.152
#> ERR978206 3 0.4226 0.796 0 0 0.736 0.000 0.112 0.152
#> ERR978207 3 0.4190 0.798 0 0 0.740 0.000 0.112 0.148
#> ERR978208 3 0.4226 0.796 0 0 0.736 0.000 0.112 0.152
#> ERR978209 3 0.4190 0.798 0 0 0.740 0.000 0.112 0.148
#> ERR978210 3 0.4226 0.796 0 0 0.736 0.000 0.112 0.152
#> ERR978211 3 0.4190 0.798 0 0 0.740 0.000 0.112 0.148
#> ERR978212 5 0.1204 0.614 0 0 0.000 0.000 0.944 0.056
#> ERR978213 5 0.0260 0.643 0 0 0.000 0.000 0.992 0.008
#> ERR978214 5 0.0790 0.641 0 0 0.000 0.000 0.968 0.032
#> ERR978215 5 0.1141 0.629 0 0 0.000 0.000 0.948 0.052
#> ERR978216 5 0.0363 0.644 0 0 0.000 0.000 0.988 0.012
#> ERR978217 5 0.0713 0.633 0 0 0.000 0.000 0.972 0.028
#> ERR978218 5 0.1610 0.584 0 0 0.000 0.000 0.916 0.084
#> ERR978219 5 0.1327 0.615 0 0 0.000 0.000 0.936 0.064
#> ERR978220 5 0.0458 0.646 0 0 0.000 0.000 0.984 0.016
#> ERR978221 5 0.1075 0.636 0 0 0.000 0.000 0.952 0.048
#> ERR978222 5 0.1204 0.630 0 0 0.000 0.000 0.944 0.056
#> ERR978223 5 0.1141 0.633 0 0 0.000 0.000 0.948 0.052
#> ERR978224 5 0.0632 0.646 0 0 0.000 0.000 0.976 0.024
#> ERR978225 5 0.1327 0.615 0 0 0.000 0.000 0.936 0.064
#> ERR978226 5 0.0790 0.644 0 0 0.000 0.000 0.968 0.032
#> ERR978227 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0 0.000 0.000 0.000 0.000
#> ERR978241 4 0.0146 0.926 0 0 0.000 0.996 0.000 0.004
#> ERR978242 4 0.0146 0.926 0 0 0.000 0.996 0.000 0.004
#> ERR978243 4 0.0000 0.926 0 0 0.000 1.000 0.000 0.000
#> ERR978244 4 0.0000 0.926 0 0 0.000 1.000 0.000 0.000
#> ERR978245 4 0.0000 0.926 0 0 0.000 1.000 0.000 0.000
#> ERR978246 4 0.0146 0.926 0 0 0.000 0.996 0.000 0.004
#> ERR978247 4 0.0146 0.926 0 0 0.000 0.996 0.000 0.004
#> ERR978248 4 0.5277 0.660 0 0 0.000 0.592 0.152 0.256
#> ERR978249 4 0.4599 0.754 0 0 0.000 0.684 0.104 0.212
#> ERR978250 4 0.3786 0.823 0 0 0.000 0.768 0.064 0.168
#> ERR978251 4 0.3332 0.847 0 0 0.000 0.808 0.048 0.144
#> ERR978252 4 0.3806 0.822 0 0 0.000 0.768 0.068 0.164
#> ERR978253 4 0.4756 0.736 0 0 0.000 0.664 0.112 0.224
#> ERR978254 4 0.5219 0.670 0 0 0.000 0.604 0.152 0.244
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 14049 rows and 148 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 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 0.579 0.783 0.881 0.2344 0.828 0.828
#> 3 3 1.000 1.000 1.000 0.9618 0.713 0.653
#> 4 4 1.000 1.000 1.000 0.5273 0.757 0.551
#> 5 5 0.879 0.889 0.863 0.0825 0.961 0.870
#> 6 6 1.000 0.988 0.990 0.0838 0.917 0.681
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 3 4
There is also optional best \(k\) = 3 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0.000 0.850 0.0 1.0
#> ERR978108 2 0.000 0.850 0.0 1.0
#> ERR978109 2 0.000 0.850 0.0 1.0
#> ERR978110 2 0.000 0.850 0.0 1.0
#> ERR978111 2 0.000 0.850 0.0 1.0
#> ERR978112 2 0.000 0.850 0.0 1.0
#> ERR978113 2 0.000 0.850 0.0 1.0
#> ERR978114 2 0.000 0.850 0.0 1.0
#> ERR978115 2 0.000 0.850 0.0 1.0
#> ERR978116 2 0.000 0.850 0.0 1.0
#> ERR978117 2 0.000 0.850 0.0 1.0
#> ERR978118 2 0.000 0.850 0.0 1.0
#> ERR978119 2 0.000 0.850 0.0 1.0
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#> ERR978151 2 0.000 0.850 0.0 1.0
#> ERR978152 2 0.000 0.850 0.0 1.0
#> ERR978153 2 0.971 0.452 0.4 0.6
#> ERR978154 2 0.971 0.452 0.4 0.6
#> ERR978155 2 0.971 0.452 0.4 0.6
#> ERR978156 2 0.971 0.452 0.4 0.6
#> ERR978157 2 0.971 0.452 0.4 0.6
#> ERR978158 2 0.971 0.452 0.4 0.6
#> ERR978159 2 0.971 0.452 0.4 0.6
#> ERR978160 2 0.971 0.452 0.4 0.6
#> ERR978161 2 0.971 0.452 0.4 0.6
#> ERR978162 2 0.971 0.452 0.4 0.6
#> ERR978163 2 0.971 0.452 0.4 0.6
#> ERR978164 2 0.971 0.452 0.4 0.6
#> ERR978165 2 0.971 0.452 0.4 0.6
#> ERR978166 2 0.971 0.452 0.4 0.6
#> ERR978167 2 0.971 0.452 0.4 0.6
#> ERR978168 2 0.971 0.452 0.4 0.6
#> ERR978169 1 0.971 1.000 0.6 0.4
#> ERR978170 1 0.971 1.000 0.6 0.4
#> ERR978171 1 0.971 1.000 0.6 0.4
#> ERR978172 1 0.971 1.000 0.6 0.4
#> ERR978173 1 0.971 1.000 0.6 0.4
#> ERR978174 1 0.971 1.000 0.6 0.4
#> ERR978175 1 0.971 1.000 0.6 0.4
#> ERR978176 1 0.971 1.000 0.6 0.4
#> ERR978177 1 0.971 1.000 0.6 0.4
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#> ERR978179 1 0.971 1.000 0.6 0.4
#> ERR978180 1 0.971 1.000 0.6 0.4
#> ERR978181 1 0.971 1.000 0.6 0.4
#> ERR978182 1 0.971 1.000 0.6 0.4
#> ERR978183 2 0.000 0.850 0.0 1.0
#> ERR978184 2 0.000 0.850 0.0 1.0
#> ERR978185 2 0.000 0.850 0.0 1.0
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#> ERR978208 2 0.000 0.850 0.0 1.0
#> ERR978209 2 0.000 0.850 0.0 1.0
#> ERR978210 2 0.000 0.850 0.0 1.0
#> ERR978211 2 0.000 0.850 0.0 1.0
#> ERR978212 2 0.000 0.850 0.0 1.0
#> ERR978213 2 0.000 0.850 0.0 1.0
#> ERR978214 2 0.000 0.850 0.0 1.0
#> ERR978215 2 0.000 0.850 0.0 1.0
#> ERR978216 2 0.000 0.850 0.0 1.0
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#> ERR978223 2 0.000 0.850 0.0 1.0
#> ERR978224 2 0.000 0.850 0.0 1.0
#> ERR978225 2 0.000 0.850 0.0 1.0
#> ERR978226 2 0.000 0.850 0.0 1.0
#> ERR978227 2 0.971 0.452 0.4 0.6
#> ERR978228 2 0.971 0.452 0.4 0.6
#> ERR978229 2 0.971 0.452 0.4 0.6
#> ERR978230 2 0.971 0.452 0.4 0.6
#> ERR978231 2 0.971 0.452 0.4 0.6
#> ERR978232 2 0.971 0.452 0.4 0.6
#> ERR978233 2 0.971 0.452 0.4 0.6
#> ERR978234 2 0.971 0.452 0.4 0.6
#> ERR978235 2 0.971 0.452 0.4 0.6
#> ERR978236 2 0.971 0.452 0.4 0.6
#> ERR978237 2 0.971 0.452 0.4 0.6
#> ERR978238 2 0.971 0.452 0.4 0.6
#> ERR978239 2 0.971 0.452 0.4 0.6
#> ERR978240 2 0.971 0.452 0.4 0.6
#> ERR978241 2 0.000 0.850 0.0 1.0
#> ERR978242 2 0.000 0.850 0.0 1.0
#> ERR978243 2 0.000 0.850 0.0 1.0
#> ERR978244 2 0.000 0.850 0.0 1.0
#> ERR978245 2 0.000 0.850 0.0 1.0
#> ERR978246 2 0.000 0.850 0.0 1.0
#> ERR978247 2 0.000 0.850 0.0 1.0
#> ERR978248 2 0.000 0.850 0.0 1.0
#> ERR978249 2 0.000 0.850 0.0 1.0
#> ERR978250 2 0.000 0.850 0.0 1.0
#> ERR978251 2 0.000 0.850 0.0 1.0
#> ERR978252 2 0.000 0.850 0.0 1.0
#> ERR978253 2 0.000 0.850 0.0 1.0
#> ERR978254 2 0.000 0.850 0.0 1.0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0 1 0 1 0
#> ERR978108 2 0 1 0 1 0
#> ERR978109 2 0 1 0 1 0
#> ERR978110 2 0 1 0 1 0
#> ERR978111 2 0 1 0 1 0
#> ERR978112 2 0 1 0 1 0
#> ERR978113 2 0 1 0 1 0
#> ERR978114 2 0 1 0 1 0
#> ERR978115 2 0 1 0 1 0
#> ERR978116 2 0 1 0 1 0
#> ERR978117 2 0 1 0 1 0
#> ERR978118 2 0 1 0 1 0
#> ERR978119 2 0 1 0 1 0
#> ERR978120 2 0 1 0 1 0
#> ERR978121 2 0 1 0 1 0
#> ERR978122 2 0 1 0 1 0
#> ERR978123 2 0 1 0 1 0
#> ERR978124 2 0 1 0 1 0
#> ERR978125 2 0 1 0 1 0
#> ERR978126 2 0 1 0 1 0
#> ERR978127 2 0 1 0 1 0
#> ERR978128 2 0 1 0 1 0
#> ERR978129 2 0 1 0 1 0
#> ERR978130 2 0 1 0 1 0
#> ERR978131 2 0 1 0 1 0
#> ERR978132 2 0 1 0 1 0
#> ERR978133 2 0 1 0 1 0
#> ERR978134 2 0 1 0 1 0
#> ERR978135 2 0 1 0 1 0
#> ERR978136 2 0 1 0 1 0
#> ERR978137 2 0 1 0 1 0
#> ERR978138 2 0 1 0 1 0
#> ERR978139 2 0 1 0 1 0
#> ERR978140 2 0 1 0 1 0
#> ERR978141 2 0 1 0 1 0
#> ERR978142 2 0 1 0 1 0
#> ERR978143 2 0 1 0 1 0
#> ERR978144 2 0 1 0 1 0
#> ERR978145 2 0 1 0 1 0
#> ERR978146 2 0 1 0 1 0
#> ERR978147 2 0 1 0 1 0
#> ERR978148 2 0 1 0 1 0
#> ERR978149 2 0 1 0 1 0
#> ERR978150 2 0 1 0 1 0
#> ERR978151 2 0 1 0 1 0
#> ERR978152 2 0 1 0 1 0
#> ERR978153 1 0 1 1 0 0
#> ERR978154 1 0 1 1 0 0
#> ERR978155 1 0 1 1 0 0
#> ERR978156 1 0 1 1 0 0
#> ERR978157 1 0 1 1 0 0
#> ERR978158 1 0 1 1 0 0
#> ERR978159 1 0 1 1 0 0
#> ERR978160 1 0 1 1 0 0
#> ERR978161 1 0 1 1 0 0
#> ERR978162 1 0 1 1 0 0
#> ERR978163 1 0 1 1 0 0
#> ERR978164 1 0 1 1 0 0
#> ERR978165 1 0 1 1 0 0
#> ERR978166 1 0 1 1 0 0
#> ERR978167 1 0 1 1 0 0
#> ERR978168 1 0 1 1 0 0
#> ERR978169 3 0 1 0 0 1
#> ERR978170 3 0 1 0 0 1
#> ERR978171 3 0 1 0 0 1
#> ERR978172 3 0 1 0 0 1
#> ERR978173 3 0 1 0 0 1
#> ERR978174 3 0 1 0 0 1
#> ERR978175 3 0 1 0 0 1
#> ERR978176 3 0 1 0 0 1
#> ERR978177 3 0 1 0 0 1
#> ERR978178 3 0 1 0 0 1
#> ERR978179 3 0 1 0 0 1
#> ERR978180 3 0 1 0 0 1
#> ERR978181 3 0 1 0 0 1
#> ERR978182 3 0 1 0 0 1
#> ERR978183 2 0 1 0 1 0
#> ERR978184 2 0 1 0 1 0
#> ERR978185 2 0 1 0 1 0
#> ERR978186 2 0 1 0 1 0
#> ERR978187 2 0 1 0 1 0
#> ERR978188 2 0 1 0 1 0
#> ERR978189 2 0 1 0 1 0
#> ERR978190 2 0 1 0 1 0
#> ERR978191 2 0 1 0 1 0
#> ERR978192 2 0 1 0 1 0
#> ERR978193 2 0 1 0 1 0
#> ERR978194 2 0 1 0 1 0
#> ERR978195 2 0 1 0 1 0
#> ERR978196 2 0 1 0 1 0
#> ERR978197 2 0 1 0 1 0
#> ERR978198 2 0 1 0 1 0
#> ERR978199 2 0 1 0 1 0
#> ERR978200 2 0 1 0 1 0
#> ERR978201 2 0 1 0 1 0
#> ERR978202 2 0 1 0 1 0
#> ERR978203 2 0 1 0 1 0
#> ERR978204 2 0 1 0 1 0
#> ERR978205 2 0 1 0 1 0
#> ERR978206 2 0 1 0 1 0
#> ERR978207 2 0 1 0 1 0
#> ERR978208 2 0 1 0 1 0
#> ERR978209 2 0 1 0 1 0
#> ERR978210 2 0 1 0 1 0
#> ERR978211 2 0 1 0 1 0
#> ERR978212 2 0 1 0 1 0
#> ERR978213 2 0 1 0 1 0
#> ERR978214 2 0 1 0 1 0
#> ERR978215 2 0 1 0 1 0
#> ERR978216 2 0 1 0 1 0
#> ERR978217 2 0 1 0 1 0
#> ERR978218 2 0 1 0 1 0
#> ERR978219 2 0 1 0 1 0
#> ERR978220 2 0 1 0 1 0
#> ERR978221 2 0 1 0 1 0
#> ERR978222 2 0 1 0 1 0
#> ERR978223 2 0 1 0 1 0
#> ERR978224 2 0 1 0 1 0
#> ERR978225 2 0 1 0 1 0
#> ERR978226 2 0 1 0 1 0
#> ERR978227 1 0 1 1 0 0
#> ERR978228 1 0 1 1 0 0
#> ERR978229 1 0 1 1 0 0
#> ERR978230 1 0 1 1 0 0
#> ERR978231 1 0 1 1 0 0
#> ERR978232 1 0 1 1 0 0
#> ERR978233 1 0 1 1 0 0
#> ERR978234 1 0 1 1 0 0
#> ERR978235 1 0 1 1 0 0
#> ERR978236 1 0 1 1 0 0
#> ERR978237 1 0 1 1 0 0
#> ERR978238 1 0 1 1 0 0
#> ERR978239 1 0 1 1 0 0
#> ERR978240 1 0 1 1 0 0
#> ERR978241 2 0 1 0 1 0
#> ERR978242 2 0 1 0 1 0
#> ERR978243 2 0 1 0 1 0
#> ERR978244 2 0 1 0 1 0
#> ERR978245 2 0 1 0 1 0
#> ERR978246 2 0 1 0 1 0
#> ERR978247 2 0 1 0 1 0
#> ERR978248 2 0 1 0 1 0
#> ERR978249 2 0 1 0 1 0
#> ERR978250 2 0 1 0 1 0
#> ERR978251 2 0 1 0 1 0
#> ERR978252 2 0 1 0 1 0
#> ERR978253 2 0 1 0 1 0
#> ERR978254 2 0 1 0 1 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0 1 0 1 0 0
#> ERR978108 2 0 1 0 1 0 0
#> ERR978109 2 0 1 0 1 0 0
#> ERR978110 2 0 1 0 1 0 0
#> ERR978111 2 0 1 0 1 0 0
#> ERR978112 2 0 1 0 1 0 0
#> ERR978113 2 0 1 0 1 0 0
#> ERR978114 2 0 1 0 1 0 0
#> ERR978115 2 0 1 0 1 0 0
#> ERR978116 2 0 1 0 1 0 0
#> ERR978117 2 0 1 0 1 0 0
#> ERR978118 2 0 1 0 1 0 0
#> ERR978119 2 0 1 0 1 0 0
#> ERR978120 2 0 1 0 1 0 0
#> ERR978121 2 0 1 0 1 0 0
#> ERR978122 2 0 1 0 1 0 0
#> ERR978123 3 0 1 0 0 1 0
#> ERR978124 3 0 1 0 0 1 0
#> ERR978125 3 0 1 0 0 1 0
#> ERR978126 3 0 1 0 0 1 0
#> ERR978127 3 0 1 0 0 1 0
#> ERR978128 3 0 1 0 0 1 0
#> ERR978129 3 0 1 0 0 1 0
#> ERR978130 3 0 1 0 0 1 0
#> ERR978131 3 0 1 0 0 1 0
#> ERR978132 3 0 1 0 0 1 0
#> ERR978133 3 0 1 0 0 1 0
#> ERR978134 3 0 1 0 0 1 0
#> ERR978135 3 0 1 0 0 1 0
#> ERR978136 3 0 1 0 0 1 0
#> ERR978137 3 0 1 0 0 1 0
#> ERR978138 3 0 1 0 0 1 0
#> ERR978139 3 0 1 0 0 1 0
#> ERR978140 3 0 1 0 0 1 0
#> ERR978141 3 0 1 0 0 1 0
#> ERR978142 3 0 1 0 0 1 0
#> ERR978143 3 0 1 0 0 1 0
#> ERR978144 3 0 1 0 0 1 0
#> ERR978145 3 0 1 0 0 1 0
#> ERR978146 3 0 1 0 0 1 0
#> ERR978147 3 0 1 0 0 1 0
#> ERR978148 3 0 1 0 0 1 0
#> ERR978149 3 0 1 0 0 1 0
#> ERR978150 3 0 1 0 0 1 0
#> ERR978151 3 0 1 0 0 1 0
#> ERR978152 3 0 1 0 0 1 0
#> ERR978153 1 0 1 1 0 0 0
#> ERR978154 1 0 1 1 0 0 0
#> ERR978155 1 0 1 1 0 0 0
#> ERR978156 1 0 1 1 0 0 0
#> ERR978157 1 0 1 1 0 0 0
#> ERR978158 1 0 1 1 0 0 0
#> ERR978159 1 0 1 1 0 0 0
#> ERR978160 1 0 1 1 0 0 0
#> ERR978161 1 0 1 1 0 0 0
#> ERR978162 1 0 1 1 0 0 0
#> ERR978163 1 0 1 1 0 0 0
#> ERR978164 1 0 1 1 0 0 0
#> ERR978165 1 0 1 1 0 0 0
#> ERR978166 1 0 1 1 0 0 0
#> ERR978167 1 0 1 1 0 0 0
#> ERR978168 1 0 1 1 0 0 0
#> ERR978169 4 0 1 0 0 0 1
#> ERR978170 4 0 1 0 0 0 1
#> ERR978171 4 0 1 0 0 0 1
#> ERR978172 4 0 1 0 0 0 1
#> ERR978173 4 0 1 0 0 0 1
#> ERR978174 4 0 1 0 0 0 1
#> ERR978175 4 0 1 0 0 0 1
#> ERR978176 4 0 1 0 0 0 1
#> ERR978177 4 0 1 0 0 0 1
#> ERR978178 4 0 1 0 0 0 1
#> ERR978179 4 0 1 0 0 0 1
#> ERR978180 4 0 1 0 0 0 1
#> ERR978181 4 0 1 0 0 0 1
#> ERR978182 4 0 1 0 0 0 1
#> ERR978183 2 0 1 0 1 0 0
#> ERR978184 2 0 1 0 1 0 0
#> ERR978185 2 0 1 0 1 0 0
#> ERR978186 2 0 1 0 1 0 0
#> ERR978187 2 0 1 0 1 0 0
#> ERR978188 2 0 1 0 1 0 0
#> ERR978189 2 0 1 0 1 0 0
#> ERR978190 2 0 1 0 1 0 0
#> ERR978191 2 0 1 0 1 0 0
#> ERR978192 2 0 1 0 1 0 0
#> ERR978193 2 0 1 0 1 0 0
#> ERR978194 2 0 1 0 1 0 0
#> ERR978195 2 0 1 0 1 0 0
#> ERR978196 2 0 1 0 1 0 0
#> ERR978197 2 0 1 0 1 0 0
#> ERR978198 2 0 1 0 1 0 0
#> ERR978199 2 0 1 0 1 0 0
#> ERR978200 2 0 1 0 1 0 0
#> ERR978201 2 0 1 0 1 0 0
#> ERR978202 2 0 1 0 1 0 0
#> ERR978203 2 0 1 0 1 0 0
#> ERR978204 2 0 1 0 1 0 0
#> ERR978205 2 0 1 0 1 0 0
#> ERR978206 2 0 1 0 1 0 0
#> ERR978207 2 0 1 0 1 0 0
#> ERR978208 2 0 1 0 1 0 0
#> ERR978209 2 0 1 0 1 0 0
#> ERR978210 2 0 1 0 1 0 0
#> ERR978211 2 0 1 0 1 0 0
#> ERR978212 2 0 1 0 1 0 0
#> ERR978213 2 0 1 0 1 0 0
#> ERR978214 2 0 1 0 1 0 0
#> ERR978215 2 0 1 0 1 0 0
#> ERR978216 2 0 1 0 1 0 0
#> ERR978217 2 0 1 0 1 0 0
#> ERR978218 2 0 1 0 1 0 0
#> ERR978219 2 0 1 0 1 0 0
#> ERR978220 2 0 1 0 1 0 0
#> ERR978221 2 0 1 0 1 0 0
#> ERR978222 2 0 1 0 1 0 0
#> ERR978223 2 0 1 0 1 0 0
#> ERR978224 2 0 1 0 1 0 0
#> ERR978225 2 0 1 0 1 0 0
#> ERR978226 2 0 1 0 1 0 0
#> ERR978227 1 0 1 1 0 0 0
#> ERR978228 1 0 1 1 0 0 0
#> ERR978229 1 0 1 1 0 0 0
#> ERR978230 1 0 1 1 0 0 0
#> ERR978231 1 0 1 1 0 0 0
#> ERR978232 1 0 1 1 0 0 0
#> ERR978233 1 0 1 1 0 0 0
#> ERR978234 1 0 1 1 0 0 0
#> ERR978235 1 0 1 1 0 0 0
#> ERR978236 1 0 1 1 0 0 0
#> ERR978237 1 0 1 1 0 0 0
#> ERR978238 1 0 1 1 0 0 0
#> ERR978239 1 0 1 1 0 0 0
#> ERR978240 1 0 1 1 0 0 0
#> ERR978241 3 0 1 0 0 1 0
#> ERR978242 3 0 1 0 0 1 0
#> ERR978243 3 0 1 0 0 1 0
#> ERR978244 3 0 1 0 0 1 0
#> ERR978245 3 0 1 0 0 1 0
#> ERR978246 3 0 1 0 0 1 0
#> ERR978247 3 0 1 0 0 1 0
#> ERR978248 3 0 1 0 0 1 0
#> ERR978249 3 0 1 0 0 1 0
#> ERR978250 3 0 1 0 0 1 0
#> ERR978251 3 0 1 0 0 1 0
#> ERR978252 3 0 1 0 0 1 0
#> ERR978253 3 0 1 0 0 1 0
#> ERR978254 3 0 1 0 0 1 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978108 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978109 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978110 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978111 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978112 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978113 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978114 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978115 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978116 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978117 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978118 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978119 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978120 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978121 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978122 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978123 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978124 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978125 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978126 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978127 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978128 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978129 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978130 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978131 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978132 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978133 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978134 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978135 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978136 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978137 3 0.134 0.944 0 0.000 0.944 0 0.056
#> ERR978138 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978139 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978140 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978141 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978142 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978143 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978144 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978145 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978146 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978147 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978148 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978149 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978150 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978151 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978152 3 0.000 0.944 0 0.000 1.000 0 0.000
#> ERR978153 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978169 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978170 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978171 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978172 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978173 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978174 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978175 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978176 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978177 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978178 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978179 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978180 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978181 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978182 4 0.000 1.000 0 0.000 0.000 1 0.000
#> ERR978183 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978184 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978185 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978186 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978187 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978188 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978189 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978190 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978191 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978192 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978193 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978194 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978195 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978196 2 0.000 0.768 0 1.000 0.000 0 0.000
#> ERR978197 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978198 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978199 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978200 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978201 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978202 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978203 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978204 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978205 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978206 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978207 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978208 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978209 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978210 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978211 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978212 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978213 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978214 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978215 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978216 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978217 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978218 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978219 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978220 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978221 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978222 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978223 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978224 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978225 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978226 2 0.425 0.768 0 0.568 0.000 0 0.432
#> ERR978227 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000 0 0.000
#> ERR978241 5 0.430 0.941 0 0.000 0.488 0 0.512
#> ERR978242 5 0.430 0.941 0 0.000 0.488 0 0.512
#> ERR978243 5 0.430 0.941 0 0.000 0.488 0 0.512
#> ERR978244 5 0.430 0.941 0 0.000 0.488 0 0.512
#> ERR978245 5 0.430 0.941 0 0.000 0.488 0 0.512
#> ERR978246 5 0.430 0.941 0 0.000 0.488 0 0.512
#> ERR978247 5 0.430 0.941 0 0.000 0.488 0 0.512
#> ERR978248 5 0.425 0.941 0 0.000 0.432 0 0.568
#> ERR978249 5 0.425 0.941 0 0.000 0.432 0 0.568
#> ERR978250 5 0.425 0.941 0 0.000 0.432 0 0.568
#> ERR978251 5 0.425 0.941 0 0.000 0.432 0 0.568
#> ERR978252 5 0.425 0.941 0 0.000 0.432 0 0.568
#> ERR978253 5 0.425 0.941 0 0.000 0.432 0 0.568
#> ERR978254 5 0.425 0.941 0 0.000 0.432 0 0.568
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978108 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978109 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978110 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978111 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978112 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978113 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978114 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978115 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978116 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978117 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978118 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978119 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978120 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978121 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978122 2 0.000 1.000 0 1 0.000 0 0 0.000
#> ERR978123 3 0.000 0.962 0 0 1.000 0 0 0.000
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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 14049 rows and 148 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 0.300 0.701 0.773 0.3648 0.579 0.579
#> 3 3 0.610 0.897 0.874 0.5853 0.686 0.499
#> 4 4 0.609 0.804 0.815 0.1539 0.890 0.714
#> 5 5 0.632 0.797 0.739 0.1069 0.874 0.603
#> 6 6 0.665 0.768 0.743 0.0566 0.957 0.816
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
#> ERR978107 2 0.0376 0.8464 0.004 0.996
#> ERR978108 2 0.0376 0.8464 0.004 0.996
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#> ERR978236 1 0.7219 0.7713 0.800 0.200
#> ERR978237 1 0.7219 0.7713 0.800 0.200
#> ERR978238 1 0.7219 0.7713 0.800 0.200
#> ERR978239 1 0.7219 0.7713 0.800 0.200
#> ERR978240 1 0.7219 0.7713 0.800 0.200
#> ERR978241 2 0.9000 0.5688 0.316 0.684
#> ERR978242 2 0.9000 0.5688 0.316 0.684
#> ERR978243 2 0.9000 0.5688 0.316 0.684
#> ERR978244 2 0.9000 0.5688 0.316 0.684
#> ERR978245 2 0.9000 0.5688 0.316 0.684
#> ERR978246 2 0.9000 0.5688 0.316 0.684
#> ERR978247 2 0.9000 0.5688 0.316 0.684
#> ERR978248 2 0.0376 0.8464 0.004 0.996
#> ERR978249 2 0.0376 0.8464 0.004 0.996
#> ERR978250 2 0.0376 0.8464 0.004 0.996
#> ERR978251 2 0.0376 0.8464 0.004 0.996
#> ERR978252 2 0.0376 0.8464 0.004 0.996
#> ERR978253 2 0.0376 0.8464 0.004 0.996
#> ERR978254 2 0.0376 0.8464 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978108 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978109 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978110 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978111 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978112 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978113 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978114 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978115 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978116 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978117 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978118 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978119 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978120 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978121 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978122 2 0.0237 0.935 0.000 0.996 0.004
#> ERR978123 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978124 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978125 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978126 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978127 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978128 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978129 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978130 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978131 3 0.6282 0.818 0.012 0.324 0.664
#> ERR978132 3 0.6282 0.818 0.012 0.324 0.664
#> ERR978133 3 0.6282 0.818 0.012 0.324 0.664
#> ERR978134 3 0.6282 0.818 0.012 0.324 0.664
#> ERR978135 3 0.6282 0.818 0.012 0.324 0.664
#> ERR978136 3 0.6282 0.818 0.012 0.324 0.664
#> ERR978137 3 0.6282 0.818 0.012 0.324 0.664
#> ERR978138 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978139 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978140 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978141 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978142 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978143 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978144 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978145 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978146 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978147 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978148 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978149 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978150 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978151 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978152 3 0.5698 0.901 0.012 0.252 0.736
#> ERR978153 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978154 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978155 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978156 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978157 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978158 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978159 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978160 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978161 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978162 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978163 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978164 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978165 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978166 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978167 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978168 1 0.2443 0.967 0.940 0.032 0.028
#> ERR978169 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978170 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978171 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978172 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978173 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978174 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978175 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978176 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978177 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978178 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978179 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978180 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978181 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978182 3 0.4449 0.818 0.040 0.100 0.860
#> ERR978183 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978184 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978185 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978186 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978187 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978188 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978189 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978190 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978191 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978192 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978193 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978194 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978195 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978196 2 0.0661 0.935 0.008 0.988 0.004
#> ERR978197 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978198 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978199 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978200 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978201 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978202 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978203 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978204 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978205 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978206 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978207 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978208 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978209 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978210 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978211 2 0.2050 0.933 0.020 0.952 0.028
#> ERR978212 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978213 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978214 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978215 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978216 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978217 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978218 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978219 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978220 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978221 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978222 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978223 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978224 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978225 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978226 2 0.1905 0.936 0.016 0.956 0.028
#> ERR978227 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978228 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978229 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978230 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978231 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978232 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978233 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978234 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978235 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978236 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978237 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978238 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978239 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978240 1 0.4731 0.963 0.840 0.032 0.128
#> ERR978241 3 0.5406 0.891 0.020 0.200 0.780
#> ERR978242 3 0.5406 0.891 0.020 0.200 0.780
#> ERR978243 3 0.5406 0.891 0.020 0.200 0.780
#> ERR978244 3 0.5406 0.891 0.020 0.200 0.780
#> ERR978245 3 0.5406 0.891 0.020 0.200 0.780
#> ERR978246 3 0.5406 0.891 0.020 0.200 0.780
#> ERR978247 3 0.5406 0.891 0.020 0.200 0.780
#> ERR978248 2 0.5327 0.522 0.000 0.728 0.272
#> ERR978249 2 0.5397 0.502 0.000 0.720 0.280
#> ERR978250 2 0.5397 0.502 0.000 0.720 0.280
#> ERR978251 2 0.5397 0.502 0.000 0.720 0.280
#> ERR978252 2 0.5397 0.502 0.000 0.720 0.280
#> ERR978253 2 0.5397 0.502 0.000 0.720 0.280
#> ERR978254 2 0.5327 0.522 0.000 0.728 0.272
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978108 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978109 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978110 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978111 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978112 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978113 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978114 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978115 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978116 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978117 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978118 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978119 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978120 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978121 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978122 2 0.2867 0.768 0.000 0.884 0.104 0.012
#> ERR978123 3 0.0188 0.838 0.000 0.004 0.996 0.000
#> ERR978124 3 0.0188 0.838 0.000 0.004 0.996 0.000
#> ERR978125 3 0.0188 0.838 0.000 0.004 0.996 0.000
#> ERR978126 3 0.0188 0.838 0.000 0.004 0.996 0.000
#> ERR978127 3 0.0188 0.838 0.000 0.004 0.996 0.000
#> ERR978128 3 0.0188 0.838 0.000 0.004 0.996 0.000
#> ERR978129 3 0.0188 0.838 0.000 0.004 0.996 0.000
#> ERR978130 3 0.0188 0.838 0.000 0.004 0.996 0.000
#> ERR978131 3 0.0707 0.832 0.000 0.020 0.980 0.000
#> ERR978132 3 0.0707 0.832 0.000 0.020 0.980 0.000
#> ERR978133 3 0.0707 0.832 0.000 0.020 0.980 0.000
#> ERR978134 3 0.0707 0.832 0.000 0.020 0.980 0.000
#> ERR978135 3 0.0707 0.832 0.000 0.020 0.980 0.000
#> ERR978136 3 0.0707 0.832 0.000 0.020 0.980 0.000
#> ERR978137 3 0.0707 0.832 0.000 0.020 0.980 0.000
#> ERR978138 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978139 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978140 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978141 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978142 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978143 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978144 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978145 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978146 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978147 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978148 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978149 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978150 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978151 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978152 3 0.2466 0.836 0.000 0.004 0.900 0.096
#> ERR978153 1 0.1339 0.923 0.964 0.004 0.008 0.024
#> ERR978154 1 0.1339 0.923 0.964 0.004 0.008 0.024
#> ERR978155 1 0.1339 0.923 0.964 0.004 0.008 0.024
#> ERR978156 1 0.1339 0.923 0.964 0.004 0.008 0.024
#> ERR978157 1 0.1339 0.923 0.964 0.004 0.008 0.024
#> ERR978158 1 0.1339 0.923 0.964 0.004 0.008 0.024
#> ERR978159 1 0.1339 0.923 0.964 0.004 0.008 0.024
#> ERR978160 1 0.1339 0.923 0.964 0.004 0.008 0.024
#> ERR978161 1 0.1256 0.923 0.964 0.028 0.008 0.000
#> ERR978162 1 0.1256 0.923 0.964 0.028 0.008 0.000
#> ERR978163 1 0.1256 0.923 0.964 0.028 0.008 0.000
#> ERR978164 1 0.1256 0.923 0.964 0.028 0.008 0.000
#> ERR978165 1 0.1256 0.923 0.964 0.028 0.008 0.000
#> ERR978166 1 0.1256 0.923 0.964 0.028 0.008 0.000
#> ERR978167 1 0.1256 0.923 0.964 0.028 0.008 0.000
#> ERR978168 1 0.1256 0.923 0.964 0.028 0.008 0.000
#> ERR978169 4 0.4188 0.988 0.004 0.000 0.244 0.752
#> ERR978170 4 0.4188 0.988 0.004 0.000 0.244 0.752
#> ERR978171 4 0.4188 0.988 0.004 0.000 0.244 0.752
#> ERR978172 4 0.4188 0.988 0.004 0.000 0.244 0.752
#> ERR978173 4 0.4188 0.988 0.004 0.000 0.244 0.752
#> ERR978174 4 0.4188 0.988 0.004 0.000 0.244 0.752
#> ERR978175 4 0.4188 0.988 0.004 0.000 0.244 0.752
#> ERR978176 4 0.5217 0.988 0.012 0.024 0.244 0.720
#> ERR978177 4 0.5217 0.988 0.012 0.024 0.244 0.720
#> ERR978178 4 0.5217 0.988 0.012 0.024 0.244 0.720
#> ERR978179 4 0.5217 0.988 0.012 0.024 0.244 0.720
#> ERR978180 4 0.5217 0.988 0.012 0.024 0.244 0.720
#> ERR978181 4 0.5217 0.988 0.012 0.024 0.244 0.720
#> ERR978182 4 0.5217 0.988 0.012 0.024 0.244 0.720
#> ERR978183 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978184 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978185 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978186 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978187 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978188 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978189 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978190 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978191 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978192 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978193 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978194 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978195 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978196 2 0.2593 0.769 0.000 0.892 0.104 0.004
#> ERR978197 2 0.6546 0.685 0.000 0.524 0.396 0.080
#> ERR978198 2 0.6546 0.685 0.000 0.524 0.396 0.080
#> ERR978199 2 0.6546 0.685 0.000 0.524 0.396 0.080
#> ERR978200 2 0.6546 0.685 0.000 0.524 0.396 0.080
#> ERR978201 2 0.6546 0.685 0.000 0.524 0.396 0.080
#> ERR978202 2 0.6546 0.685 0.000 0.524 0.396 0.080
#> ERR978203 2 0.6546 0.685 0.000 0.524 0.396 0.080
#> ERR978204 2 0.6347 0.696 0.000 0.548 0.384 0.068
#> ERR978205 2 0.6347 0.696 0.000 0.548 0.384 0.068
#> ERR978206 2 0.6347 0.696 0.000 0.548 0.384 0.068
#> ERR978207 2 0.6347 0.696 0.000 0.548 0.384 0.068
#> ERR978208 2 0.6347 0.696 0.000 0.548 0.384 0.068
#> ERR978209 2 0.6347 0.696 0.000 0.548 0.384 0.068
#> ERR978210 2 0.6347 0.696 0.000 0.548 0.384 0.068
#> ERR978211 2 0.6347 0.696 0.000 0.548 0.384 0.068
#> ERR978212 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978213 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978214 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978215 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978216 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978217 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978218 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978219 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978220 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978221 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978222 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978223 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978224 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978225 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978226 2 0.6747 0.688 0.000 0.528 0.372 0.100
#> ERR978227 1 0.4322 0.916 0.828 0.060 0.008 0.104
#> ERR978228 1 0.4322 0.916 0.828 0.060 0.008 0.104
#> ERR978229 1 0.4322 0.916 0.828 0.060 0.008 0.104
#> ERR978230 1 0.4322 0.916 0.828 0.060 0.008 0.104
#> ERR978231 1 0.4322 0.916 0.828 0.060 0.008 0.104
#> ERR978232 1 0.4322 0.916 0.828 0.060 0.008 0.104
#> ERR978233 1 0.4322 0.916 0.828 0.060 0.008 0.104
#> ERR978234 1 0.4304 0.916 0.828 0.056 0.008 0.108
#> ERR978235 1 0.4304 0.916 0.828 0.056 0.008 0.108
#> ERR978236 1 0.4304 0.916 0.828 0.056 0.008 0.108
#> ERR978237 1 0.4304 0.916 0.828 0.056 0.008 0.108
#> ERR978238 1 0.4304 0.916 0.828 0.056 0.008 0.108
#> ERR978239 1 0.4304 0.916 0.828 0.056 0.008 0.108
#> ERR978240 1 0.4304 0.916 0.828 0.056 0.008 0.108
#> ERR978241 3 0.3400 0.721 0.000 0.000 0.820 0.180
#> ERR978242 3 0.3400 0.721 0.000 0.000 0.820 0.180
#> ERR978243 3 0.3400 0.721 0.000 0.000 0.820 0.180
#> ERR978244 3 0.3400 0.721 0.000 0.000 0.820 0.180
#> ERR978245 3 0.3400 0.721 0.000 0.000 0.820 0.180
#> ERR978246 3 0.3400 0.721 0.000 0.000 0.820 0.180
#> ERR978247 3 0.3400 0.721 0.000 0.000 0.820 0.180
#> ERR978248 3 0.4872 0.531 0.004 0.212 0.752 0.032
#> ERR978249 3 0.4872 0.531 0.004 0.212 0.752 0.032
#> ERR978250 3 0.4872 0.531 0.004 0.212 0.752 0.032
#> ERR978251 3 0.4872 0.531 0.004 0.212 0.752 0.032
#> ERR978252 3 0.4872 0.531 0.004 0.212 0.752 0.032
#> ERR978253 3 0.4872 0.531 0.004 0.212 0.752 0.032
#> ERR978254 3 0.4872 0.531 0.004 0.212 0.752 0.032
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.405 0.916 0.000 0.644 0.000 0.000 0.356
#> ERR978108 2 0.405 0.916 0.000 0.644 0.000 0.000 0.356
#> ERR978109 2 0.405 0.916 0.000 0.644 0.000 0.000 0.356
#> ERR978110 2 0.405 0.916 0.000 0.644 0.000 0.000 0.356
#> ERR978111 2 0.405 0.916 0.000 0.644 0.000 0.000 0.356
#> ERR978112 2 0.405 0.916 0.000 0.644 0.000 0.000 0.356
#> ERR978113 2 0.405 0.916 0.000 0.644 0.000 0.000 0.356
#> ERR978114 2 0.405 0.916 0.000 0.644 0.000 0.000 0.356
#> ERR978115 2 0.442 0.915 0.000 0.632 0.012 0.000 0.356
#> ERR978116 2 0.442 0.915 0.000 0.632 0.012 0.000 0.356
#> ERR978117 2 0.442 0.915 0.000 0.632 0.012 0.000 0.356
#> ERR978118 2 0.442 0.915 0.000 0.632 0.012 0.000 0.356
#> ERR978119 2 0.442 0.915 0.000 0.632 0.012 0.000 0.356
#> ERR978120 2 0.442 0.915 0.000 0.632 0.012 0.000 0.356
#> ERR978121 2 0.442 0.915 0.000 0.632 0.012 0.000 0.356
#> ERR978122 2 0.442 0.915 0.000 0.632 0.012 0.000 0.356
#> ERR978123 3 0.330 0.824 0.000 0.016 0.816 0.000 0.168
#> ERR978124 3 0.330 0.824 0.000 0.016 0.816 0.000 0.168
#> ERR978125 3 0.330 0.824 0.000 0.016 0.816 0.000 0.168
#> ERR978126 3 0.330 0.824 0.000 0.016 0.816 0.000 0.168
#> ERR978127 3 0.330 0.824 0.000 0.016 0.816 0.000 0.168
#> ERR978128 3 0.330 0.824 0.000 0.016 0.816 0.000 0.168
#> ERR978129 3 0.330 0.824 0.000 0.016 0.816 0.000 0.168
#> ERR978130 3 0.330 0.824 0.000 0.016 0.816 0.000 0.168
#> ERR978131 3 0.344 0.820 0.000 0.020 0.808 0.000 0.172
#> ERR978132 3 0.344 0.820 0.000 0.020 0.808 0.000 0.172
#> ERR978133 3 0.344 0.820 0.000 0.020 0.808 0.000 0.172
#> ERR978134 3 0.344 0.820 0.000 0.020 0.808 0.000 0.172
#> ERR978135 3 0.344 0.820 0.000 0.020 0.808 0.000 0.172
#> ERR978136 3 0.344 0.820 0.000 0.020 0.808 0.000 0.172
#> ERR978137 3 0.344 0.820 0.000 0.020 0.808 0.000 0.172
#> ERR978138 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978139 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978140 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978141 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978142 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978143 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978144 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978145 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978146 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978147 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978148 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978149 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978150 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978151 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978152 3 0.603 0.853 0.000 0.020 0.632 0.140 0.208
#> ERR978153 1 0.115 0.879 0.964 0.000 0.008 0.024 0.004
#> ERR978154 1 0.115 0.879 0.964 0.000 0.008 0.024 0.004
#> ERR978155 1 0.115 0.879 0.964 0.000 0.008 0.024 0.004
#> ERR978156 1 0.115 0.879 0.964 0.000 0.008 0.024 0.004
#> ERR978157 1 0.115 0.879 0.964 0.000 0.008 0.024 0.004
#> ERR978158 1 0.115 0.879 0.964 0.000 0.008 0.024 0.004
#> ERR978159 1 0.115 0.879 0.964 0.000 0.008 0.024 0.004
#> ERR978160 1 0.115 0.879 0.964 0.000 0.008 0.024 0.004
#> ERR978161 1 0.104 0.879 0.964 0.032 0.000 0.000 0.004
#> ERR978162 1 0.104 0.879 0.964 0.032 0.000 0.000 0.004
#> ERR978163 1 0.104 0.879 0.964 0.032 0.000 0.000 0.004
#> ERR978164 1 0.104 0.879 0.964 0.032 0.000 0.000 0.004
#> ERR978165 1 0.104 0.879 0.964 0.032 0.000 0.000 0.004
#> ERR978166 1 0.104 0.879 0.964 0.032 0.000 0.000 0.004
#> ERR978167 1 0.104 0.879 0.964 0.032 0.000 0.000 0.004
#> ERR978168 1 0.104 0.879 0.964 0.032 0.000 0.000 0.004
#> ERR978169 4 0.365 0.981 0.000 0.040 0.152 0.808 0.000
#> ERR978170 4 0.365 0.981 0.000 0.040 0.152 0.808 0.000
#> ERR978171 4 0.365 0.981 0.000 0.040 0.152 0.808 0.000
#> ERR978172 4 0.365 0.981 0.000 0.040 0.152 0.808 0.000
#> ERR978173 4 0.365 0.981 0.000 0.040 0.152 0.808 0.000
#> ERR978174 4 0.365 0.981 0.000 0.040 0.152 0.808 0.000
#> ERR978175 4 0.365 0.981 0.000 0.040 0.152 0.808 0.000
#> ERR978176 4 0.256 0.981 0.000 0.000 0.144 0.856 0.000
#> ERR978177 4 0.256 0.981 0.000 0.000 0.144 0.856 0.000
#> ERR978178 4 0.256 0.981 0.000 0.000 0.144 0.856 0.000
#> ERR978179 4 0.256 0.981 0.000 0.000 0.144 0.856 0.000
#> ERR978180 4 0.256 0.981 0.000 0.000 0.144 0.856 0.000
#> ERR978181 4 0.256 0.981 0.000 0.000 0.144 0.856 0.000
#> ERR978182 4 0.256 0.981 0.000 0.000 0.144 0.856 0.000
#> ERR978183 2 0.636 0.904 0.000 0.508 0.068 0.040 0.384
#> ERR978184 2 0.636 0.904 0.000 0.508 0.068 0.040 0.384
#> ERR978185 2 0.636 0.904 0.000 0.508 0.068 0.040 0.384
#> ERR978186 2 0.636 0.904 0.000 0.508 0.068 0.040 0.384
#> ERR978187 2 0.636 0.904 0.000 0.508 0.068 0.040 0.384
#> ERR978188 2 0.636 0.904 0.000 0.508 0.068 0.040 0.384
#> ERR978189 2 0.636 0.904 0.000 0.508 0.068 0.040 0.384
#> ERR978190 2 0.645 0.904 0.000 0.500 0.076 0.040 0.384
#> ERR978191 2 0.645 0.904 0.000 0.500 0.076 0.040 0.384
#> ERR978192 2 0.645 0.904 0.000 0.500 0.076 0.040 0.384
#> ERR978193 2 0.645 0.904 0.000 0.500 0.076 0.040 0.384
#> ERR978194 2 0.645 0.904 0.000 0.500 0.076 0.040 0.384
#> ERR978195 2 0.645 0.904 0.000 0.500 0.076 0.040 0.384
#> ERR978196 2 0.645 0.904 0.000 0.500 0.076 0.040 0.384
#> ERR978197 5 0.375 0.699 0.000 0.092 0.072 0.008 0.828
#> ERR978198 5 0.375 0.699 0.000 0.092 0.072 0.008 0.828
#> ERR978199 5 0.375 0.699 0.000 0.092 0.072 0.008 0.828
#> ERR978200 5 0.375 0.699 0.000 0.092 0.072 0.008 0.828
#> ERR978201 5 0.375 0.699 0.000 0.092 0.072 0.008 0.828
#> ERR978202 5 0.375 0.699 0.000 0.092 0.072 0.008 0.828
#> ERR978203 5 0.375 0.699 0.000 0.092 0.072 0.008 0.828
#> ERR978204 5 0.345 0.703 0.000 0.068 0.068 0.012 0.852
#> ERR978205 5 0.345 0.703 0.000 0.068 0.068 0.012 0.852
#> ERR978206 5 0.345 0.703 0.000 0.068 0.068 0.012 0.852
#> ERR978207 5 0.345 0.703 0.000 0.068 0.068 0.012 0.852
#> ERR978208 5 0.345 0.703 0.000 0.068 0.068 0.012 0.852
#> ERR978209 5 0.345 0.703 0.000 0.068 0.068 0.012 0.852
#> ERR978210 5 0.345 0.703 0.000 0.068 0.068 0.012 0.852
#> ERR978211 5 0.345 0.703 0.000 0.068 0.068 0.012 0.852
#> ERR978212 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978213 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978214 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978215 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978216 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978217 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978218 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978219 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978220 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978221 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978222 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978223 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978224 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978225 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978226 5 0.051 0.722 0.000 0.016 0.000 0.000 0.984
#> ERR978227 1 0.524 0.863 0.744 0.136 0.064 0.052 0.004
#> ERR978228 1 0.524 0.863 0.744 0.136 0.064 0.052 0.004
#> ERR978229 1 0.524 0.863 0.744 0.136 0.064 0.052 0.004
#> ERR978230 1 0.524 0.863 0.744 0.136 0.064 0.052 0.004
#> ERR978231 1 0.524 0.863 0.744 0.136 0.064 0.052 0.004
#> ERR978232 1 0.524 0.863 0.744 0.136 0.064 0.052 0.004
#> ERR978233 1 0.524 0.863 0.744 0.136 0.064 0.052 0.004
#> ERR978234 1 0.502 0.863 0.744 0.164 0.060 0.028 0.004
#> ERR978235 1 0.502 0.863 0.744 0.164 0.060 0.028 0.004
#> ERR978236 1 0.502 0.863 0.744 0.164 0.060 0.028 0.004
#> ERR978237 1 0.502 0.863 0.744 0.164 0.060 0.028 0.004
#> ERR978238 1 0.502 0.863 0.744 0.164 0.060 0.028 0.004
#> ERR978239 1 0.502 0.863 0.744 0.164 0.060 0.028 0.004
#> ERR978240 1 0.502 0.863 0.744 0.164 0.060 0.028 0.004
#> ERR978241 3 0.666 0.771 0.004 0.036 0.592 0.208 0.160
#> ERR978242 3 0.666 0.771 0.004 0.036 0.592 0.208 0.160
#> ERR978243 3 0.666 0.771 0.004 0.036 0.592 0.208 0.160
#> ERR978244 3 0.666 0.771 0.004 0.036 0.592 0.208 0.160
#> ERR978245 3 0.666 0.771 0.004 0.036 0.592 0.208 0.160
#> ERR978246 3 0.666 0.771 0.004 0.036 0.592 0.208 0.160
#> ERR978247 3 0.666 0.771 0.004 0.036 0.592 0.208 0.160
#> ERR978248 5 0.668 -0.159 0.000 0.124 0.408 0.024 0.444
#> ERR978249 5 0.668 -0.159 0.000 0.124 0.408 0.024 0.444
#> ERR978250 5 0.668 -0.159 0.000 0.124 0.408 0.024 0.444
#> ERR978251 5 0.668 -0.159 0.000 0.124 0.408 0.024 0.444
#> ERR978252 5 0.668 -0.159 0.000 0.124 0.408 0.024 0.444
#> ERR978253 5 0.668 -0.159 0.000 0.124 0.408 0.024 0.444
#> ERR978254 5 0.668 -0.159 0.000 0.124 0.408 0.024 0.444
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.5519 0.854 0.000 0.616 0.000 0.020 0.220 NA
#> ERR978108 2 0.5519 0.854 0.000 0.616 0.000 0.020 0.220 NA
#> ERR978109 2 0.5519 0.854 0.000 0.616 0.000 0.020 0.220 NA
#> ERR978110 2 0.5519 0.854 0.000 0.616 0.000 0.020 0.220 NA
#> ERR978111 2 0.5519 0.854 0.000 0.616 0.000 0.020 0.220 NA
#> ERR978112 2 0.5519 0.854 0.000 0.616 0.000 0.020 0.220 NA
#> ERR978113 2 0.5519 0.854 0.000 0.616 0.000 0.020 0.220 NA
#> ERR978114 2 0.5519 0.854 0.000 0.616 0.000 0.020 0.220 NA
#> ERR978115 2 0.5678 0.852 0.000 0.580 0.000 0.012 0.212 NA
#> ERR978116 2 0.5678 0.852 0.000 0.580 0.000 0.012 0.212 NA
#> ERR978117 2 0.5678 0.852 0.000 0.580 0.000 0.012 0.212 NA
#> ERR978118 2 0.5678 0.852 0.000 0.580 0.000 0.012 0.212 NA
#> ERR978119 2 0.5678 0.852 0.000 0.580 0.000 0.012 0.212 NA
#> ERR978120 2 0.5678 0.852 0.000 0.580 0.000 0.012 0.212 NA
#> ERR978121 2 0.5678 0.852 0.000 0.580 0.000 0.012 0.212 NA
#> ERR978122 2 0.5678 0.852 0.000 0.580 0.000 0.012 0.212 NA
#> ERR978123 3 0.0632 0.625 0.000 0.000 0.976 0.000 0.024 NA
#> ERR978124 3 0.0632 0.625 0.000 0.000 0.976 0.000 0.024 NA
#> ERR978125 3 0.0632 0.625 0.000 0.000 0.976 0.000 0.024 NA
#> ERR978126 3 0.0632 0.625 0.000 0.000 0.976 0.000 0.024 NA
#> ERR978127 3 0.0632 0.625 0.000 0.000 0.976 0.000 0.024 NA
#> ERR978128 3 0.0632 0.625 0.000 0.000 0.976 0.000 0.024 NA
#> ERR978129 3 0.0632 0.625 0.000 0.000 0.976 0.000 0.024 NA
#> ERR978130 3 0.0632 0.625 0.000 0.000 0.976 0.000 0.024 NA
#> ERR978131 3 0.1003 0.624 0.000 0.004 0.964 0.000 0.028 NA
#> ERR978132 3 0.1003 0.624 0.000 0.004 0.964 0.000 0.028 NA
#> ERR978133 3 0.1003 0.624 0.000 0.004 0.964 0.000 0.028 NA
#> ERR978134 3 0.1003 0.624 0.000 0.004 0.964 0.000 0.028 NA
#> ERR978135 3 0.1003 0.624 0.000 0.004 0.964 0.000 0.028 NA
#> ERR978136 3 0.1003 0.624 0.000 0.004 0.964 0.000 0.028 NA
#> ERR978137 3 0.1003 0.624 0.000 0.004 0.964 0.000 0.028 NA
#> ERR978138 3 0.6615 0.638 0.000 0.052 0.596 0.172 0.056 NA
#> ERR978139 3 0.6615 0.638 0.000 0.052 0.596 0.172 0.056 NA
#> ERR978140 3 0.6615 0.638 0.000 0.052 0.596 0.172 0.056 NA
#> ERR978141 3 0.6615 0.638 0.000 0.052 0.596 0.172 0.056 NA
#> ERR978142 3 0.6615 0.638 0.000 0.052 0.596 0.172 0.056 NA
#> ERR978143 3 0.6615 0.638 0.000 0.052 0.596 0.172 0.056 NA
#> ERR978144 3 0.6615 0.638 0.000 0.052 0.596 0.172 0.056 NA
#> ERR978145 3 0.6615 0.638 0.000 0.052 0.596 0.172 0.056 NA
#> ERR978146 3 0.6502 0.639 0.000 0.044 0.604 0.172 0.056 NA
#> ERR978147 3 0.6502 0.639 0.000 0.044 0.604 0.172 0.056 NA
#> ERR978148 3 0.6502 0.639 0.000 0.044 0.604 0.172 0.056 NA
#> ERR978149 3 0.6502 0.639 0.000 0.044 0.604 0.172 0.056 NA
#> ERR978150 3 0.6502 0.639 0.000 0.044 0.604 0.172 0.056 NA
#> ERR978151 3 0.6502 0.639 0.000 0.044 0.604 0.172 0.056 NA
#> ERR978152 3 0.6502 0.639 0.000 0.044 0.604 0.172 0.056 NA
#> ERR978153 1 0.0260 0.857 0.992 0.000 0.000 0.008 0.000 NA
#> ERR978154 1 0.0260 0.857 0.992 0.000 0.000 0.008 0.000 NA
#> ERR978155 1 0.0260 0.857 0.992 0.000 0.000 0.008 0.000 NA
#> ERR978156 1 0.0260 0.857 0.992 0.000 0.000 0.008 0.000 NA
#> ERR978157 1 0.0260 0.857 0.992 0.000 0.000 0.008 0.000 NA
#> ERR978158 1 0.0260 0.857 0.992 0.000 0.000 0.008 0.000 NA
#> ERR978159 1 0.0260 0.857 0.992 0.000 0.000 0.008 0.000 NA
#> ERR978160 1 0.0260 0.857 0.992 0.008 0.000 0.000 0.000 NA
#> ERR978161 1 0.1370 0.857 0.948 0.036 0.004 0.012 0.000 NA
#> ERR978162 1 0.1370 0.857 0.948 0.036 0.004 0.012 0.000 NA
#> ERR978163 1 0.1296 0.857 0.948 0.044 0.004 0.004 0.000 NA
#> ERR978164 1 0.1296 0.857 0.948 0.044 0.004 0.004 0.000 NA
#> ERR978165 1 0.1296 0.857 0.948 0.044 0.004 0.004 0.000 NA
#> ERR978166 1 0.1296 0.857 0.948 0.044 0.004 0.004 0.000 NA
#> ERR978167 1 0.1296 0.857 0.948 0.044 0.004 0.004 0.000 NA
#> ERR978168 1 0.1370 0.857 0.948 0.036 0.004 0.012 0.000 NA
#> ERR978169 4 0.3917 0.960 0.000 0.024 0.124 0.792 0.000 NA
#> ERR978170 4 0.3917 0.960 0.000 0.024 0.124 0.792 0.000 NA
#> ERR978171 4 0.3917 0.960 0.000 0.024 0.124 0.792 0.000 NA
#> ERR978172 4 0.3917 0.960 0.000 0.024 0.124 0.792 0.000 NA
#> ERR978173 4 0.3917 0.960 0.000 0.024 0.124 0.792 0.000 NA
#> ERR978174 4 0.3917 0.960 0.000 0.024 0.124 0.792 0.000 NA
#> ERR978175 4 0.3935 0.959 0.000 0.028 0.124 0.792 0.000 NA
#> ERR978176 4 0.2003 0.960 0.000 0.000 0.116 0.884 0.000 NA
#> ERR978177 4 0.2003 0.960 0.000 0.000 0.116 0.884 0.000 NA
#> ERR978178 4 0.2003 0.960 0.000 0.000 0.116 0.884 0.000 NA
#> ERR978179 4 0.2003 0.960 0.000 0.000 0.116 0.884 0.000 NA
#> ERR978180 4 0.2003 0.960 0.000 0.000 0.116 0.884 0.000 NA
#> ERR978181 4 0.2003 0.960 0.000 0.000 0.116 0.884 0.000 NA
#> ERR978182 4 0.2146 0.959 0.000 0.004 0.116 0.880 0.000 NA
#> ERR978183 2 0.3695 0.830 0.000 0.712 0.000 0.016 0.272 NA
#> ERR978184 2 0.3695 0.830 0.000 0.712 0.000 0.016 0.272 NA
#> ERR978185 2 0.3695 0.830 0.000 0.712 0.000 0.016 0.272 NA
#> ERR978186 2 0.3695 0.830 0.000 0.712 0.000 0.016 0.272 NA
#> ERR978187 2 0.3695 0.830 0.000 0.712 0.000 0.016 0.272 NA
#> ERR978188 2 0.3695 0.830 0.000 0.712 0.000 0.016 0.272 NA
#> ERR978189 2 0.3695 0.830 0.000 0.712 0.000 0.016 0.272 NA
#> ERR978190 2 0.4327 0.830 0.000 0.680 0.000 0.000 0.264 NA
#> ERR978191 2 0.4327 0.830 0.000 0.680 0.000 0.000 0.264 NA
#> ERR978192 2 0.4327 0.830 0.000 0.680 0.000 0.000 0.264 NA
#> ERR978193 2 0.4327 0.830 0.000 0.680 0.000 0.000 0.264 NA
#> ERR978194 2 0.4327 0.830 0.000 0.680 0.000 0.000 0.264 NA
#> ERR978195 2 0.4327 0.830 0.000 0.680 0.000 0.000 0.264 NA
#> ERR978196 2 0.4327 0.830 0.000 0.680 0.000 0.000 0.264 NA
#> ERR978197 5 0.3996 0.829 0.000 0.036 0.152 0.012 0.784 NA
#> ERR978198 5 0.3996 0.829 0.000 0.036 0.152 0.012 0.784 NA
#> ERR978199 5 0.3996 0.829 0.000 0.036 0.152 0.012 0.784 NA
#> ERR978200 5 0.3996 0.829 0.000 0.036 0.152 0.012 0.784 NA
#> ERR978201 5 0.3996 0.829 0.000 0.036 0.152 0.012 0.784 NA
#> ERR978202 5 0.3996 0.829 0.000 0.036 0.152 0.012 0.784 NA
#> ERR978203 5 0.3996 0.829 0.000 0.036 0.152 0.012 0.784 NA
#> ERR978204 5 0.3201 0.845 0.000 0.028 0.140 0.008 0.824 NA
#> ERR978205 5 0.3201 0.845 0.000 0.028 0.140 0.008 0.824 NA
#> ERR978206 5 0.3201 0.845 0.000 0.028 0.140 0.008 0.824 NA
#> ERR978207 5 0.3201 0.845 0.000 0.028 0.140 0.008 0.824 NA
#> ERR978208 5 0.3201 0.845 0.000 0.028 0.140 0.008 0.824 NA
#> ERR978209 5 0.3201 0.845 0.000 0.028 0.140 0.008 0.824 NA
#> ERR978210 5 0.3201 0.845 0.000 0.028 0.140 0.008 0.824 NA
#> ERR978211 5 0.3201 0.845 0.000 0.028 0.140 0.008 0.824 NA
#> ERR978212 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978213 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978214 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978215 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978216 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978217 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978218 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978219 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978220 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978221 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978222 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978223 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978224 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978225 5 0.4457 0.850 0.000 0.052 0.080 0.012 0.780 NA
#> ERR978226 5 0.4508 0.848 0.000 0.052 0.080 0.012 0.776 NA
#> ERR978227 1 0.3888 0.841 0.672 0.016 0.000 0.000 0.000 NA
#> ERR978228 1 0.3888 0.841 0.672 0.016 0.000 0.000 0.000 NA
#> ERR978229 1 0.3888 0.841 0.672 0.016 0.000 0.000 0.000 NA
#> ERR978230 1 0.3888 0.841 0.672 0.016 0.000 0.000 0.000 NA
#> ERR978231 1 0.3888 0.841 0.672 0.016 0.000 0.000 0.000 NA
#> ERR978232 1 0.3888 0.841 0.672 0.016 0.000 0.000 0.000 NA
#> ERR978233 1 0.3888 0.841 0.672 0.016 0.000 0.000 0.000 NA
#> ERR978234 1 0.4159 0.841 0.672 0.000 0.000 0.008 0.020 NA
#> ERR978235 1 0.4159 0.841 0.672 0.000 0.000 0.008 0.020 NA
#> ERR978236 1 0.4159 0.841 0.672 0.000 0.000 0.008 0.020 NA
#> ERR978237 1 0.4159 0.841 0.672 0.000 0.000 0.008 0.020 NA
#> ERR978238 1 0.4159 0.841 0.672 0.000 0.000 0.008 0.020 NA
#> ERR978239 1 0.4159 0.841 0.672 0.000 0.000 0.008 0.020 NA
#> ERR978240 1 0.4190 0.841 0.672 0.004 0.000 0.004 0.020 NA
#> ERR978241 3 0.7182 0.529 0.000 0.060 0.492 0.256 0.044 NA
#> ERR978242 3 0.7182 0.529 0.000 0.060 0.492 0.256 0.044 NA
#> ERR978243 3 0.7182 0.529 0.000 0.060 0.492 0.256 0.044 NA
#> ERR978244 3 0.7182 0.529 0.000 0.060 0.492 0.256 0.044 NA
#> ERR978245 3 0.7182 0.529 0.000 0.060 0.492 0.256 0.044 NA
#> ERR978246 3 0.7182 0.529 0.000 0.060 0.492 0.256 0.044 NA
#> ERR978247 3 0.7182 0.529 0.000 0.060 0.492 0.256 0.044 NA
#> ERR978248 3 0.7745 0.214 0.000 0.108 0.348 0.020 0.268 NA
#> ERR978249 3 0.7725 0.214 0.000 0.104 0.348 0.020 0.268 NA
#> ERR978250 3 0.7725 0.214 0.000 0.104 0.348 0.020 0.268 NA
#> ERR978251 3 0.7725 0.214 0.000 0.104 0.348 0.020 0.268 NA
#> ERR978252 3 0.7725 0.214 0.000 0.104 0.348 0.020 0.268 NA
#> ERR978253 3 0.7725 0.214 0.000 0.104 0.348 0.020 0.268 NA
#> ERR978254 3 0.7745 0.214 0.000 0.108 0.348 0.020 0.268 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "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 14049 rows and 148 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 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 0.904 0.932 0.964 0.4975 0.501 0.501
#> 3 3 1.000 1.000 1.000 0.2862 0.859 0.719
#> 4 4 0.950 0.960 0.968 0.0838 0.950 0.862
#> 5 5 0.854 0.898 0.855 0.1006 0.898 0.672
#> 6 6 0.818 0.913 0.873 0.0545 0.981 0.908
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
#> ERR978107 2 0.0000 1.000 0.000 1.000
#> ERR978108 2 0.0000 1.000 0.000 1.000
#> ERR978109 2 0.0000 1.000 0.000 1.000
#> ERR978110 2 0.0000 1.000 0.000 1.000
#> ERR978111 2 0.0000 1.000 0.000 1.000
#> ERR978112 2 0.0000 1.000 0.000 1.000
#> ERR978113 2 0.0000 1.000 0.000 1.000
#> ERR978114 2 0.0000 1.000 0.000 1.000
#> ERR978115 2 0.0000 1.000 0.000 1.000
#> ERR978116 2 0.0000 1.000 0.000 1.000
#> ERR978117 2 0.0000 1.000 0.000 1.000
#> ERR978118 2 0.0000 1.000 0.000 1.000
#> ERR978119 2 0.0000 1.000 0.000 1.000
#> ERR978120 2 0.0000 1.000 0.000 1.000
#> ERR978121 2 0.0000 1.000 0.000 1.000
#> ERR978122 2 0.0000 1.000 0.000 1.000
#> ERR978123 1 0.2043 0.921 0.968 0.032
#> ERR978124 1 0.2043 0.921 0.968 0.032
#> ERR978125 1 0.2043 0.921 0.968 0.032
#> ERR978126 1 0.2043 0.921 0.968 0.032
#> ERR978127 1 0.2043 0.921 0.968 0.032
#> ERR978128 1 0.2043 0.921 0.968 0.032
#> ERR978129 1 0.2043 0.921 0.968 0.032
#> ERR978130 1 0.2043 0.921 0.968 0.032
#> ERR978131 1 0.2778 0.912 0.952 0.048
#> ERR978132 1 0.2778 0.912 0.952 0.048
#> ERR978133 1 0.2778 0.912 0.952 0.048
#> ERR978134 1 0.2778 0.912 0.952 0.048
#> ERR978135 1 0.2778 0.912 0.952 0.048
#> ERR978136 1 0.2778 0.912 0.952 0.048
#> ERR978137 1 0.2778 0.912 0.952 0.048
#> ERR978138 1 0.9129 0.615 0.672 0.328
#> ERR978139 1 0.9129 0.615 0.672 0.328
#> ERR978140 1 0.9129 0.615 0.672 0.328
#> ERR978141 1 0.9129 0.615 0.672 0.328
#> ERR978142 1 0.9129 0.615 0.672 0.328
#> ERR978143 1 0.9129 0.615 0.672 0.328
#> ERR978144 1 0.9129 0.615 0.672 0.328
#> ERR978145 1 0.9129 0.615 0.672 0.328
#> ERR978146 1 0.8713 0.671 0.708 0.292
#> ERR978147 1 0.8713 0.671 0.708 0.292
#> ERR978148 1 0.8713 0.671 0.708 0.292
#> ERR978149 1 0.8713 0.671 0.708 0.292
#> ERR978150 1 0.8713 0.671 0.708 0.292
#> ERR978151 1 0.8713 0.671 0.708 0.292
#> ERR978152 1 0.8713 0.671 0.708 0.292
#> ERR978153 1 0.0000 0.932 1.000 0.000
#> ERR978154 1 0.0000 0.932 1.000 0.000
#> ERR978155 1 0.0000 0.932 1.000 0.000
#> ERR978156 1 0.0000 0.932 1.000 0.000
#> ERR978157 1 0.0000 0.932 1.000 0.000
#> ERR978158 1 0.0000 0.932 1.000 0.000
#> ERR978159 1 0.0000 0.932 1.000 0.000
#> ERR978160 1 0.0000 0.932 1.000 0.000
#> ERR978161 1 0.0000 0.932 1.000 0.000
#> ERR978162 1 0.0000 0.932 1.000 0.000
#> ERR978163 1 0.0000 0.932 1.000 0.000
#> ERR978164 1 0.0000 0.932 1.000 0.000
#> ERR978165 1 0.0000 0.932 1.000 0.000
#> ERR978166 1 0.0000 0.932 1.000 0.000
#> ERR978167 1 0.0000 0.932 1.000 0.000
#> ERR978168 1 0.0000 0.932 1.000 0.000
#> ERR978169 1 0.0000 0.932 1.000 0.000
#> ERR978170 1 0.0000 0.932 1.000 0.000
#> ERR978171 1 0.0000 0.932 1.000 0.000
#> ERR978172 1 0.0000 0.932 1.000 0.000
#> ERR978173 1 0.0000 0.932 1.000 0.000
#> ERR978174 1 0.0000 0.932 1.000 0.000
#> ERR978175 1 0.0000 0.932 1.000 0.000
#> ERR978176 1 0.0000 0.932 1.000 0.000
#> ERR978177 1 0.0000 0.932 1.000 0.000
#> ERR978178 1 0.0000 0.932 1.000 0.000
#> ERR978179 1 0.0000 0.932 1.000 0.000
#> ERR978180 1 0.0000 0.932 1.000 0.000
#> ERR978181 1 0.0000 0.932 1.000 0.000
#> ERR978182 1 0.0000 0.932 1.000 0.000
#> ERR978183 2 0.0000 1.000 0.000 1.000
#> ERR978184 2 0.0000 1.000 0.000 1.000
#> ERR978185 2 0.0000 1.000 0.000 1.000
#> ERR978186 2 0.0000 1.000 0.000 1.000
#> ERR978187 2 0.0000 1.000 0.000 1.000
#> ERR978188 2 0.0000 1.000 0.000 1.000
#> ERR978189 2 0.0000 1.000 0.000 1.000
#> ERR978190 2 0.0000 1.000 0.000 1.000
#> ERR978191 2 0.0000 1.000 0.000 1.000
#> ERR978192 2 0.0000 1.000 0.000 1.000
#> ERR978193 2 0.0000 1.000 0.000 1.000
#> ERR978194 2 0.0000 1.000 0.000 1.000
#> ERR978195 2 0.0000 1.000 0.000 1.000
#> ERR978196 2 0.0000 1.000 0.000 1.000
#> ERR978197 2 0.0000 1.000 0.000 1.000
#> ERR978198 2 0.0000 1.000 0.000 1.000
#> ERR978199 2 0.0000 1.000 0.000 1.000
#> ERR978200 2 0.0000 1.000 0.000 1.000
#> ERR978201 2 0.0000 1.000 0.000 1.000
#> ERR978202 2 0.0000 1.000 0.000 1.000
#> ERR978203 2 0.0000 1.000 0.000 1.000
#> ERR978204 2 0.0000 1.000 0.000 1.000
#> ERR978205 2 0.0000 1.000 0.000 1.000
#> ERR978206 2 0.0000 1.000 0.000 1.000
#> ERR978207 2 0.0000 1.000 0.000 1.000
#> ERR978208 2 0.0000 1.000 0.000 1.000
#> ERR978209 2 0.0000 1.000 0.000 1.000
#> ERR978210 2 0.0000 1.000 0.000 1.000
#> ERR978211 2 0.0000 1.000 0.000 1.000
#> ERR978212 2 0.0000 1.000 0.000 1.000
#> ERR978213 2 0.0000 1.000 0.000 1.000
#> ERR978214 2 0.0000 1.000 0.000 1.000
#> ERR978215 2 0.0000 1.000 0.000 1.000
#> ERR978216 2 0.0000 1.000 0.000 1.000
#> ERR978217 2 0.0000 1.000 0.000 1.000
#> ERR978218 2 0.0000 1.000 0.000 1.000
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#> ERR978221 2 0.0000 1.000 0.000 1.000
#> ERR978222 2 0.0000 1.000 0.000 1.000
#> ERR978223 2 0.0000 1.000 0.000 1.000
#> ERR978224 2 0.0000 1.000 0.000 1.000
#> ERR978225 2 0.0000 1.000 0.000 1.000
#> ERR978226 2 0.0000 1.000 0.000 1.000
#> ERR978227 1 0.0000 0.932 1.000 0.000
#> ERR978228 1 0.0000 0.932 1.000 0.000
#> ERR978229 1 0.0000 0.932 1.000 0.000
#> ERR978230 1 0.0000 0.932 1.000 0.000
#> ERR978231 1 0.0000 0.932 1.000 0.000
#> ERR978232 1 0.0000 0.932 1.000 0.000
#> ERR978233 1 0.0000 0.932 1.000 0.000
#> ERR978234 1 0.0000 0.932 1.000 0.000
#> ERR978235 1 0.0000 0.932 1.000 0.000
#> ERR978236 1 0.0000 0.932 1.000 0.000
#> ERR978237 1 0.0000 0.932 1.000 0.000
#> ERR978238 1 0.0000 0.932 1.000 0.000
#> ERR978239 1 0.0000 0.932 1.000 0.000
#> ERR978240 1 0.0000 0.932 1.000 0.000
#> ERR978241 1 0.0376 0.931 0.996 0.004
#> ERR978242 1 0.0376 0.931 0.996 0.004
#> ERR978243 1 0.0376 0.931 0.996 0.004
#> ERR978244 1 0.0376 0.931 0.996 0.004
#> ERR978245 1 0.0376 0.931 0.996 0.004
#> ERR978246 1 0.0376 0.931 0.996 0.004
#> ERR978247 1 0.0376 0.931 0.996 0.004
#> ERR978248 2 0.0000 1.000 0.000 1.000
#> ERR978249 2 0.0000 1.000 0.000 1.000
#> ERR978250 2 0.0000 1.000 0.000 1.000
#> ERR978251 2 0.0000 1.000 0.000 1.000
#> ERR978252 2 0.0000 1.000 0.000 1.000
#> ERR978253 2 0.0000 1.000 0.000 1.000
#> ERR978254 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0 1 0 1 0
#> ERR978108 2 0 1 0 1 0
#> ERR978109 2 0 1 0 1 0
#> ERR978110 2 0 1 0 1 0
#> ERR978111 2 0 1 0 1 0
#> ERR978112 2 0 1 0 1 0
#> ERR978113 2 0 1 0 1 0
#> ERR978114 2 0 1 0 1 0
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#> ERR978247 3 0 1 0 0 1
#> ERR978248 2 0 1 0 1 0
#> ERR978249 2 0 1 0 1 0
#> ERR978250 2 0 1 0 1 0
#> ERR978251 2 0 1 0 1 0
#> ERR978252 2 0 1 0 1 0
#> ERR978253 2 0 1 0 1 0
#> ERR978254 2 0 1 0 1 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978108 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978109 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978110 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978111 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978112 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978113 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978114 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978115 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978116 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978117 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978118 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978119 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978120 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978121 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978122 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978123 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978124 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978125 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978126 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978127 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978128 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978129 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978130 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978131 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978132 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978133 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978134 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978135 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978136 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978137 3 0.1022 1.000 0 0.000 0.968 0.032
#> ERR978138 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978139 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978140 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978141 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978142 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978143 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978144 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978145 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978146 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978147 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978148 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978149 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978150 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978151 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978152 4 0.3649 0.839 0 0.000 0.204 0.796
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978170 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978171 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978172 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978173 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978174 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978175 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978176 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978177 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978178 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978179 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978180 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978181 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978182 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978183 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978184 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978185 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978186 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978187 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978188 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978189 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978190 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978191 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978192 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978193 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978194 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978195 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978196 2 0.0000 0.984 0 1.000 0.000 0.000
#> ERR978197 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978198 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978199 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978200 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978201 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978202 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978203 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978204 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978205 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978206 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978207 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978208 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978209 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978210 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978211 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978212 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978213 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978214 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978215 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978216 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978217 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978218 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978219 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978220 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978221 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978222 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978223 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978224 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978225 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978226 2 0.0921 0.983 0 0.972 0.028 0.000
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978241 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978242 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978243 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978244 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978245 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978246 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978247 4 0.0000 0.899 0 0.000 0.000 1.000
#> ERR978248 2 0.1398 0.954 0 0.956 0.004 0.040
#> ERR978249 2 0.1398 0.954 0 0.956 0.004 0.040
#> ERR978250 2 0.1398 0.954 0 0.956 0.004 0.040
#> ERR978251 2 0.1398 0.954 0 0.956 0.004 0.040
#> ERR978252 2 0.1398 0.954 0 0.956 0.004 0.040
#> ERR978253 2 0.1398 0.954 0 0.956 0.004 0.040
#> ERR978254 2 0.1398 0.954 0 0.956 0.004 0.040
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978123 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978124 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978125 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978126 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978127 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978128 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978129 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978130 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978131 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978132 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978133 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978134 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978135 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978136 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978137 3 0.0000 1.000 0 0.000 1.000 0.000 0.000
#> ERR978138 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978139 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978140 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978141 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978142 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978143 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978144 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978145 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978146 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978147 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978148 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978149 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978150 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978151 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978152 4 0.5342 0.771 0 0.000 0.172 0.672 0.156
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978170 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978171 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978172 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978173 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978174 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978175 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978176 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978177 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978178 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978179 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978180 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978181 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978182 4 0.0000 0.854 0 0.000 0.000 1.000 0.000
#> ERR978183 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978184 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978185 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978186 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978187 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978188 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978189 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978190 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978191 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978192 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978193 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978194 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978195 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978196 2 0.0000 0.865 0 1.000 0.000 0.000 0.000
#> ERR978197 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978198 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978199 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978200 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978201 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978202 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978203 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978204 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978205 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978206 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978207 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978208 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978209 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978210 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978211 5 0.4443 0.968 0 0.472 0.004 0.000 0.524
#> ERR978212 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978213 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978214 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978215 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978216 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978217 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978218 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978219 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978220 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978221 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978222 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978223 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978224 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978225 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978226 5 0.4291 0.968 0 0.464 0.000 0.000 0.536
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978241 4 0.0609 0.847 0 0.000 0.000 0.980 0.020
#> ERR978242 4 0.0609 0.847 0 0.000 0.000 0.980 0.020
#> ERR978243 4 0.0609 0.847 0 0.000 0.000 0.980 0.020
#> ERR978244 4 0.0609 0.847 0 0.000 0.000 0.980 0.020
#> ERR978245 4 0.0609 0.847 0 0.000 0.000 0.980 0.020
#> ERR978246 4 0.0609 0.847 0 0.000 0.000 0.980 0.020
#> ERR978247 4 0.0609 0.847 0 0.000 0.000 0.980 0.020
#> ERR978248 2 0.4856 0.489 0 0.584 0.000 0.028 0.388
#> ERR978249 2 0.4856 0.489 0 0.584 0.000 0.028 0.388
#> ERR978250 2 0.4856 0.489 0 0.584 0.000 0.028 0.388
#> ERR978251 2 0.4856 0.489 0 0.584 0.000 0.028 0.388
#> ERR978252 2 0.4856 0.489 0 0.584 0.000 0.028 0.388
#> ERR978253 2 0.4856 0.489 0 0.584 0.000 0.028 0.388
#> ERR978254 2 0.4856 0.489 0 0.584 0.000 0.028 0.388
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.2491 0.997 0.000 0.836 0.000 0.000 0.164 0.000
#> ERR978108 2 0.2491 0.997 0.000 0.836 0.000 0.000 0.164 0.000
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#> ERR978241 4 0.3847 0.737 0.000 0.000 0.000 0.544 0.000 0.456
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#> ERR978248 6 0.5227 1.000 0.000 0.368 0.000 0.004 0.088 0.540
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#> ERR978254 6 0.5227 1.000 0.000 0.368 0.000 0.004 0.088 0.540
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 14049 rows and 148 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.3261 0.675 0.675
#> 3 3 0.835 0.842 0.941 0.9643 0.681 0.527
#> 4 4 0.876 0.893 0.956 0.1018 0.852 0.629
#> 5 5 1.000 0.983 0.993 0.1177 0.854 0.551
#> 6 6 1.000 0.989 0.994 0.0403 0.970 0.856
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 5
There is also optional best \(k\) = 2 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
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#> ERR978200 2 0 1 0 1
#> ERR978201 2 0 1 0 1
#> ERR978202 2 0 1 0 1
#> ERR978203 2 0 1 0 1
#> ERR978204 2 0 1 0 1
#> ERR978205 2 0 1 0 1
#> ERR978206 2 0 1 0 1
#> ERR978207 2 0 1 0 1
#> ERR978208 2 0 1 0 1
#> ERR978209 2 0 1 0 1
#> ERR978210 2 0 1 0 1
#> ERR978211 2 0 1 0 1
#> ERR978212 2 0 1 0 1
#> ERR978213 2 0 1 0 1
#> ERR978214 2 0 1 0 1
#> ERR978215 2 0 1 0 1
#> ERR978216 2 0 1 0 1
#> ERR978217 2 0 1 0 1
#> ERR978218 2 0 1 0 1
#> ERR978219 2 0 1 0 1
#> ERR978220 2 0 1 0 1
#> ERR978221 2 0 1 0 1
#> ERR978222 2 0 1 0 1
#> ERR978223 2 0 1 0 1
#> ERR978224 2 0 1 0 1
#> ERR978225 2 0 1 0 1
#> ERR978226 2 0 1 0 1
#> ERR978227 1 0 1 1 0
#> ERR978228 1 0 1 1 0
#> ERR978229 1 0 1 1 0
#> ERR978230 1 0 1 1 0
#> ERR978231 1 0 1 1 0
#> ERR978232 1 0 1 1 0
#> ERR978233 1 0 1 1 0
#> ERR978234 1 0 1 1 0
#> ERR978235 1 0 1 1 0
#> ERR978236 1 0 1 1 0
#> ERR978237 1 0 1 1 0
#> ERR978238 1 0 1 1 0
#> ERR978239 1 0 1 1 0
#> ERR978240 1 0 1 1 0
#> ERR978241 2 0 1 0 1
#> ERR978242 2 0 1 0 1
#> ERR978243 2 0 1 0 1
#> ERR978244 2 0 1 0 1
#> ERR978245 2 0 1 0 1
#> ERR978246 2 0 1 0 1
#> ERR978247 2 0 1 0 1
#> ERR978248 2 0 1 0 1
#> ERR978249 2 0 1 0 1
#> ERR978250 2 0 1 0 1
#> ERR978251 2 0 1 0 1
#> ERR978252 2 0 1 0 1
#> ERR978253 2 0 1 0 1
#> ERR978254 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.0000 0.89410 0 1.000 0.000
#> ERR978108 2 0.0000 0.89410 0 1.000 0.000
#> ERR978109 2 0.0000 0.89410 0 1.000 0.000
#> ERR978110 2 0.0000 0.89410 0 1.000 0.000
#> ERR978111 2 0.0000 0.89410 0 1.000 0.000
#> ERR978112 2 0.0000 0.89410 0 1.000 0.000
#> ERR978113 2 0.0000 0.89410 0 1.000 0.000
#> ERR978114 2 0.0000 0.89410 0 1.000 0.000
#> ERR978115 2 0.0000 0.89410 0 1.000 0.000
#> ERR978116 2 0.0000 0.89410 0 1.000 0.000
#> ERR978117 2 0.0000 0.89410 0 1.000 0.000
#> ERR978118 2 0.0000 0.89410 0 1.000 0.000
#> ERR978119 2 0.0000 0.89410 0 1.000 0.000
#> ERR978120 2 0.0000 0.89410 0 1.000 0.000
#> ERR978121 2 0.0000 0.89410 0 1.000 0.000
#> ERR978122 2 0.0000 0.89410 0 1.000 0.000
#> ERR978123 3 0.0000 0.93000 0 0.000 1.000
#> ERR978124 3 0.0000 0.93000 0 0.000 1.000
#> ERR978125 3 0.0000 0.93000 0 0.000 1.000
#> ERR978126 3 0.0000 0.93000 0 0.000 1.000
#> ERR978127 3 0.0000 0.93000 0 0.000 1.000
#> ERR978128 3 0.0000 0.93000 0 0.000 1.000
#> ERR978129 3 0.0000 0.93000 0 0.000 1.000
#> ERR978130 3 0.0000 0.93000 0 0.000 1.000
#> ERR978131 3 0.0000 0.93000 0 0.000 1.000
#> ERR978132 3 0.0000 0.93000 0 0.000 1.000
#> ERR978133 3 0.0000 0.93000 0 0.000 1.000
#> ERR978134 3 0.0000 0.93000 0 0.000 1.000
#> ERR978135 3 0.0000 0.93000 0 0.000 1.000
#> ERR978136 3 0.0000 0.93000 0 0.000 1.000
#> ERR978137 3 0.0000 0.93000 0 0.000 1.000
#> ERR978138 3 0.0000 0.93000 0 0.000 1.000
#> ERR978139 3 0.0000 0.93000 0 0.000 1.000
#> ERR978140 3 0.0000 0.93000 0 0.000 1.000
#> ERR978141 3 0.0000 0.93000 0 0.000 1.000
#> ERR978142 3 0.0000 0.93000 0 0.000 1.000
#> ERR978143 3 0.0000 0.93000 0 0.000 1.000
#> ERR978144 3 0.0000 0.93000 0 0.000 1.000
#> ERR978145 3 0.0000 0.93000 0 0.000 1.000
#> ERR978146 3 0.0000 0.93000 0 0.000 1.000
#> ERR978147 3 0.0000 0.93000 0 0.000 1.000
#> ERR978148 3 0.0000 0.93000 0 0.000 1.000
#> ERR978149 3 0.0000 0.93000 0 0.000 1.000
#> ERR978150 3 0.0000 0.93000 0 0.000 1.000
#> ERR978151 3 0.0000 0.93000 0 0.000 1.000
#> ERR978152 3 0.0000 0.93000 0 0.000 1.000
#> ERR978153 1 0.0000 1.00000 1 0.000 0.000
#> ERR978154 1 0.0000 1.00000 1 0.000 0.000
#> ERR978155 1 0.0000 1.00000 1 0.000 0.000
#> ERR978156 1 0.0000 1.00000 1 0.000 0.000
#> ERR978157 1 0.0000 1.00000 1 0.000 0.000
#> ERR978158 1 0.0000 1.00000 1 0.000 0.000
#> ERR978159 1 0.0000 1.00000 1 0.000 0.000
#> ERR978160 1 0.0000 1.00000 1 0.000 0.000
#> ERR978161 1 0.0000 1.00000 1 0.000 0.000
#> ERR978162 1 0.0000 1.00000 1 0.000 0.000
#> ERR978163 1 0.0000 1.00000 1 0.000 0.000
#> ERR978164 1 0.0000 1.00000 1 0.000 0.000
#> ERR978165 1 0.0000 1.00000 1 0.000 0.000
#> ERR978166 1 0.0000 1.00000 1 0.000 0.000
#> ERR978167 1 0.0000 1.00000 1 0.000 0.000
#> ERR978168 1 0.0000 1.00000 1 0.000 0.000
#> ERR978169 3 0.0000 0.93000 0 0.000 1.000
#> ERR978170 3 0.0000 0.93000 0 0.000 1.000
#> ERR978171 3 0.0000 0.93000 0 0.000 1.000
#> ERR978172 3 0.0000 0.93000 0 0.000 1.000
#> ERR978173 3 0.0000 0.93000 0 0.000 1.000
#> ERR978174 3 0.0000 0.93000 0 0.000 1.000
#> ERR978175 3 0.0000 0.93000 0 0.000 1.000
#> ERR978176 3 0.0000 0.93000 0 0.000 1.000
#> ERR978177 3 0.0000 0.93000 0 0.000 1.000
#> ERR978178 3 0.0000 0.93000 0 0.000 1.000
#> ERR978179 3 0.0000 0.93000 0 0.000 1.000
#> ERR978180 3 0.0000 0.93000 0 0.000 1.000
#> ERR978181 3 0.0000 0.93000 0 0.000 1.000
#> ERR978182 3 0.0000 0.93000 0 0.000 1.000
#> ERR978183 2 0.0000 0.89410 0 1.000 0.000
#> ERR978184 2 0.0000 0.89410 0 1.000 0.000
#> ERR978185 2 0.0000 0.89410 0 1.000 0.000
#> ERR978186 2 0.0000 0.89410 0 1.000 0.000
#> ERR978187 2 0.0000 0.89410 0 1.000 0.000
#> ERR978188 2 0.0000 0.89410 0 1.000 0.000
#> ERR978189 2 0.0000 0.89410 0 1.000 0.000
#> ERR978190 2 0.0000 0.89410 0 1.000 0.000
#> ERR978191 2 0.0000 0.89410 0 1.000 0.000
#> ERR978192 2 0.0000 0.89410 0 1.000 0.000
#> ERR978193 2 0.0000 0.89410 0 1.000 0.000
#> ERR978194 2 0.0000 0.89410 0 1.000 0.000
#> ERR978195 2 0.0000 0.89410 0 1.000 0.000
#> ERR978196 2 0.0000 0.89410 0 1.000 0.000
#> ERR978197 2 0.0000 0.89410 0 1.000 0.000
#> ERR978198 2 0.0592 0.88639 0 0.988 0.012
#> ERR978199 2 0.4121 0.74965 0 0.832 0.168
#> ERR978200 2 0.4750 0.69353 0 0.784 0.216
#> ERR978201 2 0.1643 0.86434 0 0.956 0.044
#> ERR978202 2 0.0000 0.89410 0 1.000 0.000
#> ERR978203 2 0.0000 0.89410 0 1.000 0.000
#> ERR978204 2 0.0000 0.89410 0 1.000 0.000
#> ERR978205 2 0.0000 0.89410 0 1.000 0.000
#> ERR978206 2 0.0000 0.89410 0 1.000 0.000
#> ERR978207 2 0.0000 0.89410 0 1.000 0.000
#> ERR978208 2 0.0000 0.89410 0 1.000 0.000
#> ERR978209 2 0.0000 0.89410 0 1.000 0.000
#> ERR978210 2 0.0000 0.89410 0 1.000 0.000
#> ERR978211 2 0.0000 0.89410 0 1.000 0.000
#> ERR978212 2 0.6309 0.06953 0 0.504 0.496
#> ERR978213 3 0.5948 0.39127 0 0.360 0.640
#> ERR978214 3 0.5650 0.50578 0 0.312 0.688
#> ERR978215 3 0.5529 0.53916 0 0.296 0.704
#> ERR978216 3 0.5810 0.45135 0 0.336 0.664
#> ERR978217 3 0.6302 -0.00608 0 0.480 0.520
#> ERR978218 2 0.6308 0.08461 0 0.508 0.492
#> ERR978219 2 0.4555 0.71266 0 0.800 0.200
#> ERR978220 2 0.6140 0.34615 0 0.596 0.404
#> ERR978221 2 0.6308 0.08461 0 0.508 0.492
#> ERR978222 3 0.6299 0.01032 0 0.476 0.524
#> ERR978223 2 0.6308 0.08461 0 0.508 0.492
#> ERR978224 2 0.6192 0.30544 0 0.580 0.420
#> ERR978225 2 0.4702 0.69781 0 0.788 0.212
#> ERR978226 2 0.6008 0.41984 0 0.628 0.372
#> ERR978227 1 0.0000 1.00000 1 0.000 0.000
#> ERR978228 1 0.0000 1.00000 1 0.000 0.000
#> ERR978229 1 0.0000 1.00000 1 0.000 0.000
#> ERR978230 1 0.0000 1.00000 1 0.000 0.000
#> ERR978231 1 0.0000 1.00000 1 0.000 0.000
#> ERR978232 1 0.0000 1.00000 1 0.000 0.000
#> ERR978233 1 0.0000 1.00000 1 0.000 0.000
#> ERR978234 1 0.0000 1.00000 1 0.000 0.000
#> ERR978235 1 0.0000 1.00000 1 0.000 0.000
#> ERR978236 1 0.0000 1.00000 1 0.000 0.000
#> ERR978237 1 0.0000 1.00000 1 0.000 0.000
#> ERR978238 1 0.0000 1.00000 1 0.000 0.000
#> ERR978239 1 0.0000 1.00000 1 0.000 0.000
#> ERR978240 1 0.0000 1.00000 1 0.000 0.000
#> ERR978241 3 0.0000 0.93000 0 0.000 1.000
#> ERR978242 3 0.0000 0.93000 0 0.000 1.000
#> ERR978243 3 0.0000 0.93000 0 0.000 1.000
#> ERR978244 3 0.0000 0.93000 0 0.000 1.000
#> ERR978245 3 0.0000 0.93000 0 0.000 1.000
#> ERR978246 3 0.0000 0.93000 0 0.000 1.000
#> ERR978247 3 0.0000 0.93000 0 0.000 1.000
#> ERR978248 2 0.6299 0.05963 0 0.524 0.476
#> ERR978249 3 0.6295 0.09706 0 0.472 0.528
#> ERR978250 3 0.4235 0.74998 0 0.176 0.824
#> ERR978251 3 0.3038 0.82806 0 0.104 0.896
#> ERR978252 3 0.4974 0.66510 0 0.236 0.764
#> ERR978253 3 0.6302 0.07012 0 0.480 0.520
#> ERR978254 2 0.6280 0.11486 0 0.540 0.460
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978108 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978109 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978110 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978111 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978112 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978113 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978114 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978115 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978116 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978117 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978118 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978119 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978120 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978121 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978122 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978123 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978124 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978125 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978126 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978127 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978128 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978129 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978130 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978131 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978132 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978133 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978134 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978135 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978136 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978137 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978138 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978139 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978140 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978141 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978142 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978143 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978144 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978145 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978146 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978147 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978148 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978149 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978150 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978151 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978152 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978169 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978170 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978171 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978172 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978173 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978174 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978175 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978176 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978177 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978178 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978179 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978180 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978181 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978182 4 0.0000 1.000 0 0.000 0.000 1
#> ERR978183 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978184 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978185 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978186 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978187 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978188 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978189 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978190 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978191 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978192 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978193 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978194 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978195 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978196 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978197 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978198 2 0.2469 0.843 0 0.892 0.108 0
#> ERR978199 2 0.4164 0.611 0 0.736 0.264 0
#> ERR978200 3 0.4989 0.161 0 0.472 0.528 0
#> ERR978201 2 0.3610 0.715 0 0.800 0.200 0
#> ERR978202 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978203 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978204 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978205 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978206 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978207 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978208 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978209 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978210 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978211 2 0.0000 0.965 0 1.000 0.000 0
#> ERR978212 3 0.4072 0.683 0 0.252 0.748 0
#> ERR978213 3 0.3356 0.765 0 0.176 0.824 0
#> ERR978214 3 0.2647 0.808 0 0.120 0.880 0
#> ERR978215 3 0.1474 0.854 0 0.052 0.948 0
#> ERR978216 3 0.3444 0.758 0 0.184 0.816 0
#> ERR978217 3 0.3649 0.741 0 0.204 0.796 0
#> ERR978218 3 0.4103 0.678 0 0.256 0.744 0
#> ERR978219 2 0.4193 0.590 0 0.732 0.268 0
#> ERR978220 3 0.4955 0.291 0 0.444 0.556 0
#> ERR978221 3 0.4382 0.617 0 0.296 0.704 0
#> ERR978222 3 0.3688 0.737 0 0.208 0.792 0
#> ERR978223 3 0.4643 0.530 0 0.344 0.656 0
#> ERR978224 3 0.4955 0.291 0 0.444 0.556 0
#> ERR978225 2 0.4776 0.318 0 0.624 0.376 0
#> ERR978226 3 0.5000 0.105 0 0.500 0.500 0
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0
#> ERR978241 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978242 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978243 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978244 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978245 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978246 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978247 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978248 3 0.4855 0.367 0 0.400 0.600 0
#> ERR978249 3 0.3975 0.662 0 0.240 0.760 0
#> ERR978250 3 0.0336 0.881 0 0.008 0.992 0
#> ERR978251 3 0.0000 0.886 0 0.000 1.000 0
#> ERR978252 3 0.0469 0.878 0 0.012 0.988 0
#> ERR978253 3 0.4193 0.626 0 0.268 0.732 0
#> ERR978254 3 0.4866 0.357 0 0.404 0.596 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978108 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978109 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978110 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978111 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978112 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978113 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978114 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978115 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978116 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978117 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978118 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978119 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978120 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978121 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978122 2 0.000 1.000 0 1 0.000 0 0.000
#> ERR978123 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978124 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978125 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978126 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978127 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978128 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978129 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978130 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978131 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978132 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978133 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978134 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978135 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978136 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978137 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978138 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978139 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978140 3 0.000 1.000 0 0 1.000 0 0.000
#> ERR978141 3 0.000 1.000 0 0 1.000 0 0.000
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#> ERR978249 5 0.285 0.797 0 0 0.172 0 0.828
#> ERR978250 5 0.314 0.758 0 0 0.204 0 0.796
#> ERR978251 5 0.366 0.658 0 0 0.276 0 0.724
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#> ERR978254 5 0.112 0.926 0 0 0.044 0 0.956
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.000 1.000 0 1 0.000 0 0.000 0.000
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#> ERR978241 6 0.000 1.000 0 0 0.000 0 0.000 1.000
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#> ERR978248 5 0.101 0.946 0 0 0.036 0 0.960 0.004
#> ERR978249 5 0.261 0.871 0 0 0.036 0 0.868 0.096
#> ERR978250 5 0.321 0.813 0 0 0.036 0 0.812 0.152
#> ERR978251 5 0.335 0.793 0 0 0.036 0 0.796 0.168
#> ERR978252 5 0.317 0.817 0 0 0.036 0 0.816 0.148
#> ERR978253 5 0.246 0.883 0 0 0.036 0 0.880 0.084
#> ERR978254 5 0.112 0.944 0 0 0.036 0 0.956 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["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 14049 rows and 148 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.3261 0.675 0.675
#> 3 3 0.585 0.885 0.870 0.8305 0.585 0.418
#> 4 4 0.765 0.903 0.923 0.1880 0.905 0.731
#> 5 5 0.905 0.960 0.963 0.1247 0.917 0.702
#> 6 6 1.000 1.000 1.000 0.0325 0.982 0.907
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 5
There is also optional best \(k\) = 2 5 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> ERR978107 2 0 1 0 1
#> ERR978108 2 0 1 0 1
#> ERR978109 2 0 1 0 1
#> ERR978110 2 0 1 0 1
#> ERR978111 2 0 1 0 1
#> ERR978112 2 0 1 0 1
#> ERR978113 2 0 1 0 1
#> ERR978114 2 0 1 0 1
#> ERR978115 2 0 1 0 1
#> ERR978116 2 0 1 0 1
#> ERR978117 2 0 1 0 1
#> ERR978118 2 0 1 0 1
#> ERR978119 2 0 1 0 1
#> ERR978120 2 0 1 0 1
#> ERR978121 2 0 1 0 1
#> ERR978122 2 0 1 0 1
#> ERR978123 2 0 1 0 1
#> ERR978124 2 0 1 0 1
#> ERR978125 2 0 1 0 1
#> ERR978126 2 0 1 0 1
#> ERR978127 2 0 1 0 1
#> ERR978128 2 0 1 0 1
#> ERR978129 2 0 1 0 1
#> ERR978130 2 0 1 0 1
#> ERR978131 2 0 1 0 1
#> ERR978132 2 0 1 0 1
#> ERR978133 2 0 1 0 1
#> ERR978134 2 0 1 0 1
#> ERR978135 2 0 1 0 1
#> ERR978136 2 0 1 0 1
#> ERR978137 2 0 1 0 1
#> ERR978138 2 0 1 0 1
#> ERR978139 2 0 1 0 1
#> ERR978140 2 0 1 0 1
#> ERR978141 2 0 1 0 1
#> ERR978142 2 0 1 0 1
#> ERR978143 2 0 1 0 1
#> ERR978144 2 0 1 0 1
#> ERR978145 2 0 1 0 1
#> ERR978146 2 0 1 0 1
#> ERR978147 2 0 1 0 1
#> ERR978148 2 0 1 0 1
#> ERR978149 2 0 1 0 1
#> ERR978150 2 0 1 0 1
#> ERR978151 2 0 1 0 1
#> ERR978152 2 0 1 0 1
#> ERR978153 1 0 1 1 0
#> ERR978154 1 0 1 1 0
#> ERR978155 1 0 1 1 0
#> ERR978156 1 0 1 1 0
#> ERR978157 1 0 1 1 0
#> ERR978158 1 0 1 1 0
#> ERR978159 1 0 1 1 0
#> ERR978160 1 0 1 1 0
#> ERR978161 1 0 1 1 0
#> ERR978162 1 0 1 1 0
#> ERR978163 1 0 1 1 0
#> ERR978164 1 0 1 1 0
#> ERR978165 1 0 1 1 0
#> ERR978166 1 0 1 1 0
#> ERR978167 1 0 1 1 0
#> ERR978168 1 0 1 1 0
#> ERR978169 2 0 1 0 1
#> ERR978170 2 0 1 0 1
#> ERR978171 2 0 1 0 1
#> ERR978172 2 0 1 0 1
#> ERR978173 2 0 1 0 1
#> ERR978174 2 0 1 0 1
#> ERR978175 2 0 1 0 1
#> ERR978176 2 0 1 0 1
#> ERR978177 2 0 1 0 1
#> ERR978178 2 0 1 0 1
#> ERR978179 2 0 1 0 1
#> ERR978180 2 0 1 0 1
#> ERR978181 2 0 1 0 1
#> ERR978182 2 0 1 0 1
#> ERR978183 2 0 1 0 1
#> ERR978184 2 0 1 0 1
#> ERR978185 2 0 1 0 1
#> ERR978186 2 0 1 0 1
#> ERR978187 2 0 1 0 1
#> ERR978188 2 0 1 0 1
#> ERR978189 2 0 1 0 1
#> ERR978190 2 0 1 0 1
#> ERR978191 2 0 1 0 1
#> ERR978192 2 0 1 0 1
#> ERR978193 2 0 1 0 1
#> ERR978194 2 0 1 0 1
#> ERR978195 2 0 1 0 1
#> ERR978196 2 0 1 0 1
#> ERR978197 2 0 1 0 1
#> ERR978198 2 0 1 0 1
#> ERR978199 2 0 1 0 1
#> ERR978200 2 0 1 0 1
#> ERR978201 2 0 1 0 1
#> ERR978202 2 0 1 0 1
#> ERR978203 2 0 1 0 1
#> ERR978204 2 0 1 0 1
#> ERR978205 2 0 1 0 1
#> ERR978206 2 0 1 0 1
#> ERR978207 2 0 1 0 1
#> ERR978208 2 0 1 0 1
#> ERR978209 2 0 1 0 1
#> ERR978210 2 0 1 0 1
#> ERR978211 2 0 1 0 1
#> ERR978212 2 0 1 0 1
#> ERR978213 2 0 1 0 1
#> ERR978214 2 0 1 0 1
#> ERR978215 2 0 1 0 1
#> ERR978216 2 0 1 0 1
#> ERR978217 2 0 1 0 1
#> ERR978218 2 0 1 0 1
#> ERR978219 2 0 1 0 1
#> ERR978220 2 0 1 0 1
#> ERR978221 2 0 1 0 1
#> ERR978222 2 0 1 0 1
#> ERR978223 2 0 1 0 1
#> ERR978224 2 0 1 0 1
#> ERR978225 2 0 1 0 1
#> ERR978226 2 0 1 0 1
#> ERR978227 1 0 1 1 0
#> ERR978228 1 0 1 1 0
#> ERR978229 1 0 1 1 0
#> ERR978230 1 0 1 1 0
#> ERR978231 1 0 1 1 0
#> ERR978232 1 0 1 1 0
#> ERR978233 1 0 1 1 0
#> ERR978234 1 0 1 1 0
#> ERR978235 1 0 1 1 0
#> ERR978236 1 0 1 1 0
#> ERR978237 1 0 1 1 0
#> ERR978238 1 0 1 1 0
#> ERR978239 1 0 1 1 0
#> ERR978240 1 0 1 1 0
#> ERR978241 2 0 1 0 1
#> ERR978242 2 0 1 0 1
#> ERR978243 2 0 1 0 1
#> ERR978244 2 0 1 0 1
#> ERR978245 2 0 1 0 1
#> ERR978246 2 0 1 0 1
#> ERR978247 2 0 1 0 1
#> ERR978248 2 0 1 0 1
#> ERR978249 2 0 1 0 1
#> ERR978250 2 0 1 0 1
#> ERR978251 2 0 1 0 1
#> ERR978252 2 0 1 0 1
#> ERR978253 2 0 1 0 1
#> ERR978254 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.000 0.888 0.00 1.000 0.000
#> ERR978108 2 0.000 0.888 0.00 1.000 0.000
#> ERR978109 2 0.000 0.888 0.00 1.000 0.000
#> ERR978110 2 0.000 0.888 0.00 1.000 0.000
#> ERR978111 2 0.000 0.888 0.00 1.000 0.000
#> ERR978112 2 0.000 0.888 0.00 1.000 0.000
#> ERR978113 2 0.000 0.888 0.00 1.000 0.000
#> ERR978114 2 0.000 0.888 0.00 1.000 0.000
#> ERR978115 2 0.000 0.888 0.00 1.000 0.000
#> ERR978116 2 0.000 0.888 0.00 1.000 0.000
#> ERR978117 2 0.000 0.888 0.00 1.000 0.000
#> ERR978118 2 0.000 0.888 0.00 1.000 0.000
#> ERR978119 2 0.000 0.888 0.00 1.000 0.000
#> ERR978120 2 0.000 0.888 0.00 1.000 0.000
#> ERR978121 2 0.000 0.888 0.00 1.000 0.000
#> ERR978122 2 0.000 0.888 0.00 1.000 0.000
#> ERR978123 3 0.475 1.000 0.00 0.216 0.784
#> ERR978124 3 0.475 1.000 0.00 0.216 0.784
#> ERR978125 3 0.475 1.000 0.00 0.216 0.784
#> ERR978126 3 0.475 1.000 0.00 0.216 0.784
#> ERR978127 3 0.475 1.000 0.00 0.216 0.784
#> ERR978128 3 0.475 1.000 0.00 0.216 0.784
#> ERR978129 3 0.475 1.000 0.00 0.216 0.784
#> ERR978130 3 0.475 1.000 0.00 0.216 0.784
#> ERR978131 3 0.475 1.000 0.00 0.216 0.784
#> ERR978132 3 0.475 1.000 0.00 0.216 0.784
#> ERR978133 3 0.475 1.000 0.00 0.216 0.784
#> ERR978134 3 0.475 1.000 0.00 0.216 0.784
#> ERR978135 3 0.475 1.000 0.00 0.216 0.784
#> ERR978136 3 0.475 1.000 0.00 0.216 0.784
#> ERR978137 3 0.475 1.000 0.00 0.216 0.784
#> ERR978138 3 0.475 1.000 0.00 0.216 0.784
#> ERR978139 3 0.475 1.000 0.00 0.216 0.784
#> ERR978140 3 0.475 1.000 0.00 0.216 0.784
#> ERR978141 3 0.475 1.000 0.00 0.216 0.784
#> ERR978142 3 0.475 1.000 0.00 0.216 0.784
#> ERR978143 3 0.475 1.000 0.00 0.216 0.784
#> ERR978144 3 0.475 1.000 0.00 0.216 0.784
#> ERR978145 3 0.475 1.000 0.00 0.216 0.784
#> ERR978146 3 0.475 1.000 0.00 0.216 0.784
#> ERR978147 3 0.475 1.000 0.00 0.216 0.784
#> ERR978148 3 0.475 1.000 0.00 0.216 0.784
#> ERR978149 3 0.475 1.000 0.00 0.216 0.784
#> ERR978150 3 0.475 1.000 0.00 0.216 0.784
#> ERR978151 3 0.475 1.000 0.00 0.216 0.784
#> ERR978152 3 0.475 1.000 0.00 0.216 0.784
#> ERR978153 1 0.000 0.861 1.00 0.000 0.000
#> ERR978154 1 0.000 0.861 1.00 0.000 0.000
#> ERR978155 1 0.000 0.861 1.00 0.000 0.000
#> ERR978156 1 0.000 0.861 1.00 0.000 0.000
#> ERR978157 1 0.000 0.861 1.00 0.000 0.000
#> ERR978158 1 0.000 0.861 1.00 0.000 0.000
#> ERR978159 1 0.000 0.861 1.00 0.000 0.000
#> ERR978160 1 0.000 0.861 1.00 0.000 0.000
#> ERR978161 1 0.000 0.861 1.00 0.000 0.000
#> ERR978162 1 0.000 0.861 1.00 0.000 0.000
#> ERR978163 1 0.000 0.861 1.00 0.000 0.000
#> ERR978164 1 0.000 0.861 1.00 0.000 0.000
#> ERR978165 1 0.000 0.861 1.00 0.000 0.000
#> ERR978166 1 0.000 0.861 1.00 0.000 0.000
#> ERR978167 1 0.000 0.861 1.00 0.000 0.000
#> ERR978168 1 0.000 0.861 1.00 0.000 0.000
#> ERR978169 1 0.649 0.644 0.54 0.004 0.456
#> ERR978170 1 0.649 0.644 0.54 0.004 0.456
#> ERR978171 1 0.649 0.644 0.54 0.004 0.456
#> ERR978172 1 0.649 0.644 0.54 0.004 0.456
#> ERR978173 1 0.649 0.644 0.54 0.004 0.456
#> ERR978174 1 0.649 0.644 0.54 0.004 0.456
#> ERR978175 1 0.649 0.644 0.54 0.004 0.456
#> ERR978176 1 0.649 0.644 0.54 0.004 0.456
#> ERR978177 1 0.649 0.644 0.54 0.004 0.456
#> ERR978178 1 0.649 0.644 0.54 0.004 0.456
#> ERR978179 1 0.649 0.644 0.54 0.004 0.456
#> ERR978180 1 0.649 0.644 0.54 0.004 0.456
#> ERR978181 1 0.649 0.644 0.54 0.004 0.456
#> ERR978182 1 0.649 0.644 0.54 0.004 0.456
#> ERR978183 2 0.000 0.888 0.00 1.000 0.000
#> ERR978184 2 0.000 0.888 0.00 1.000 0.000
#> ERR978185 2 0.000 0.888 0.00 1.000 0.000
#> ERR978186 2 0.000 0.888 0.00 1.000 0.000
#> ERR978187 2 0.000 0.888 0.00 1.000 0.000
#> ERR978188 2 0.000 0.888 0.00 1.000 0.000
#> ERR978189 2 0.000 0.888 0.00 1.000 0.000
#> ERR978190 2 0.000 0.888 0.00 1.000 0.000
#> ERR978191 2 0.000 0.888 0.00 1.000 0.000
#> ERR978192 2 0.000 0.888 0.00 1.000 0.000
#> ERR978193 2 0.000 0.888 0.00 1.000 0.000
#> ERR978194 2 0.000 0.888 0.00 1.000 0.000
#> ERR978195 2 0.000 0.888 0.00 1.000 0.000
#> ERR978196 2 0.000 0.888 0.00 1.000 0.000
#> ERR978197 3 0.475 1.000 0.00 0.216 0.784
#> ERR978198 3 0.475 1.000 0.00 0.216 0.784
#> ERR978199 3 0.475 1.000 0.00 0.216 0.784
#> ERR978200 3 0.475 1.000 0.00 0.216 0.784
#> ERR978201 3 0.475 1.000 0.00 0.216 0.784
#> ERR978202 3 0.475 1.000 0.00 0.216 0.784
#> ERR978203 3 0.475 1.000 0.00 0.216 0.784
#> ERR978204 3 0.475 1.000 0.00 0.216 0.784
#> ERR978205 3 0.475 1.000 0.00 0.216 0.784
#> ERR978206 3 0.475 1.000 0.00 0.216 0.784
#> ERR978207 3 0.475 1.000 0.00 0.216 0.784
#> ERR978208 3 0.475 1.000 0.00 0.216 0.784
#> ERR978209 3 0.475 1.000 0.00 0.216 0.784
#> ERR978210 3 0.475 1.000 0.00 0.216 0.784
#> ERR978211 3 0.475 1.000 0.00 0.216 0.784
#> ERR978212 3 0.475 1.000 0.00 0.216 0.784
#> ERR978213 3 0.475 1.000 0.00 0.216 0.784
#> ERR978214 3 0.475 1.000 0.00 0.216 0.784
#> ERR978215 3 0.475 1.000 0.00 0.216 0.784
#> ERR978216 3 0.475 1.000 0.00 0.216 0.784
#> ERR978217 3 0.475 1.000 0.00 0.216 0.784
#> ERR978218 3 0.475 1.000 0.00 0.216 0.784
#> ERR978219 3 0.475 1.000 0.00 0.216 0.784
#> ERR978220 3 0.475 1.000 0.00 0.216 0.784
#> ERR978221 3 0.475 1.000 0.00 0.216 0.784
#> ERR978222 3 0.475 1.000 0.00 0.216 0.784
#> ERR978223 3 0.475 1.000 0.00 0.216 0.784
#> ERR978224 3 0.475 1.000 0.00 0.216 0.784
#> ERR978225 3 0.475 1.000 0.00 0.216 0.784
#> ERR978226 3 0.475 1.000 0.00 0.216 0.784
#> ERR978227 1 0.000 0.861 1.00 0.000 0.000
#> ERR978228 1 0.000 0.861 1.00 0.000 0.000
#> ERR978229 1 0.000 0.861 1.00 0.000 0.000
#> ERR978230 1 0.000 0.861 1.00 0.000 0.000
#> ERR978231 1 0.000 0.861 1.00 0.000 0.000
#> ERR978232 1 0.000 0.861 1.00 0.000 0.000
#> ERR978233 1 0.000 0.861 1.00 0.000 0.000
#> ERR978234 1 0.000 0.861 1.00 0.000 0.000
#> ERR978235 1 0.000 0.861 1.00 0.000 0.000
#> ERR978236 1 0.000 0.861 1.00 0.000 0.000
#> ERR978237 1 0.000 0.861 1.00 0.000 0.000
#> ERR978238 1 0.000 0.861 1.00 0.000 0.000
#> ERR978239 1 0.000 0.861 1.00 0.000 0.000
#> ERR978240 1 0.000 0.861 1.00 0.000 0.000
#> ERR978241 2 0.502 0.672 0.00 0.760 0.240
#> ERR978242 2 0.502 0.672 0.00 0.760 0.240
#> ERR978243 2 0.502 0.672 0.00 0.760 0.240
#> ERR978244 2 0.502 0.672 0.00 0.760 0.240
#> ERR978245 2 0.502 0.672 0.00 0.760 0.240
#> ERR978246 2 0.502 0.672 0.00 0.760 0.240
#> ERR978247 2 0.502 0.672 0.00 0.760 0.240
#> ERR978248 2 0.502 0.672 0.00 0.760 0.240
#> ERR978249 2 0.502 0.672 0.00 0.760 0.240
#> ERR978250 2 0.502 0.672 0.00 0.760 0.240
#> ERR978251 2 0.502 0.672 0.00 0.760 0.240
#> ERR978252 2 0.502 0.672 0.00 0.760 0.240
#> ERR978253 2 0.502 0.672 0.00 0.760 0.240
#> ERR978254 2 0.502 0.672 0.00 0.760 0.240
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978108 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978109 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978110 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978111 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978112 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978113 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978114 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978115 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978116 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978117 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978118 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978119 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978120 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978121 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978122 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978123 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978124 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978125 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978126 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978127 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978128 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978129 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978130 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978131 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978132 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978133 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978134 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978135 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978136 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978137 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978138 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978139 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978140 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978141 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978142 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978143 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978144 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978145 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978146 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978147 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978148 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978149 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978150 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978151 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978152 3 0.156 0.838 0 0.000 0.944 0.056
#> ERR978153 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978154 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978155 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978156 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978157 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978158 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978159 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978160 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978161 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978162 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978163 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978164 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978165 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978166 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978167 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978168 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978169 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978170 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978171 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978172 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978173 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978174 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978175 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978176 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978177 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978178 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978179 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978180 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978181 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978182 4 0.000 0.843 0 0.000 0.000 1.000
#> ERR978183 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978184 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978185 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978186 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978187 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978188 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978189 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978190 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978191 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978192 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978193 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978194 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978195 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978196 2 0.000 1.000 0 1.000 0.000 0.000
#> ERR978197 3 0.389 0.838 0 0.184 0.804 0.012
#> ERR978198 3 0.381 0.839 0 0.188 0.804 0.008
#> ERR978199 3 0.381 0.839 0 0.188 0.804 0.008
#> ERR978200 3 0.371 0.840 0 0.192 0.804 0.004
#> ERR978201 3 0.381 0.839 0 0.188 0.804 0.008
#> ERR978202 3 0.381 0.839 0 0.188 0.804 0.008
#> ERR978203 3 0.389 0.838 0 0.184 0.804 0.012
#> ERR978204 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978205 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978206 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978207 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978208 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978209 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978210 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978211 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978212 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978213 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978214 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978215 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978216 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978217 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978218 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978219 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978220 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978221 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978222 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978223 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978224 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978225 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978226 3 0.357 0.841 0 0.196 0.804 0.000
#> ERR978227 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978228 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978229 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978230 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978231 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978232 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978233 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978234 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978235 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978236 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978237 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978238 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978239 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978240 1 0.000 1.000 1 0.000 0.000 0.000
#> ERR978241 4 0.511 0.821 0 0.060 0.196 0.744
#> ERR978242 4 0.511 0.821 0 0.060 0.196 0.744
#> ERR978243 4 0.511 0.821 0 0.060 0.196 0.744
#> ERR978244 4 0.511 0.821 0 0.060 0.196 0.744
#> ERR978245 4 0.511 0.821 0 0.060 0.196 0.744
#> ERR978246 4 0.511 0.821 0 0.060 0.196 0.744
#> ERR978247 4 0.511 0.821 0 0.060 0.196 0.744
#> ERR978248 4 0.518 0.820 0 0.064 0.196 0.740
#> ERR978249 4 0.518 0.820 0 0.064 0.196 0.740
#> ERR978250 4 0.518 0.820 0 0.064 0.196 0.740
#> ERR978251 4 0.518 0.820 0 0.064 0.196 0.740
#> ERR978252 4 0.518 0.820 0 0.064 0.196 0.740
#> ERR978253 4 0.518 0.820 0 0.064 0.196 0.740
#> ERR978254 4 0.518 0.820 0 0.064 0.196 0.740
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978108 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978109 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978110 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978111 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978112 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978113 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978114 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978115 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978116 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978117 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978118 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978119 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978120 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978121 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978122 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978123 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978124 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978125 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978126 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978127 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978128 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978129 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978130 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978131 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978132 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978133 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978134 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978135 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978136 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978137 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978138 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978139 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978140 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978141 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978142 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978143 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978144 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978145 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978146 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978147 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978148 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978149 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978150 3 0.000 1.000 0 0.000 1.000 0.000 0
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#> ERR978152 3 0.000 1.000 0 0.000 1.000 0.000 0
#> ERR978153 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978154 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978155 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978156 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978157 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978158 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978159 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978160 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978161 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978162 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978163 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978164 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978165 1 0.000 1.000 1 0.000 0.000 0.000 0
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#> ERR978167 1 0.000 1.000 1 0.000 0.000 0.000 0
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#> ERR978169 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978170 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978171 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978172 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978173 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978174 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978175 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978176 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978177 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978178 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978179 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978180 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978181 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978182 4 0.000 0.806 0 0.000 0.000 1.000 0
#> ERR978183 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978184 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978185 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978186 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978187 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978188 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978189 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978190 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978191 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978192 2 0.000 1.000 0 1.000 0.000 0.000 0
#> ERR978193 2 0.000 1.000 0 1.000 0.000 0.000 0
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#> ERR978197 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978198 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978199 5 0.000 1.000 0 0.000 0.000 0.000 1
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#> ERR978201 5 0.000 1.000 0 0.000 0.000 0.000 1
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#> ERR978203 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978204 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978205 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978206 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978207 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978208 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978209 5 0.000 1.000 0 0.000 0.000 0.000 1
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#> ERR978216 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978217 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978218 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978219 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978220 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978221 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978222 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978223 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978224 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978225 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978226 5 0.000 1.000 0 0.000 0.000 0.000 1
#> ERR978227 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978228 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978229 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978230 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978231 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978232 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978233 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978234 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978235 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978236 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978237 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978238 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978239 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978240 1 0.000 1.000 1 0.000 0.000 0.000 0
#> ERR978241 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978242 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978243 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978244 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978245 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978246 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978247 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978248 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978249 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978250 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978251 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978252 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978253 4 0.582 0.774 0 0.204 0.184 0.612 0
#> ERR978254 4 0.582 0.774 0 0.204 0.184 0.612 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0 1 0 1 0 0 0 0
#> ERR978108 2 0 1 0 1 0 0 0 0
#> ERR978109 2 0 1 0 1 0 0 0 0
#> ERR978110 2 0 1 0 1 0 0 0 0
#> ERR978111 2 0 1 0 1 0 0 0 0
#> ERR978112 2 0 1 0 1 0 0 0 0
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#> ERR978114 2 0 1 0 1 0 0 0 0
#> ERR978115 2 0 1 0 1 0 0 0 0
#> ERR978116 2 0 1 0 1 0 0 0 0
#> ERR978117 2 0 1 0 1 0 0 0 0
#> ERR978118 2 0 1 0 1 0 0 0 0
#> ERR978119 2 0 1 0 1 0 0 0 0
#> ERR978120 2 0 1 0 1 0 0 0 0
#> ERR978121 2 0 1 0 1 0 0 0 0
#> ERR978122 2 0 1 0 1 0 0 0 0
#> ERR978123 3 0 1 0 0 1 0 0 0
#> ERR978124 3 0 1 0 0 1 0 0 0
#> ERR978125 3 0 1 0 0 1 0 0 0
#> ERR978126 3 0 1 0 0 1 0 0 0
#> ERR978127 3 0 1 0 0 1 0 0 0
#> ERR978128 3 0 1 0 0 1 0 0 0
#> ERR978129 3 0 1 0 0 1 0 0 0
#> ERR978130 3 0 1 0 0 1 0 0 0
#> ERR978131 3 0 1 0 0 1 0 0 0
#> ERR978132 3 0 1 0 0 1 0 0 0
#> ERR978133 3 0 1 0 0 1 0 0 0
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#> ERR978140 3 0 1 0 0 1 0 0 0
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#> ERR978144 3 0 1 0 0 1 0 0 0
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#> ERR978148 3 0 1 0 0 1 0 0 0
#> ERR978149 3 0 1 0 0 1 0 0 0
#> ERR978150 3 0 1 0 0 1 0 0 0
#> ERR978151 3 0 1 0 0 1 0 0 0
#> ERR978152 3 0 1 0 0 1 0 0 0
#> ERR978153 1 0 1 1 0 0 0 0 0
#> ERR978154 1 0 1 1 0 0 0 0 0
#> ERR978155 1 0 1 1 0 0 0 0 0
#> ERR978156 1 0 1 1 0 0 0 0 0
#> ERR978157 1 0 1 1 0 0 0 0 0
#> ERR978158 1 0 1 1 0 0 0 0 0
#> ERR978159 1 0 1 1 0 0 0 0 0
#> ERR978160 1 0 1 1 0 0 0 0 0
#> ERR978161 1 0 1 1 0 0 0 0 0
#> ERR978162 1 0 1 1 0 0 0 0 0
#> ERR978163 1 0 1 1 0 0 0 0 0
#> ERR978164 1 0 1 1 0 0 0 0 0
#> ERR978165 1 0 1 1 0 0 0 0 0
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#> ERR978193 2 0 1 0 1 0 0 0 0
#> ERR978194 2 0 1 0 1 0 0 0 0
#> ERR978195 2 0 1 0 1 0 0 0 0
#> ERR978196 2 0 1 0 1 0 0 0 0
#> ERR978197 5 0 1 0 0 0 0 1 0
#> ERR978198 5 0 1 0 0 0 0 1 0
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#> ERR978200 5 0 1 0 0 0 0 1 0
#> ERR978201 5 0 1 0 0 0 0 1 0
#> ERR978202 5 0 1 0 0 0 0 1 0
#> ERR978203 5 0 1 0 0 0 0 1 0
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#> ERR978205 5 0 1 0 0 0 0 1 0
#> ERR978206 5 0 1 0 0 0 0 1 0
#> ERR978207 5 0 1 0 0 0 0 1 0
#> ERR978208 5 0 1 0 0 0 0 1 0
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#> ERR978219 5 0 1 0 0 0 0 1 0
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#> ERR978221 5 0 1 0 0 0 0 1 0
#> ERR978222 5 0 1 0 0 0 0 1 0
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#> ERR978224 5 0 1 0 0 0 0 1 0
#> ERR978225 5 0 1 0 0 0 0 1 0
#> ERR978226 5 0 1 0 0 0 0 1 0
#> ERR978227 1 0 1 1 0 0 0 0 0
#> ERR978228 1 0 1 1 0 0 0 0 0
#> ERR978229 1 0 1 1 0 0 0 0 0
#> ERR978230 1 0 1 1 0 0 0 0 0
#> ERR978231 1 0 1 1 0 0 0 0 0
#> ERR978232 1 0 1 1 0 0 0 0 0
#> ERR978233 1 0 1 1 0 0 0 0 0
#> ERR978234 1 0 1 1 0 0 0 0 0
#> ERR978235 1 0 1 1 0 0 0 0 0
#> ERR978236 1 0 1 1 0 0 0 0 0
#> ERR978237 1 0 1 1 0 0 0 0 0
#> ERR978238 1 0 1 1 0 0 0 0 0
#> ERR978239 1 0 1 1 0 0 0 0 0
#> ERR978240 1 0 1 1 0 0 0 0 0
#> ERR978241 6 0 1 0 0 0 0 0 1
#> ERR978242 6 0 1 0 0 0 0 0 1
#> ERR978243 6 0 1 0 0 0 0 0 1
#> ERR978244 6 0 1 0 0 0 0 0 1
#> ERR978245 6 0 1 0 0 0 0 0 1
#> ERR978246 6 0 1 0 0 0 0 0 1
#> ERR978247 6 0 1 0 0 0 0 0 1
#> ERR978248 6 0 1 0 0 0 0 0 1
#> ERR978249 6 0 1 0 0 0 0 0 1
#> ERR978250 6 0 1 0 0 0 0 0 1
#> ERR978251 6 0 1 0 0 0 0 0 1
#> ERR978252 6 0 1 0 0 0 0 0 1
#> ERR978253 6 0 1 0 0 0 0 0 1
#> ERR978254 6 0 1 0 0 0 0 0 1
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 14049 rows and 148 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 1.000 1.000 1.000 0.3263 0.675 0.675
#> 3 3 1.000 0.965 0.981 0.8695 0.710 0.570
#> 4 4 0.865 0.825 0.929 0.1765 0.796 0.526
#> 5 5 0.872 0.886 0.928 0.0802 0.866 0.573
#> 6 6 0.876 0.788 0.868 0.0510 0.944 0.757
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 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
#> ERR978107 2 0.0000 1.000 0.000 1.000
#> ERR978108 2 0.0000 1.000 0.000 1.000
#> ERR978109 2 0.0000 1.000 0.000 1.000
#> ERR978110 2 0.0000 1.000 0.000 1.000
#> ERR978111 2 0.0000 1.000 0.000 1.000
#> ERR978112 2 0.0000 1.000 0.000 1.000
#> ERR978113 2 0.0000 1.000 0.000 1.000
#> ERR978114 2 0.0000 1.000 0.000 1.000
#> ERR978115 2 0.0000 1.000 0.000 1.000
#> ERR978116 2 0.0000 1.000 0.000 1.000
#> ERR978117 2 0.0000 1.000 0.000 1.000
#> ERR978118 2 0.0000 1.000 0.000 1.000
#> ERR978119 2 0.0000 1.000 0.000 1.000
#> ERR978120 2 0.0000 1.000 0.000 1.000
#> ERR978121 2 0.0000 1.000 0.000 1.000
#> ERR978122 2 0.0000 1.000 0.000 1.000
#> ERR978123 2 0.0000 1.000 0.000 1.000
#> ERR978124 2 0.0000 1.000 0.000 1.000
#> ERR978125 2 0.0000 1.000 0.000 1.000
#> ERR978126 2 0.0000 1.000 0.000 1.000
#> ERR978127 2 0.0000 1.000 0.000 1.000
#> ERR978128 2 0.0000 1.000 0.000 1.000
#> ERR978129 2 0.0000 1.000 0.000 1.000
#> ERR978130 2 0.0000 1.000 0.000 1.000
#> ERR978131 2 0.0000 1.000 0.000 1.000
#> ERR978132 2 0.0000 1.000 0.000 1.000
#> ERR978133 2 0.0000 1.000 0.000 1.000
#> ERR978134 2 0.0000 1.000 0.000 1.000
#> ERR978135 2 0.0000 1.000 0.000 1.000
#> ERR978136 2 0.0000 1.000 0.000 1.000
#> ERR978137 2 0.0000 1.000 0.000 1.000
#> ERR978138 2 0.0000 1.000 0.000 1.000
#> ERR978139 2 0.0000 1.000 0.000 1.000
#> ERR978140 2 0.0000 1.000 0.000 1.000
#> ERR978141 2 0.0000 1.000 0.000 1.000
#> ERR978142 2 0.0000 1.000 0.000 1.000
#> ERR978143 2 0.0000 1.000 0.000 1.000
#> ERR978144 2 0.0000 1.000 0.000 1.000
#> ERR978145 2 0.0000 1.000 0.000 1.000
#> ERR978146 2 0.0000 1.000 0.000 1.000
#> ERR978147 2 0.0000 1.000 0.000 1.000
#> ERR978148 2 0.0000 1.000 0.000 1.000
#> ERR978149 2 0.0000 1.000 0.000 1.000
#> ERR978150 2 0.0000 1.000 0.000 1.000
#> ERR978151 2 0.0000 1.000 0.000 1.000
#> ERR978152 2 0.0000 1.000 0.000 1.000
#> ERR978153 1 0.0000 1.000 1.000 0.000
#> ERR978154 1 0.0000 1.000 1.000 0.000
#> ERR978155 1 0.0000 1.000 1.000 0.000
#> ERR978156 1 0.0000 1.000 1.000 0.000
#> ERR978157 1 0.0000 1.000 1.000 0.000
#> ERR978158 1 0.0000 1.000 1.000 0.000
#> ERR978159 1 0.0000 1.000 1.000 0.000
#> ERR978160 1 0.0000 1.000 1.000 0.000
#> ERR978161 1 0.0000 1.000 1.000 0.000
#> ERR978162 1 0.0000 1.000 1.000 0.000
#> ERR978163 1 0.0000 1.000 1.000 0.000
#> ERR978164 1 0.0000 1.000 1.000 0.000
#> ERR978165 1 0.0000 1.000 1.000 0.000
#> ERR978166 1 0.0000 1.000 1.000 0.000
#> ERR978167 1 0.0000 1.000 1.000 0.000
#> ERR978168 1 0.0000 1.000 1.000 0.000
#> ERR978169 2 0.0376 0.996 0.004 0.996
#> ERR978170 2 0.0672 0.992 0.008 0.992
#> ERR978171 2 0.0672 0.992 0.008 0.992
#> ERR978172 2 0.0672 0.992 0.008 0.992
#> ERR978173 2 0.0376 0.996 0.004 0.996
#> ERR978174 2 0.0000 1.000 0.000 1.000
#> ERR978175 2 0.0000 1.000 0.000 1.000
#> ERR978176 2 0.0000 1.000 0.000 1.000
#> ERR978177 2 0.0000 1.000 0.000 1.000
#> ERR978178 2 0.0000 1.000 0.000 1.000
#> ERR978179 2 0.0000 1.000 0.000 1.000
#> ERR978180 2 0.0000 1.000 0.000 1.000
#> ERR978181 2 0.0000 1.000 0.000 1.000
#> ERR978182 2 0.0000 1.000 0.000 1.000
#> ERR978183 2 0.0000 1.000 0.000 1.000
#> ERR978184 2 0.0000 1.000 0.000 1.000
#> ERR978185 2 0.0000 1.000 0.000 1.000
#> ERR978186 2 0.0000 1.000 0.000 1.000
#> ERR978187 2 0.0000 1.000 0.000 1.000
#> ERR978188 2 0.0000 1.000 0.000 1.000
#> ERR978189 2 0.0000 1.000 0.000 1.000
#> ERR978190 2 0.0000 1.000 0.000 1.000
#> ERR978191 2 0.0000 1.000 0.000 1.000
#> ERR978192 2 0.0000 1.000 0.000 1.000
#> ERR978193 2 0.0000 1.000 0.000 1.000
#> ERR978194 2 0.0000 1.000 0.000 1.000
#> ERR978195 2 0.0000 1.000 0.000 1.000
#> ERR978196 2 0.0000 1.000 0.000 1.000
#> ERR978197 2 0.0000 1.000 0.000 1.000
#> ERR978198 2 0.0000 1.000 0.000 1.000
#> ERR978199 2 0.0000 1.000 0.000 1.000
#> ERR978200 2 0.0000 1.000 0.000 1.000
#> ERR978201 2 0.0000 1.000 0.000 1.000
#> ERR978202 2 0.0000 1.000 0.000 1.000
#> ERR978203 2 0.0000 1.000 0.000 1.000
#> ERR978204 2 0.0000 1.000 0.000 1.000
#> ERR978205 2 0.0000 1.000 0.000 1.000
#> ERR978206 2 0.0000 1.000 0.000 1.000
#> ERR978207 2 0.0000 1.000 0.000 1.000
#> ERR978208 2 0.0000 1.000 0.000 1.000
#> ERR978209 2 0.0000 1.000 0.000 1.000
#> ERR978210 2 0.0000 1.000 0.000 1.000
#> ERR978211 2 0.0000 1.000 0.000 1.000
#> ERR978212 2 0.0000 1.000 0.000 1.000
#> ERR978213 2 0.0000 1.000 0.000 1.000
#> ERR978214 2 0.0000 1.000 0.000 1.000
#> ERR978215 2 0.0000 1.000 0.000 1.000
#> ERR978216 2 0.0000 1.000 0.000 1.000
#> ERR978217 2 0.0000 1.000 0.000 1.000
#> ERR978218 2 0.0000 1.000 0.000 1.000
#> ERR978219 2 0.0000 1.000 0.000 1.000
#> ERR978220 2 0.0000 1.000 0.000 1.000
#> ERR978221 2 0.0000 1.000 0.000 1.000
#> ERR978222 2 0.0000 1.000 0.000 1.000
#> ERR978223 2 0.0000 1.000 0.000 1.000
#> ERR978224 2 0.0000 1.000 0.000 1.000
#> ERR978225 2 0.0000 1.000 0.000 1.000
#> ERR978226 2 0.0000 1.000 0.000 1.000
#> ERR978227 1 0.0000 1.000 1.000 0.000
#> ERR978228 1 0.0000 1.000 1.000 0.000
#> ERR978229 1 0.0000 1.000 1.000 0.000
#> ERR978230 1 0.0000 1.000 1.000 0.000
#> ERR978231 1 0.0000 1.000 1.000 0.000
#> ERR978232 1 0.0000 1.000 1.000 0.000
#> ERR978233 1 0.0000 1.000 1.000 0.000
#> ERR978234 1 0.0000 1.000 1.000 0.000
#> ERR978235 1 0.0000 1.000 1.000 0.000
#> ERR978236 1 0.0000 1.000 1.000 0.000
#> ERR978237 1 0.0000 1.000 1.000 0.000
#> ERR978238 1 0.0000 1.000 1.000 0.000
#> ERR978239 1 0.0000 1.000 1.000 0.000
#> ERR978240 1 0.0000 1.000 1.000 0.000
#> ERR978241 2 0.0000 1.000 0.000 1.000
#> ERR978242 2 0.0000 1.000 0.000 1.000
#> ERR978243 2 0.0000 1.000 0.000 1.000
#> ERR978244 2 0.0000 1.000 0.000 1.000
#> ERR978245 2 0.0000 1.000 0.000 1.000
#> ERR978246 2 0.0000 1.000 0.000 1.000
#> ERR978247 2 0.0000 1.000 0.000 1.000
#> ERR978248 2 0.0000 1.000 0.000 1.000
#> ERR978249 2 0.0000 1.000 0.000 1.000
#> ERR978250 2 0.0000 1.000 0.000 1.000
#> ERR978251 2 0.0000 1.000 0.000 1.000
#> ERR978252 2 0.0000 1.000 0.000 1.000
#> ERR978253 2 0.0000 1.000 0.000 1.000
#> ERR978254 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ERR978107 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978108 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978109 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978110 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978111 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978112 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978113 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978114 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978115 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978116 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978117 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978118 2 0.0237 0.986 0.000 0.996 0.004
#> ERR978119 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978120 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978121 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978122 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978123 2 0.4178 0.779 0.000 0.828 0.172
#> ERR978124 3 0.3752 0.836 0.000 0.144 0.856
#> ERR978125 3 0.1643 0.944 0.000 0.044 0.956
#> ERR978126 3 0.1031 0.958 0.000 0.024 0.976
#> ERR978127 3 0.1289 0.953 0.000 0.032 0.968
#> ERR978128 3 0.3412 0.860 0.000 0.124 0.876
#> ERR978129 2 0.6008 0.361 0.000 0.628 0.372
#> ERR978130 2 0.2878 0.884 0.000 0.904 0.096
#> ERR978131 2 0.0592 0.981 0.000 0.988 0.012
#> ERR978132 2 0.0592 0.981 0.000 0.988 0.012
#> ERR978133 2 0.0592 0.981 0.000 0.988 0.012
#> ERR978134 2 0.0892 0.978 0.000 0.980 0.020
#> ERR978135 2 0.0592 0.981 0.000 0.988 0.012
#> ERR978136 2 0.0592 0.981 0.000 0.988 0.012
#> ERR978137 2 0.0592 0.981 0.000 0.988 0.012
#> ERR978138 3 0.5859 0.528 0.000 0.344 0.656
#> ERR978139 3 0.1860 0.936 0.000 0.052 0.948
#> ERR978140 3 0.1031 0.958 0.000 0.024 0.976
#> ERR978141 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978142 3 0.1031 0.958 0.000 0.024 0.976
#> ERR978143 3 0.1289 0.953 0.000 0.032 0.968
#> ERR978144 3 0.3482 0.856 0.000 0.128 0.872
#> ERR978145 3 0.6192 0.339 0.000 0.420 0.580
#> ERR978146 3 0.1163 0.956 0.000 0.028 0.972
#> ERR978147 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978148 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978149 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978150 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978151 3 0.1031 0.958 0.000 0.024 0.976
#> ERR978152 3 0.1643 0.944 0.000 0.044 0.956
#> ERR978153 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978154 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978155 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978156 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978157 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978158 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978159 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978160 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978161 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978162 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978163 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978164 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978165 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978166 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978167 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978168 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978169 3 0.1015 0.954 0.008 0.012 0.980
#> ERR978170 3 0.1015 0.954 0.008 0.012 0.980
#> ERR978171 3 0.1015 0.954 0.008 0.012 0.980
#> ERR978172 3 0.1015 0.954 0.008 0.012 0.980
#> ERR978173 3 0.1015 0.954 0.008 0.012 0.980
#> ERR978174 3 0.0983 0.958 0.004 0.016 0.980
#> ERR978175 3 0.0983 0.958 0.004 0.016 0.980
#> ERR978176 3 0.0983 0.958 0.004 0.016 0.980
#> ERR978177 3 0.0983 0.958 0.004 0.016 0.980
#> ERR978178 3 0.0983 0.958 0.004 0.016 0.980
#> ERR978179 3 0.0983 0.958 0.004 0.016 0.980
#> ERR978180 3 0.0983 0.958 0.004 0.016 0.980
#> ERR978181 3 0.0983 0.958 0.004 0.016 0.980
#> ERR978182 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978183 2 0.0424 0.984 0.000 0.992 0.008
#> ERR978184 2 0.0237 0.986 0.000 0.996 0.004
#> ERR978185 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978186 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978187 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978188 2 0.0424 0.984 0.000 0.992 0.008
#> ERR978189 2 0.0424 0.984 0.000 0.992 0.008
#> ERR978190 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978191 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978192 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978193 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978194 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978195 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978196 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978197 2 0.0592 0.981 0.000 0.988 0.012
#> ERR978198 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978199 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978200 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978201 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978202 2 0.0424 0.984 0.000 0.992 0.008
#> ERR978203 2 0.0592 0.981 0.000 0.988 0.012
#> ERR978204 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978205 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978206 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978207 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978208 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978209 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978210 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978211 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978212 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978213 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978214 2 0.0237 0.986 0.000 0.996 0.004
#> ERR978215 2 0.0237 0.986 0.000 0.996 0.004
#> ERR978216 2 0.0237 0.986 0.000 0.996 0.004
#> ERR978217 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978218 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978219 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978220 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978221 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978222 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978223 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978224 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978225 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978226 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978227 1 0.0237 0.997 0.996 0.000 0.004
#> ERR978228 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978229 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978230 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978231 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978232 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978233 1 0.0237 0.997 0.996 0.000 0.004
#> ERR978234 1 0.0424 0.994 0.992 0.000 0.008
#> ERR978235 1 0.0237 0.997 0.996 0.000 0.004
#> ERR978236 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978237 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978238 1 0.0000 0.999 1.000 0.000 0.000
#> ERR978239 1 0.0237 0.997 0.996 0.000 0.004
#> ERR978240 1 0.0424 0.994 0.992 0.000 0.008
#> ERR978241 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978242 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978243 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978244 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978245 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978246 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978247 3 0.0892 0.959 0.000 0.020 0.980
#> ERR978248 2 0.0424 0.984 0.000 0.992 0.008
#> ERR978249 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978250 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978251 2 0.0237 0.986 0.000 0.996 0.004
#> ERR978252 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978253 2 0.0000 0.989 0.000 1.000 0.000
#> ERR978254 2 0.0592 0.981 0.000 0.988 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ERR978107 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978108 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978109 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978110 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978111 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978112 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978113 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978114 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978115 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978116 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978117 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978118 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978119 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978120 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978121 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978122 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978123 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978124 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978125 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978126 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978127 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978128 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978129 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978130 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978131 3 0.0188 0.889 0 0.004 0.996 0.000
#> ERR978132 3 0.0188 0.889 0 0.004 0.996 0.000
#> ERR978133 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978134 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978135 3 0.0000 0.889 0 0.000 1.000 0.000
#> ERR978136 3 0.0188 0.889 0 0.004 0.996 0.000
#> ERR978137 3 0.0188 0.889 0 0.004 0.996 0.000
#> ERR978138 3 0.0376 0.890 0 0.004 0.992 0.004
#> ERR978139 3 0.0592 0.887 0 0.000 0.984 0.016
#> ERR978140 3 0.1211 0.877 0 0.000 0.960 0.040
#> ERR978141 3 0.2216 0.845 0 0.000 0.908 0.092
#> ERR978142 3 0.1389 0.873 0 0.000 0.952 0.048
#> ERR978143 3 0.1389 0.873 0 0.000 0.952 0.048
#> ERR978144 3 0.0469 0.888 0 0.000 0.988 0.012
#> ERR978145 3 0.0376 0.890 0 0.004 0.992 0.004
#> ERR978146 3 0.0188 0.889 0 0.000 0.996 0.004
#> ERR978147 3 0.0469 0.888 0 0.000 0.988 0.012
#> ERR978148 3 0.0592 0.887 0 0.000 0.984 0.016
#> ERR978149 3 0.0592 0.887 0 0.000 0.984 0.016
#> ERR978150 3 0.0592 0.887 0 0.000 0.984 0.016
#> ERR978151 3 0.0188 0.889 0 0.000 0.996 0.004
#> ERR978152 3 0.0188 0.889 0 0.000 0.996 0.004
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978169 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978170 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978171 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978172 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978173 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978174 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978175 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978176 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978177 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978178 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978179 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978180 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978181 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978182 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978183 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978184 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978185 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978186 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978187 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978188 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978189 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978190 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978191 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978192 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978193 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978194 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978195 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978196 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978197 3 0.2589 0.817 0 0.116 0.884 0.000
#> ERR978198 3 0.2149 0.843 0 0.088 0.912 0.000
#> ERR978199 3 0.2011 0.849 0 0.080 0.920 0.000
#> ERR978200 3 0.2081 0.847 0 0.084 0.916 0.000
#> ERR978201 3 0.2081 0.847 0 0.084 0.916 0.000
#> ERR978202 3 0.2216 0.840 0 0.092 0.908 0.000
#> ERR978203 3 0.2704 0.809 0 0.124 0.876 0.000
#> ERR978204 3 0.4855 0.306 0 0.400 0.600 0.000
#> ERR978205 3 0.4746 0.399 0 0.368 0.632 0.000
#> ERR978206 3 0.4713 0.420 0 0.360 0.640 0.000
#> ERR978207 3 0.4697 0.429 0 0.356 0.644 0.000
#> ERR978208 3 0.4585 0.483 0 0.332 0.668 0.000
#> ERR978209 3 0.4679 0.439 0 0.352 0.648 0.000
#> ERR978210 3 0.4804 0.353 0 0.384 0.616 0.000
#> ERR978211 3 0.4830 0.332 0 0.392 0.608 0.000
#> ERR978212 2 0.4967 0.245 0 0.548 0.452 0.000
#> ERR978213 2 0.4981 0.206 0 0.536 0.464 0.000
#> ERR978214 2 0.4933 0.301 0 0.568 0.432 0.000
#> ERR978215 2 0.4925 0.311 0 0.572 0.428 0.000
#> ERR978216 2 0.4967 0.245 0 0.548 0.452 0.000
#> ERR978217 2 0.4955 0.269 0 0.556 0.444 0.000
#> ERR978218 2 0.4888 0.347 0 0.588 0.412 0.000
#> ERR978219 2 0.4804 0.401 0 0.616 0.384 0.000
#> ERR978220 2 0.4961 0.258 0 0.552 0.448 0.000
#> ERR978221 2 0.4948 0.280 0 0.560 0.440 0.000
#> ERR978222 2 0.4898 0.339 0 0.584 0.416 0.000
#> ERR978223 2 0.4898 0.339 0 0.584 0.416 0.000
#> ERR978224 2 0.4866 0.363 0 0.596 0.404 0.000
#> ERR978225 2 0.4948 0.280 0 0.560 0.440 0.000
#> ERR978226 2 0.4994 0.146 0 0.520 0.480 0.000
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000
#> ERR978241 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978242 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978243 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978244 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978245 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978246 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978247 4 0.0188 1.000 0 0.000 0.004 0.996
#> ERR978248 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978249 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978250 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978251 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978252 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978253 2 0.0000 0.842 0 1.000 0.000 0.000
#> ERR978254 2 0.0000 0.842 0 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> ERR978107 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978119 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978120 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978121 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978122 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978123 3 0.2852 0.995 0 0.000 0.828 0.000 0.172
#> ERR978124 3 0.2852 0.995 0 0.000 0.828 0.000 0.172
#> ERR978125 3 0.2852 0.995 0 0.000 0.828 0.000 0.172
#> ERR978126 3 0.2813 0.992 0 0.000 0.832 0.000 0.168
#> ERR978127 3 0.2813 0.992 0 0.000 0.832 0.000 0.168
#> ERR978128 3 0.2852 0.995 0 0.000 0.828 0.000 0.172
#> ERR978129 3 0.2852 0.995 0 0.000 0.828 0.000 0.172
#> ERR978130 3 0.2813 0.992 0 0.000 0.832 0.000 0.168
#> ERR978131 3 0.2929 0.989 0 0.000 0.820 0.000 0.180
#> ERR978132 3 0.2891 0.995 0 0.000 0.824 0.000 0.176
#> ERR978133 3 0.2891 0.995 0 0.000 0.824 0.000 0.176
#> ERR978134 3 0.2891 0.995 0 0.000 0.824 0.000 0.176
#> ERR978135 3 0.2891 0.995 0 0.000 0.824 0.000 0.176
#> ERR978136 3 0.2891 0.995 0 0.000 0.824 0.000 0.176
#> ERR978137 3 0.2891 0.995 0 0.000 0.824 0.000 0.176
#> ERR978138 5 0.0404 0.776 0 0.000 0.012 0.000 0.988
#> ERR978139 5 0.0162 0.777 0 0.000 0.004 0.000 0.996
#> ERR978140 5 0.0162 0.777 0 0.000 0.004 0.000 0.996
#> ERR978141 5 0.0865 0.772 0 0.000 0.004 0.024 0.972
#> ERR978142 5 0.0451 0.777 0 0.000 0.004 0.008 0.988
#> ERR978143 5 0.0324 0.777 0 0.000 0.004 0.004 0.992
#> ERR978144 5 0.0290 0.777 0 0.000 0.008 0.000 0.992
#> ERR978145 5 0.0404 0.776 0 0.000 0.012 0.000 0.988
#> ERR978146 5 0.3895 0.438 0 0.000 0.320 0.000 0.680
#> ERR978147 5 0.3895 0.439 0 0.000 0.320 0.000 0.680
#> ERR978148 5 0.5028 0.455 0 0.000 0.260 0.072 0.668
#> ERR978149 5 0.5365 0.465 0 0.000 0.204 0.132 0.664
#> ERR978150 5 0.4520 0.467 0 0.000 0.284 0.032 0.684
#> ERR978151 5 0.3913 0.430 0 0.000 0.324 0.000 0.676
#> ERR978152 5 0.4074 0.338 0 0.000 0.364 0.000 0.636
#> ERR978153 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978154 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978155 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978156 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978157 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978158 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978159 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978160 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978161 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978162 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978163 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978164 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978165 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978166 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978167 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978168 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978169 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978170 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978171 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978172 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978173 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978174 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978175 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978176 4 0.0162 0.998 0 0.000 0.004 0.996 0.000
#> ERR978177 4 0.0162 0.998 0 0.000 0.004 0.996 0.000
#> ERR978178 4 0.0162 0.998 0 0.000 0.004 0.996 0.000
#> ERR978179 4 0.0162 0.998 0 0.000 0.004 0.996 0.000
#> ERR978180 4 0.0162 0.998 0 0.000 0.004 0.996 0.000
#> ERR978181 4 0.0162 0.998 0 0.000 0.004 0.996 0.000
#> ERR978182 4 0.0162 0.998 0 0.000 0.004 0.996 0.000
#> ERR978183 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978184 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978185 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978186 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978187 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978188 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978189 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978190 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978191 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978192 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978193 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978194 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978195 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978196 2 0.0000 0.992 0 1.000 0.000 0.000 0.000
#> ERR978197 5 0.5696 0.308 0 0.096 0.344 0.000 0.560
#> ERR978198 5 0.5165 0.292 0 0.048 0.376 0.000 0.576
#> ERR978199 5 0.5071 0.150 0 0.036 0.424 0.000 0.540
#> ERR978200 5 0.4989 0.182 0 0.032 0.416 0.000 0.552
#> ERR978201 5 0.5195 0.259 0 0.048 0.388 0.000 0.564
#> ERR978202 5 0.5405 0.261 0 0.064 0.380 0.000 0.556
#> ERR978203 5 0.5793 0.280 0 0.104 0.348 0.000 0.548
#> ERR978204 5 0.3656 0.693 0 0.196 0.020 0.000 0.784
#> ERR978205 5 0.3241 0.744 0 0.144 0.024 0.000 0.832
#> ERR978206 5 0.3197 0.748 0 0.140 0.024 0.000 0.836
#> ERR978207 5 0.3197 0.748 0 0.140 0.024 0.000 0.836
#> ERR978208 5 0.3197 0.748 0 0.140 0.024 0.000 0.836
#> ERR978209 5 0.3106 0.753 0 0.132 0.024 0.000 0.844
#> ERR978210 5 0.3409 0.730 0 0.160 0.024 0.000 0.816
#> ERR978211 5 0.3656 0.693 0 0.196 0.020 0.000 0.784
#> ERR978212 5 0.1341 0.796 0 0.056 0.000 0.000 0.944
#> ERR978213 5 0.1270 0.797 0 0.052 0.000 0.000 0.948
#> ERR978214 5 0.1270 0.797 0 0.052 0.000 0.000 0.948
#> ERR978215 5 0.1270 0.797 0 0.052 0.000 0.000 0.948
#> ERR978216 5 0.1270 0.797 0 0.052 0.000 0.000 0.948
#> ERR978217 5 0.1341 0.797 0 0.056 0.000 0.000 0.944
#> ERR978218 5 0.1341 0.797 0 0.056 0.000 0.000 0.944
#> ERR978219 5 0.1410 0.795 0 0.060 0.000 0.000 0.940
#> ERR978220 5 0.1270 0.797 0 0.052 0.000 0.000 0.948
#> ERR978221 5 0.1270 0.797 0 0.052 0.000 0.000 0.948
#> ERR978222 5 0.1270 0.797 0 0.052 0.000 0.000 0.948
#> ERR978223 5 0.1270 0.797 0 0.052 0.000 0.000 0.948
#> ERR978224 5 0.1270 0.797 0 0.052 0.000 0.000 0.948
#> ERR978225 5 0.1341 0.797 0 0.056 0.000 0.000 0.944
#> ERR978226 5 0.1270 0.797 0 0.052 0.000 0.000 0.948
#> ERR978227 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978228 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978229 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978230 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978231 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978232 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978233 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978234 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978235 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978236 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978237 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978238 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978239 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978240 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> ERR978241 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978242 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978243 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978244 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978245 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978246 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978247 4 0.0000 0.999 0 0.000 0.000 1.000 0.000
#> ERR978248 2 0.0963 0.964 0 0.964 0.000 0.000 0.036
#> ERR978249 2 0.0865 0.973 0 0.972 0.000 0.004 0.024
#> ERR978250 2 0.1485 0.955 0 0.948 0.000 0.020 0.032
#> ERR978251 2 0.1753 0.945 0 0.936 0.000 0.032 0.032
#> ERR978252 2 0.1168 0.964 0 0.960 0.000 0.008 0.032
#> ERR978253 2 0.0955 0.969 0 0.968 0.000 0.004 0.028
#> ERR978254 2 0.0703 0.974 0 0.976 0.000 0.000 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> ERR978107 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978108 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978109 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978110 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978111 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978112 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978113 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978114 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978115 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978116 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978117 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
#> ERR978118 2 0.0000 0.919 0 1.000 0.000 0.000 0.000 0.000
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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