Date: 2021-01-11 00:37:22 CST, cola version: 1.9.0.1013
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First the variable is renamed to res_list.
res_list = final
All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 1 methods.
#> On a matrix with 13 rows and 2737 columns.
#> Top rows are extracted by 'ATC' methods.
#> Subgroups are detected by 'skmeans' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6, 7, 8, 9, 10.
#> Performed in total 450 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["ATC", "skmeans"] or object["ATC:skmeans"]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = mat_adj, top_value_method = "ATC", partition_method = "skmeans",
#> max_k = 10, top_n = 13, mc.cores = 8)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 13 2737
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 = 1)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partitioning methods. Clicking on the method name in
the table goes to the corresponding 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 | ||
|---|---|---|---|---|---|
| ATC:skmeans | 2 | 0.679 | 0.858 | 0.937 |
**: 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 = 1)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 1)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 1)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 1)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 1)
collect_plots(res_list, k = 7, fun = consensus_heatmap, mc.cores = 1)
collect_plots(res_list, k = 8, fun = consensus_heatmap, mc.cores = 1)
collect_plots(res_list, k = 9, fun = consensus_heatmap, mc.cores = 1)
collect_plots(res_list, k = 10, fun = consensus_heatmap, mc.cores = 1)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 1)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 1)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 1)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 1)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 1)
collect_plots(res_list, k = 7, fun = membership_heatmap, mc.cores = 1)
collect_plots(res_list, k = 8, fun = membership_heatmap, mc.cores = 1)
collect_plots(res_list, k = 9, fun = membership_heatmap, mc.cores = 1)
collect_plots(res_list, k = 10, fun = membership_heatmap, mc.cores = 1)
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 = 1)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 1)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 1)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 1)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 1)
collect_plots(res_list, k = 7, fun = get_signatures, mc.cores = 1)
collect_plots(res_list, k = 8, fun = get_signatures, mc.cores = 1)
collect_plots(res_list, k = 9, fun = get_signatures, mc.cores = 1)
collect_plots(res_list, k = 10, fun = get_signatures, mc.cores = 1)
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
#> ATC:skmeans 2 0.679 0.858 0.937 0.463 0.537 0.537
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> ATC:skmeans 3 0.762 0.851 0.931 0.399 0.732 0.534
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> ATC:skmeans 4 0.687 0.732 0.834 0.0961 0.898 0.72
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> ATC:skmeans 5 0.768 0.783 0.893 0.0656 0.932 0.768
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> ATC:skmeans 6 0.76 0.707 0.839 0.0399 0.944 0.777
get_stats(res_list, k = 7)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> ATC:skmeans 7 0.759 0.655 0.788 0.0271 0.94 0.737
get_stats(res_list, k = 8)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> ATC:skmeans 8 0.77 0.661 0.784 0.0209 0.963 0.811
get_stats(res_list, k = 9)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> ATC:skmeans 9 0.766 0.608 0.775 0.0193 0.968 0.823
get_stats(res_list, k = 10)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> ATC:skmeans 10 0.778 0.565 0.746 0.0185 0.972 0.828
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_stats(res_list, k = 7)
collect_stats(res_list, k = 8)
collect_stats(res_list, k = 9)
collect_stats(res_list, k = 10)
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)
collect_classes(res_list, k = 7)
collect_classes(res_list, k = 8)
collect_classes(res_list, k = 9)
collect_classes(res_list, k = 10)
Heatmaps of the top rows:
top_rows_heatmap(res_list, top_n = 13)
The object with results only for a single top-value method and a single partitioning 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, 7, 8, 9, 10.
#> On a matrix with 13 rows and 2737 columns.
#> Top rows (13) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 450 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] "predict_classes" "rownames" "select_partition_number"
#> [28] "show" "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
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, higher 1-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 0.679 0.858 0.937 0.4626 0.537 0.537
#> 3 3 0.762 0.851 0.931 0.3990 0.732 0.534
#> 4 4 0.687 0.732 0.834 0.0961 0.898 0.720
#> 5 5 0.768 0.783 0.893 0.0656 0.932 0.768
#> 6 6 0.760 0.707 0.839 0.0399 0.944 0.777
#> 7 7 0.759 0.655 0.788 0.0271 0.940 0.737
#> 8 8 0.770 0.661 0.784 0.0209 0.963 0.811
#> 9 9 0.766 0.608 0.775 0.0193 0.968 0.823
#> 10 10 0.778 0.565 0.746 0.0185 0.972 0.828
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is 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 subgroup
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
#> SP1003 2 0.000 0.90780 0.00 1.00
#> SP10084 2 0.000 0.90780 0.00 1.00
#> SP1009 1 0.904 0.55054 0.68 0.32
#> SP10150 2 0.327 0.88640 0.06 0.94
#> SP101515 2 0.855 0.62118 0.28 0.72
#> SP101519 2 0.000 0.90780 0.00 1.00
#> SP101521 2 0.000 0.90780 0.00 1.00
#> SP101523 1 0.827 0.66400 0.74 0.26
#> SP101526 2 0.000 0.90780 0.00 1.00
#> SP101528 2 0.529 0.83826 0.12 0.88
#> SP101532 1 0.680 0.78093 0.82 0.18
#> SP101536 2 0.242 0.89595 0.04 0.96
#> SP101540 2 0.000 0.90780 0.00 1.00
#> SP101544 2 0.000 0.90780 0.00 1.00
#> SP101548 2 0.999 0.09622 0.48 0.52
#> SP101552 2 0.000 0.90780 0.00 1.00
#> SP101558 1 0.760 0.72593 0.78 0.22
#> SP101564 2 0.000 0.90780 0.00 1.00
#> SP101572 2 0.141 0.90316 0.02 0.98
#> SP101576 2 0.000 0.90780 0.00 1.00
#> SP101580 2 0.000 0.90780 0.00 1.00
#> SP101584 2 0.000 0.90780 0.00 1.00
#> SP101588 2 0.000 0.90780 0.00 1.00
#> SP101592 2 0.000 0.90780 0.00 1.00
#> SP101596 2 0.000 0.90780 0.00 1.00
#> SP101600 1 0.795 0.69454 0.76 0.24
#> SP101604 1 0.827 0.66257 0.74 0.26
#> SP101610 1 0.990 0.23082 0.56 0.44
#> SP101616 2 0.000 0.90780 0.00 1.00
#> SP101622 2 0.000 0.90780 0.00 1.00
#> SP101628 2 0.000 0.90780 0.00 1.00
#> SP101634 1 0.943 0.45989 0.64 0.36
#> SP101642 2 0.000 0.90780 0.00 1.00
#> SP101648 1 0.999 0.09111 0.52 0.48
#> SP101654 2 0.000 0.90780 0.00 1.00
#> SP101658 1 0.000 0.94439 1.00 0.00
#> SP101662 1 0.469 0.86690 0.90 0.10
#> SP101666 1 0.760 0.72594 0.78 0.22
#> SP101670 2 0.000 0.90780 0.00 1.00
#> SP101674 2 0.000 0.90780 0.00 1.00
#> SP101678 2 0.000 0.90780 0.00 1.00
#> SP101682 2 0.000 0.90780 0.00 1.00
#> SP101686 2 0.000 0.90780 0.00 1.00
#> SP101690 2 0.000 0.90780 0.00 1.00
#> SP101694 2 0.141 0.90316 0.02 0.98
#> SP101700 2 0.141 0.90171 0.02 0.98
#> SP101708 1 0.981 0.29632 0.58 0.42
#> SP101716 2 0.000 0.90780 0.00 1.00
#> SP101724 2 0.000 0.90780 0.00 1.00
#> SP101732 2 0.000 0.90780 0.00 1.00
#> SP101740 2 0.000 0.90780 0.00 1.00
#> SP101795 2 0.000 0.90780 0.00 1.00
#> SP101845 2 0.000 0.90780 0.00 1.00
#> SP101881 1 1.000 0.00885 0.50 0.50
#> SP101891 2 0.000 0.90780 0.00 1.00
#> SP101921 2 0.000 0.90780 0.00 1.00
#> SP101931 2 0.000 0.90780 0.00 1.00
#> SP102015 2 0.000 0.90780 0.00 1.00
#> SP102035 2 0.999 0.07677 0.48 0.52
#> SP102045 2 0.141 0.90316 0.02 0.98
#> SP102055 1 0.722 0.75321 0.80 0.20
#> SP102064 1 0.402 0.88371 0.92 0.08
#> SP102074 2 0.000 0.90780 0.00 1.00
#> SP102084 1 0.971 0.35326 0.60 0.40
#> SP102090 2 0.000 0.90780 0.00 1.00
#> SP102096 2 0.000 0.90780 0.00 1.00
#> SP102103 2 0.000 0.90780 0.00 1.00
#> SP102113 2 0.999 0.06798 0.48 0.52
#> SP102123 2 0.000 0.90780 0.00 1.00
#> SP102133 2 0.000 0.90780 0.00 1.00
#> SP102143 2 0.795 0.69735 0.24 0.76
#> SP102161 2 0.402 0.87577 0.08 0.92
#> SP102168 2 0.680 0.77482 0.18 0.82
#> SP102174 2 0.000 0.90780 0.00 1.00
#> SP102187 1 0.680 0.78120 0.82 0.18
#> SP102485 1 0.000 0.94439 1.00 0.00
#> SP102489 1 0.000 0.94439 1.00 0.00
#> SP102499 1 0.000 0.94439 1.00 0.00
#> SP102507 1 0.402 0.88371 0.92 0.08
#> SP102511 1 0.000 0.94439 1.00 0.00
#> SP102517 1 0.999 -0.06455 0.52 0.48
#> SP102523 1 0.958 0.31009 0.62 0.38
#> SP102529 2 0.999 0.20520 0.48 0.52
#> SP102537 1 0.000 0.94439 1.00 0.00
#> SP102541 2 0.958 0.48163 0.38 0.62
#> SP102547 1 0.000 0.94439 1.00 0.00
#> SP102557 1 0.000 0.94439 1.00 0.00
#> SP102561 1 0.000 0.94439 1.00 0.00
#> SP102567 1 0.990 0.09604 0.56 0.44
#> SP102573 1 0.584 0.80112 0.86 0.14
#> SP102581 1 0.000 0.94439 1.00 0.00
#> SP102591 1 0.000 0.94439 1.00 0.00
#> SP102597 1 0.000 0.94439 1.00 0.00
#> SP102605 1 0.000 0.94439 1.00 0.00
#> SP102611 2 0.242 0.89595 0.04 0.96
#> SP102617 1 0.000 0.94439 1.00 0.00
#> SP102620 1 0.141 0.93033 0.98 0.02
#> SP102622 2 0.958 0.47831 0.38 0.62
#> SP102626 1 0.141 0.93033 0.98 0.02
#> SP102630 1 0.999 0.08349 0.52 0.48
#> SP102633 1 0.000 0.94439 1.00 0.00
#> SP102647 1 0.469 0.86690 0.90 0.10
#> SP102652 2 0.584 0.82076 0.14 0.86
#> SP102690 1 0.000 0.94439 1.00 0.00
#> SP102716 1 0.000 0.94439 1.00 0.00
#> SP102718 1 0.000 0.94439 1.00 0.00
#> SP102733 1 0.000 0.94439 1.00 0.00
#> SP102741 1 0.000 0.94439 1.00 0.00
#> SP102747 1 0.000 0.94439 1.00 0.00
#> SP102755 1 0.000 0.94439 1.00 0.00
#> SP102759 1 0.000 0.94439 1.00 0.00
#> SP102783 1 0.000 0.94439 1.00 0.00
#> SP102804 1 0.000 0.94439 1.00 0.00
#> SP102816 1 0.000 0.94439 1.00 0.00
#> SP102827 2 1.000 0.13899 0.50 0.50
#> SP102839 1 0.000 0.94439 1.00 0.00
#> SP102873 1 0.000 0.94439 1.00 0.00
#> SP102881 1 0.000 0.94439 1.00 0.00
#> SP102897 1 0.000 0.94439 1.00 0.00
#> SP102913 2 0.529 0.85049 0.12 0.88
#> SP102921 1 0.000 0.94439 1.00 0.00
#> SP102929 1 0.000 0.94439 1.00 0.00
#> SP102945 1 0.000 0.94439 1.00 0.00
#> SP102957 1 0.000 0.94439 1.00 0.00
#> SP102965 1 0.000 0.94439 1.00 0.00
#> SP102973 1 0.000 0.94439 1.00 0.00
#> SP102989 1 0.000 0.94439 1.00 0.00
#> SP103005 2 0.855 0.66635 0.28 0.72
#> SP103021 1 0.971 0.23871 0.60 0.40
#> SP103037 1 0.000 0.94439 1.00 0.00
#> SP103045 2 0.795 0.72186 0.24 0.76
#> SP103057 1 0.000 0.94439 1.00 0.00
#> SP103065 1 0.000 0.94439 1.00 0.00
#> SP103080 1 0.795 0.66806 0.76 0.24
#> SP103100 1 0.000 0.94439 1.00 0.00
#> SP103128 1 0.000 0.94439 1.00 0.00
#> SP103140 1 0.000 0.94439 1.00 0.00
#> SP103156 1 0.000 0.94439 1.00 0.00
#> SP103197 1 0.000 0.94439 1.00 0.00
#> SP103213 1 0.000 0.94439 1.00 0.00
#> SP103221 1 0.000 0.94439 1.00 0.00
#> SP103233 1 0.000 0.94439 1.00 0.00
#> SP103245 1 0.000 0.94439 1.00 0.00
#> SP103261 1 0.000 0.94439 1.00 0.00
#> SP103288 1 0.000 0.94439 1.00 0.00
#> SP103300 1 0.000 0.94439 1.00 0.00
#> SP103340 1 0.000 0.94439 1.00 0.00
#> SP103396 1 0.000 0.94439 1.00 0.00
#> SP103408 1 0.000 0.94439 1.00 0.00
#> SP103416 1 0.242 0.91169 0.96 0.04
#> SP103428 1 0.000 0.94439 1.00 0.00
#> SP103436 1 0.000 0.94439 1.00 0.00
#> SP103444 1 0.000 0.94439 1.00 0.00
#> SP103455 2 0.760 0.73804 0.22 0.78
#> SP103467 1 0.000 0.94439 1.00 0.00
#> SP103483 1 0.000 0.94439 1.00 0.00
#> SP103507 1 0.000 0.94439 1.00 0.00
#> SP103515 1 0.000 0.94439 1.00 0.00
#> SP103523 1 0.000 0.94439 1.00 0.00
#> SP103535 2 0.855 0.66598 0.28 0.72
#> SP103547 1 0.000 0.94439 1.00 0.00
#> SP103555 1 0.000 0.94439 1.00 0.00
#> SP103575 2 0.402 0.87577 0.08 0.92
#> SP103595 1 1.000 -0.13730 0.50 0.50
#> SP103603 1 1.000 -0.13859 0.50 0.50
#> SP103619 1 0.000 0.94439 1.00 0.00
#> SP103673 1 0.000 0.94439 1.00 0.00
#> SP103679 1 0.000 0.94439 1.00 0.00
#> SP103685 1 0.000 0.94439 1.00 0.00
#> SP103694 1 0.000 0.94439 1.00 0.00
#> SP103706 1 0.000 0.94439 1.00 0.00
#> SP103715 1 0.000 0.94439 1.00 0.00
#> SP103730 1 0.000 0.94439 1.00 0.00
#> SP103742 1 0.000 0.94439 1.00 0.00
#> SP103826 1 0.000 0.94439 1.00 0.00
#> SP103844 1 0.000 0.94439 1.00 0.00
#> SP103856 1 0.000 0.94439 1.00 0.00
#> SP103866 1 0.000 0.94439 1.00 0.00
#> SP103894 2 0.402 0.87767 0.08 0.92
#> SP104056 1 0.000 0.94439 1.00 0.00
#> SP104330 1 0.000 0.94439 1.00 0.00
#> SP104530 2 0.000 0.90780 0.00 1.00
#> SP10470 2 0.000 0.90780 0.00 1.00
#> SP104984 1 0.584 0.81532 0.86 0.14
#> SP105006 2 0.402 0.87577 0.08 0.92
#> SP105018 2 0.000 0.90780 0.00 1.00
#> SP105086 2 0.529 0.84118 0.12 0.88
#> SP105159 1 0.000 0.94439 1.00 0.00
#> SP105213 1 0.000 0.94439 1.00 0.00
#> SP105253 2 0.000 0.90780 0.00 1.00
#> SP105261 2 0.000 0.90780 0.00 1.00
#> SP105375 2 0.000 0.90780 0.00 1.00
#> SP105425 1 0.827 0.66293 0.74 0.26
#> SP105577 1 0.904 0.54874 0.68 0.32
#> SP10563 2 0.000 0.90780 0.00 1.00
#> SP105673 2 0.999 0.08361 0.48 0.52
#> SP105708 1 0.000 0.94439 1.00 0.00
#> SP105759 1 0.000 0.94439 1.00 0.00
#> SP105807 1 0.000 0.94439 1.00 0.00
#> SP1059 2 0.000 0.90780 0.00 1.00
#> [ reached 'max' / getOption("max.print") -- omitted 2537 rows ]
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SP1003 2 0.0000 0.9233 0.00 1.00 0.00
#> SP10084 2 0.0000 0.9233 0.00 1.00 0.00
#> SP1009 3 0.0000 0.8669 0.00 0.00 1.00
#> SP10150 2 0.0892 0.9147 0.02 0.98 0.00
#> SP101515 3 0.0000 0.8669 0.00 0.00 1.00
#> SP101519 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101521 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101523 3 0.0892 0.8683 0.02 0.00 0.98
#> SP101526 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101528 3 0.0000 0.8669 0.00 0.00 1.00
#> SP101532 3 0.0892 0.8678 0.02 0.00 0.98
#> SP101536 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101540 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101544 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101548 3 0.0000 0.8669 0.00 0.00 1.00
#> SP101552 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101558 3 0.3340 0.8339 0.12 0.00 0.88
#> SP101564 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101572 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101576 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101580 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101584 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101588 2 0.1529 0.8930 0.00 0.96 0.04
#> SP101592 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101596 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101600 3 0.1529 0.8663 0.04 0.00 0.96
#> SP101604 3 0.0000 0.8669 0.00 0.00 1.00
#> SP101610 3 0.0000 0.8669 0.00 0.00 1.00
#> SP101616 2 0.4291 0.7345 0.00 0.82 0.18
#> SP101622 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101628 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101634 3 0.1529 0.8654 0.04 0.00 0.96
#> SP101642 2 0.0892 0.9097 0.00 0.98 0.02
#> SP101648 3 0.0000 0.8669 0.00 0.00 1.00
#> SP101654 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101658 3 0.5948 0.5244 0.36 0.00 0.64
#> SP101662 3 0.0000 0.8669 0.00 0.00 1.00
#> SP101666 3 0.0000 0.8669 0.00 0.00 1.00
#> SP101670 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101674 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101678 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101682 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101686 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101690 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101694 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101700 3 0.2959 0.8175 0.00 0.10 0.90
#> SP101708 3 0.0000 0.8669 0.00 0.00 1.00
#> SP101716 2 0.6244 0.1654 0.00 0.56 0.44
#> SP101724 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101732 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101740 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101795 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101845 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101881 3 0.0000 0.8669 0.00 0.00 1.00
#> SP101891 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101921 2 0.0000 0.9233 0.00 1.00 0.00
#> SP101931 2 0.0000 0.9233 0.00 1.00 0.00
#> SP102015 2 0.0000 0.9233 0.00 1.00 0.00
#> SP102035 3 0.0000 0.8669 0.00 0.00 1.00
#> SP102045 2 0.0000 0.9233 0.00 1.00 0.00
#> SP102055 3 0.0000 0.8669 0.00 0.00 1.00
#> SP102064 3 0.0000 0.8669 0.00 0.00 1.00
#> SP102074 2 0.0000 0.9233 0.00 1.00 0.00
#> SP102084 3 0.0892 0.8683 0.02 0.00 0.98
#> SP102090 2 0.4555 0.6997 0.00 0.80 0.20
#> SP102096 2 0.0000 0.9233 0.00 1.00 0.00
#> SP102103 2 0.0000 0.9233 0.00 1.00 0.00
#> SP102113 3 0.0000 0.8669 0.00 0.00 1.00
#> SP102123 2 0.0000 0.9233 0.00 1.00 0.00
#> SP102133 2 0.0000 0.9233 0.00 1.00 0.00
#> SP102143 3 0.0000 0.8669 0.00 0.00 1.00
#> SP102161 2 0.0892 0.9147 0.02 0.98 0.00
#> SP102168 3 0.7395 0.3871 0.04 0.38 0.58
#> SP102174 2 0.0000 0.9233 0.00 1.00 0.00
#> SP102187 3 0.2537 0.8543 0.08 0.00 0.92
#> SP102485 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102489 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102499 1 0.2537 0.8799 0.92 0.00 0.08
#> SP102507 3 0.2537 0.8603 0.08 0.00 0.92
#> SP102511 1 0.2959 0.8617 0.90 0.00 0.10
#> SP102517 2 0.6045 0.4516 0.38 0.62 0.00
#> SP102523 1 0.6280 0.0653 0.54 0.46 0.00
#> SP102529 2 0.5948 0.4959 0.36 0.64 0.00
#> SP102537 1 0.6309 -0.1001 0.50 0.00 0.50
#> SP102541 2 0.5560 0.6149 0.30 0.70 0.00
#> SP102547 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102557 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102561 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102567 2 0.6302 0.1721 0.48 0.52 0.00
#> SP102573 1 0.3340 0.8281 0.88 0.12 0.00
#> SP102581 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102591 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102597 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102605 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102611 2 0.0000 0.9233 0.00 1.00 0.00
#> SP102617 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102620 3 0.1529 0.8664 0.04 0.00 0.96
#> SP102622 2 0.5216 0.6783 0.26 0.74 0.00
#> SP102626 3 0.1529 0.8664 0.04 0.00 0.96
#> SP102630 3 0.0000 0.8669 0.00 0.00 1.00
#> SP102633 3 0.4796 0.7493 0.22 0.00 0.78
#> SP102647 3 0.0000 0.8669 0.00 0.00 1.00
#> SP102652 3 0.0000 0.8669 0.00 0.00 1.00
#> SP102690 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102716 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102718 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102733 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102741 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102747 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102755 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102759 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102783 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102804 3 0.5560 0.6264 0.30 0.00 0.70
#> SP102816 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102827 2 0.5835 0.5400 0.34 0.66 0.00
#> SP102839 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102873 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102881 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102897 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102913 2 0.3340 0.8373 0.12 0.88 0.00
#> SP102921 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102929 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102945 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102957 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102965 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102973 1 0.0000 0.9561 1.00 0.00 0.00
#> SP102989 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103005 2 0.5216 0.6792 0.26 0.74 0.00
#> SP103021 1 0.6192 0.2012 0.58 0.42 0.00
#> SP103037 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103045 2 0.4796 0.7272 0.22 0.78 0.00
#> SP103057 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103065 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103080 1 0.9083 0.2043 0.52 0.16 0.32
#> SP103100 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103128 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103140 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103156 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103197 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103213 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103221 3 0.6280 0.2941 0.46 0.00 0.54
#> SP103233 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103245 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103261 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103288 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103300 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103340 1 0.4002 0.7777 0.84 0.00 0.16
#> SP103396 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103408 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103416 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103428 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103436 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103444 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103455 3 0.6407 0.7275 0.08 0.16 0.76
#> SP103467 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103483 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103507 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103515 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103523 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103535 2 0.2537 0.8749 0.08 0.92 0.00
#> SP103547 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103555 1 0.0000 0.9561 1.00 0.00 0.00
#> SP103575 2 0.2066 0.8908 0.06 0.94 0.00
#> SP103595 2 0.6302 0.1721 0.48 0.52 0.00
#> SP103603 2 0.6280 0.2365 0.46 0.54 0.00
#> SP103619 1 0.0000 0.9561 1.00 0.00 0.00
#> [ reached 'max' / getOption("max.print") -- omitted 2571 rows ]
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SP1003 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP10084 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP1009 4 0.3975 0.6823 0.00 0.00 0.24 0.76
#> SP10150 2 0.3606 0.7611 0.02 0.84 0.00 0.14
#> SP101515 3 0.4948 0.1715 0.00 0.00 0.56 0.44
#> SP101519 2 0.3037 0.7594 0.00 0.88 0.02 0.10
#> SP101521 2 0.6976 0.7339 0.00 0.58 0.18 0.24
#> SP101523 3 0.3335 0.6568 0.02 0.00 0.86 0.12
#> SP101526 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101528 4 0.3975 0.6823 0.00 0.00 0.24 0.76
#> SP101532 3 0.5062 0.4786 0.02 0.00 0.68 0.30
#> SP101536 2 0.3821 0.7671 0.00 0.84 0.04 0.12
#> SP101540 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101544 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101548 4 0.3975 0.6823 0.00 0.00 0.24 0.76
#> SP101552 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101558 3 0.3611 0.6585 0.06 0.00 0.86 0.08
#> SP101564 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101572 2 0.5594 0.7734 0.00 0.72 0.10 0.18
#> SP101576 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101580 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101584 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101588 2 0.5657 0.7719 0.00 0.72 0.12 0.16
#> SP101592 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101596 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101600 3 0.3611 0.6550 0.08 0.00 0.86 0.06
#> SP101604 4 0.4277 0.6311 0.00 0.00 0.28 0.72
#> SP101610 3 0.4624 0.4147 0.00 0.00 0.66 0.34
#> SP101616 3 0.7004 0.0783 0.00 0.20 0.58 0.22
#> SP101622 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101628 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101634 3 0.3335 0.6564 0.02 0.00 0.86 0.12
#> SP101642 2 0.7653 0.5767 0.00 0.46 0.30 0.24
#> SP101648 3 0.2921 0.6463 0.00 0.00 0.86 0.14
#> SP101654 2 0.7583 0.6277 0.00 0.48 0.28 0.24
#> SP101658 3 0.6595 0.5020 0.24 0.04 0.66 0.06
#> SP101662 4 0.4907 0.3437 0.00 0.00 0.42 0.58
#> SP101666 3 0.4522 0.4534 0.00 0.00 0.68 0.32
#> SP101670 2 0.5383 0.7661 0.00 0.74 0.10 0.16
#> SP101674 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101678 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101682 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101686 2 0.5594 0.7728 0.00 0.72 0.10 0.18
#> SP101690 2 0.7394 0.6770 0.00 0.52 0.24 0.24
#> SP101694 2 0.4949 0.7727 0.00 0.76 0.06 0.18
#> SP101700 3 0.3935 0.6462 0.00 0.10 0.84 0.06
#> SP101708 3 0.2921 0.6463 0.00 0.00 0.86 0.14
#> SP101716 3 0.6976 0.0923 0.00 0.18 0.58 0.24
#> SP101724 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101732 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101740 2 0.7135 0.7115 0.00 0.56 0.20 0.24
#> SP101795 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101845 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101881 3 0.2921 0.6463 0.00 0.00 0.86 0.14
#> SP101891 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP101921 2 0.0000 0.7260 0.00 1.00 0.00 0.00
#> SP101931 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP102015 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP102035 3 0.4522 0.4534 0.00 0.00 0.68 0.32
#> SP102045 2 0.6104 0.7720 0.00 0.68 0.14 0.18
#> SP102055 3 0.4855 0.2924 0.00 0.00 0.60 0.40
#> SP102064 3 0.4522 0.4534 0.00 0.00 0.68 0.32
#> SP102074 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP102084 3 0.3037 0.6657 0.02 0.00 0.88 0.10
#> SP102090 3 0.6594 0.2038 0.00 0.14 0.62 0.24
#> SP102096 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP102103 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP102113 3 0.2921 0.6463 0.00 0.00 0.86 0.14
#> SP102123 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP102133 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP102143 3 0.4855 0.2681 0.00 0.00 0.60 0.40
#> SP102161 2 0.0707 0.7215 0.02 0.98 0.00 0.00
#> SP102168 3 0.3853 0.5545 0.00 0.02 0.82 0.16
#> SP102174 2 0.6594 0.7638 0.00 0.62 0.14 0.24
#> SP102187 3 0.3611 0.6585 0.06 0.00 0.86 0.08
#> SP102485 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102489 1 0.3172 0.7808 0.84 0.16 0.00 0.00
#> SP102499 1 0.2345 0.8497 0.90 0.00 0.10 0.00
#> SP102507 4 0.5486 0.7088 0.08 0.00 0.20 0.72
#> SP102511 1 0.3037 0.8325 0.88 0.02 0.10 0.00
#> SP102517 2 0.4134 0.5032 0.26 0.74 0.00 0.00
#> SP102523 2 0.4624 0.3993 0.34 0.66 0.00 0.00
#> SP102529 2 0.3801 0.5373 0.22 0.78 0.00 0.00
#> SP102537 3 0.5636 0.4986 0.26 0.00 0.68 0.06
#> SP102541 2 0.3801 0.5373 0.22 0.78 0.00 0.00
#> SP102547 1 0.3172 0.7808 0.84 0.16 0.00 0.00
#> SP102557 1 0.2706 0.8578 0.90 0.02 0.08 0.00
#> SP102561 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102567 2 0.3975 0.5120 0.24 0.76 0.00 0.00
#> SP102573 1 0.4994 0.1609 0.52 0.48 0.00 0.00
#> SP102581 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102591 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102597 1 0.2011 0.8740 0.92 0.00 0.08 0.00
#> SP102605 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102611 2 0.3198 0.7566 0.00 0.88 0.08 0.04
#> SP102617 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102620 4 0.4755 0.7227 0.04 0.00 0.20 0.76
#> SP102622 2 0.2011 0.6906 0.08 0.92 0.00 0.00
#> SP102626 4 0.4755 0.7227 0.04 0.00 0.20 0.76
#> SP102630 4 0.4134 0.6592 0.00 0.00 0.26 0.74
#> SP102633 4 0.5077 0.7421 0.16 0.00 0.08 0.76
#> SP102647 4 0.3975 0.6823 0.00 0.00 0.24 0.76
#> SP102652 4 0.5077 0.6817 0.00 0.08 0.16 0.76
#> SP102690 1 0.0707 0.9362 0.98 0.00 0.00 0.02
#> SP102716 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102718 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102733 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102741 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102747 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102755 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102759 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102783 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102804 4 0.4755 0.7305 0.20 0.00 0.04 0.76
#> SP102816 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102827 2 0.4406 0.4573 0.30 0.70 0.00 0.00
#> SP102839 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102873 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102881 1 0.0707 0.9362 0.98 0.00 0.00 0.02
#> SP102897 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102913 2 0.2011 0.6893 0.08 0.92 0.00 0.00
#> SP102921 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102929 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102945 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102957 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102965 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102973 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP102989 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP103005 2 0.2647 0.6518 0.12 0.88 0.00 0.00
#> SP103021 2 0.4406 0.4276 0.30 0.70 0.00 0.00
#> SP103037 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP103045 2 0.2345 0.6715 0.10 0.90 0.00 0.00
#> SP103057 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP103065 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP103080 4 0.7832 0.2704 0.26 0.36 0.00 0.38
#> SP103100 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP103128 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP103140 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP103156 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP103197 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP103213 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> SP103221 3 0.4994 0.1912 0.48 0.00 0.52 0.00
#> SP103233 1 0.0000 0.9544 1.00 0.00 0.00 0.00
#> [ reached 'max' / getOption("max.print") -- omitted 2595 rows ]
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SP1003 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP10084 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP1009 4 0.1043 0.7853 0.00 0.00 0.04 0.96 0.00
#> SP10150 2 0.4287 0.2284 0.00 0.54 0.00 0.00 0.46
#> SP101515 3 0.4262 0.2620 0.00 0.00 0.56 0.44 0.00
#> SP101519 5 0.4060 0.3771 0.00 0.36 0.00 0.00 0.64
#> SP101521 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101523 3 0.0609 0.7715 0.02 0.00 0.98 0.00 0.00
#> SP101526 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101528 4 0.3109 0.6982 0.00 0.00 0.20 0.80 0.00
#> SP101532 3 0.4675 0.5364 0.02 0.00 0.60 0.38 0.00
#> SP101536 2 0.4182 0.4064 0.00 0.60 0.00 0.00 0.40
#> SP101540 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101544 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101548 4 0.0609 0.7944 0.00 0.00 0.02 0.98 0.00
#> SP101552 2 0.0609 0.8695 0.00 0.98 0.00 0.00 0.02
#> SP101558 3 0.0609 0.7715 0.02 0.00 0.98 0.00 0.00
#> SP101564 2 0.0609 0.8693 0.00 0.98 0.00 0.00 0.02
#> SP101572 2 0.2929 0.7559 0.00 0.82 0.00 0.00 0.18
#> SP101576 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101580 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101584 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101588 2 0.4060 0.4593 0.00 0.64 0.00 0.00 0.36
#> SP101592 2 0.0609 0.8693 0.00 0.98 0.00 0.00 0.02
#> SP101596 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101600 3 0.0609 0.7715 0.02 0.00 0.98 0.00 0.00
#> SP101604 4 0.2516 0.7315 0.00 0.00 0.14 0.86 0.00
#> SP101610 3 0.3561 0.6052 0.00 0.00 0.74 0.26 0.00
#> SP101616 2 0.3983 0.5096 0.00 0.66 0.34 0.00 0.00
#> SP101622 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101628 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101634 3 0.1732 0.7610 0.00 0.00 0.92 0.08 0.00
#> SP101642 2 0.1732 0.8318 0.00 0.92 0.08 0.00 0.00
#> SP101648 3 0.0609 0.7690 0.00 0.00 0.98 0.02 0.00
#> SP101654 2 0.0609 0.8681 0.00 0.98 0.02 0.00 0.00
#> SP101658 3 0.6484 0.5681 0.22 0.00 0.60 0.14 0.04
#> SP101662 4 0.4227 0.0702 0.00 0.00 0.42 0.58 0.00
#> SP101666 3 0.3983 0.5341 0.00 0.00 0.66 0.34 0.00
#> SP101670 2 0.4307 0.0559 0.00 0.50 0.00 0.00 0.50
#> SP101674 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101678 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101682 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101686 2 0.3561 0.6449 0.00 0.74 0.00 0.00 0.26
#> SP101690 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101694 2 0.3895 0.5586 0.00 0.68 0.00 0.00 0.32
#> SP101700 3 0.3291 0.7287 0.00 0.00 0.84 0.04 0.12
#> SP101708 3 0.0609 0.7690 0.00 0.00 0.98 0.02 0.00
#> SP101716 2 0.4060 0.4773 0.00 0.64 0.36 0.00 0.00
#> SP101724 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101732 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101740 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101795 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101845 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101881 3 0.0609 0.7690 0.00 0.00 0.98 0.02 0.00
#> SP101891 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP101921 5 0.0000 0.8625 0.00 0.00 0.00 0.00 1.00
#> SP101931 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP102015 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP102035 3 0.2280 0.7348 0.00 0.00 0.88 0.12 0.00
#> SP102045 2 0.2280 0.8116 0.00 0.88 0.00 0.00 0.12
#> SP102055 3 0.4126 0.4189 0.00 0.00 0.62 0.38 0.00
#> SP102064 3 0.3561 0.6052 0.00 0.00 0.74 0.26 0.00
#> SP102074 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP102084 3 0.0609 0.7690 0.00 0.00 0.98 0.02 0.00
#> SP102090 2 0.3274 0.7053 0.00 0.78 0.22 0.00 0.00
#> SP102096 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP102103 2 0.2280 0.8095 0.00 0.88 0.00 0.00 0.12
#> SP102113 3 0.0609 0.7690 0.00 0.00 0.98 0.02 0.00
#> SP102123 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP102133 2 0.2516 0.7931 0.00 0.86 0.00 0.00 0.14
#> SP102143 3 0.4126 0.4778 0.00 0.00 0.62 0.38 0.00
#> SP102161 5 0.0000 0.8625 0.00 0.00 0.00 0.00 1.00
#> SP102168 3 0.3109 0.6372 0.00 0.20 0.80 0.00 0.00
#> SP102174 2 0.0000 0.8742 0.00 1.00 0.00 0.00 0.00
#> SP102187 3 0.0609 0.7715 0.02 0.00 0.98 0.00 0.00
#> SP102485 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102489 1 0.3895 0.5339 0.68 0.00 0.00 0.00 0.32
#> SP102499 1 0.4458 0.6878 0.76 0.00 0.12 0.12 0.00
#> SP102507 4 0.0609 0.7974 0.02 0.00 0.00 0.98 0.00
#> SP102511 1 0.5032 0.6630 0.74 0.00 0.12 0.12 0.02
#> SP102517 5 0.2929 0.7347 0.18 0.00 0.00 0.00 0.82
#> SP102523 5 0.3274 0.6643 0.22 0.00 0.00 0.00 0.78
#> SP102529 5 0.1410 0.8548 0.06 0.00 0.00 0.00 0.94
#> SP102537 3 0.5599 0.5549 0.26 0.00 0.62 0.12 0.00
#> SP102541 5 0.0000 0.8625 0.00 0.00 0.00 0.00 1.00
#> SP102547 1 0.4840 0.4765 0.64 0.00 0.00 0.04 0.32
#> SP102557 1 0.4794 0.6938 0.76 0.00 0.10 0.12 0.02
#> SP102561 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102567 5 0.1410 0.8548 0.06 0.00 0.00 0.00 0.94
#> SP102573 5 0.3274 0.6643 0.22 0.00 0.00 0.00 0.78
#> SP102581 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102591 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102597 1 0.4458 0.6878 0.76 0.00 0.12 0.12 0.00
#> SP102605 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102611 5 0.3684 0.5782 0.00 0.28 0.00 0.00 0.72
#> SP102617 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102620 4 0.0000 0.7986 0.00 0.00 0.00 1.00 0.00
#> SP102622 5 0.1410 0.8548 0.06 0.00 0.00 0.00 0.94
#> SP102626 4 0.0000 0.7986 0.00 0.00 0.00 1.00 0.00
#> SP102630 4 0.2516 0.7219 0.00 0.00 0.14 0.86 0.00
#> SP102633 4 0.1043 0.8048 0.04 0.00 0.00 0.96 0.00
#> SP102647 4 0.0000 0.7986 0.00 0.00 0.00 1.00 0.00
#> SP102652 4 0.3513 0.7064 0.00 0.00 0.02 0.80 0.18
#> SP102690 1 0.0609 0.9351 0.98 0.00 0.00 0.02 0.00
#> SP102716 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102718 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102733 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102741 1 0.1043 0.9189 0.96 0.00 0.00 0.04 0.00
#> SP102747 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102755 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102759 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102783 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102804 4 0.3109 0.7488 0.20 0.00 0.00 0.80 0.00
#> SP102816 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102827 5 0.3684 0.6042 0.28 0.00 0.00 0.00 0.72
#> SP102839 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102873 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102881 1 0.1043 0.9183 0.96 0.00 0.00 0.04 0.00
#> SP102897 1 0.1043 0.9182 0.96 0.00 0.00 0.04 0.00
#> SP102913 5 0.0000 0.8625 0.00 0.00 0.00 0.00 1.00
#> SP102921 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102929 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102945 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102957 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> SP102965 1 0.0000 0.9524 1.00 0.00 0.00 0.00 0.00
#> [ reached 'max' / getOption("max.print") -- omitted 2612 rows ]
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SP1003 2 0.0937 0.8667 0.00 0.96 0.00 0.00 0.00 0.04
#> SP10084 2 0.0937 0.8667 0.00 0.96 0.00 0.00 0.00 0.04
#> SP1009 4 0.0000 0.6108 0.00 0.00 0.00 1.00 0.00 0.00
#> SP10150 2 0.4282 0.3508 0.00 0.56 0.00 0.00 0.42 0.02
#> SP101515 3 0.5012 0.5644 0.00 0.00 0.60 0.30 0.00 0.10
#> SP101519 5 0.5260 0.1120 0.00 0.44 0.02 0.02 0.50 0.02
#> SP101521 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101523 3 0.2094 0.7372 0.02 0.00 0.90 0.00 0.00 0.08
#> SP101526 2 0.0937 0.8667 0.00 0.96 0.00 0.00 0.00 0.04
#> SP101528 4 0.5747 0.5461 0.00 0.00 0.20 0.50 0.00 0.30
#> SP101532 4 0.5992 -0.1649 0.02 0.00 0.14 0.48 0.00 0.36
#> SP101536 2 0.4310 0.2816 0.00 0.54 0.00 0.00 0.44 0.02
#> SP101540 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101544 2 0.0937 0.8667 0.00 0.96 0.00 0.00 0.00 0.04
#> SP101548 4 0.1267 0.6111 0.00 0.00 0.00 0.94 0.00 0.06
#> SP101552 2 0.0547 0.8683 0.00 0.98 0.00 0.00 0.02 0.00
#> SP101558 3 0.2094 0.7372 0.02 0.00 0.90 0.00 0.00 0.08
#> SP101564 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101572 2 0.3163 0.7910 0.00 0.82 0.00 0.00 0.14 0.04
#> SP101576 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101580 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101584 2 0.0937 0.8667 0.00 0.96 0.00 0.00 0.00 0.04
#> SP101588 2 0.5262 0.4770 0.00 0.62 0.04 0.02 0.30 0.02
#> SP101592 2 0.0547 0.8684 0.00 0.98 0.00 0.00 0.00 0.02
#> SP101596 2 0.0937 0.8667 0.00 0.96 0.00 0.00 0.00 0.04
#> SP101600 3 0.2094 0.7372 0.02 0.00 0.90 0.00 0.00 0.08
#> SP101604 4 0.5371 0.5128 0.00 0.00 0.12 0.52 0.00 0.36
#> SP101610 6 0.5882 -0.1265 0.00 0.00 0.38 0.20 0.00 0.42
#> SP101616 3 0.3797 0.3028 0.00 0.42 0.58 0.00 0.00 0.00
#> SP101622 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101628 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101634 3 0.5888 0.3398 0.00 0.00 0.40 0.40 0.00 0.20
#> SP101642 2 0.1814 0.8088 0.00 0.90 0.10 0.00 0.00 0.00
#> SP101648 3 0.2350 0.7531 0.00 0.00 0.88 0.02 0.00 0.10
#> SP101654 2 0.0937 0.8558 0.00 0.96 0.04 0.00 0.00 0.00
#> SP101658 6 0.5529 0.6698 0.20 0.00 0.12 0.00 0.04 0.64
#> SP101662 6 0.5324 -0.1935 0.00 0.00 0.12 0.34 0.00 0.54
#> SP101666 6 0.5876 0.2943 0.00 0.00 0.26 0.26 0.00 0.48
#> SP101670 2 0.4310 0.1809 0.00 0.54 0.00 0.00 0.44 0.02
#> SP101674 2 0.0547 0.8684 0.00 0.98 0.00 0.00 0.00 0.02
#> SP101678 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101682 2 0.0937 0.8667 0.00 0.96 0.00 0.00 0.00 0.04
#> SP101686 2 0.2793 0.7303 0.00 0.80 0.00 0.00 0.20 0.00
#> SP101690 2 0.0547 0.8652 0.00 0.98 0.02 0.00 0.00 0.00
#> SP101694 2 0.3460 0.7148 0.00 0.76 0.00 0.00 0.22 0.02
#> SP101700 6 0.6200 0.1394 0.00 0.00 0.34 0.18 0.02 0.46
#> SP101708 3 0.1814 0.7433 0.00 0.00 0.90 0.00 0.00 0.10
#> SP101716 3 0.3828 0.2426 0.00 0.44 0.56 0.00 0.00 0.00
#> SP101724 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101732 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101740 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101795 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101845 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101881 3 0.2474 0.7546 0.00 0.00 0.88 0.04 0.00 0.08
#> SP101891 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP101921 5 0.2725 0.8082 0.00 0.00 0.02 0.04 0.88 0.06
#> SP101931 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP102015 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP102035 3 0.4873 0.6040 0.00 0.00 0.60 0.32 0.00 0.08
#> SP102045 2 0.2790 0.7961 0.00 0.84 0.00 0.00 0.14 0.02
#> SP102055 3 0.5071 0.4294 0.00 0.00 0.52 0.40 0.00 0.08
#> SP102064 6 0.3660 0.4450 0.00 0.00 0.16 0.06 0.00 0.78
#> SP102074 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP102084 3 0.1814 0.7433 0.00 0.00 0.90 0.00 0.00 0.10
#> SP102090 3 0.3851 0.1749 0.00 0.46 0.54 0.00 0.00 0.00
#> SP102096 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP102103 2 0.1814 0.8266 0.00 0.90 0.00 0.00 0.10 0.00
#> SP102113 3 0.1814 0.7433 0.00 0.00 0.90 0.00 0.00 0.10
#> SP102123 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP102133 2 0.1814 0.8266 0.00 0.90 0.00 0.00 0.10 0.00
#> SP102143 4 0.5371 -0.0521 0.00 0.00 0.12 0.52 0.00 0.36
#> SP102161 5 0.0000 0.8358 0.00 0.00 0.00 0.00 1.00 0.00
#> SP102168 3 0.3163 0.7235 0.00 0.14 0.82 0.00 0.00 0.04
#> SP102174 2 0.0000 0.8689 0.00 1.00 0.00 0.00 0.00 0.00
#> SP102187 3 0.2094 0.7372 0.02 0.00 0.90 0.00 0.00 0.08
#> SP102485 1 0.0000 0.9277 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102489 1 0.5945 0.2195 0.56 0.00 0.02 0.04 0.32 0.06
#> SP102499 6 0.4798 0.6855 0.30 0.00 0.08 0.00 0.00 0.62
#> SP102507 4 0.0937 0.6140 0.00 0.00 0.00 0.96 0.00 0.04
#> SP102511 6 0.4798 0.6855 0.30 0.00 0.08 0.00 0.00 0.62
#> SP102517 5 0.2793 0.6600 0.20 0.00 0.00 0.00 0.80 0.00
#> SP102523 5 0.5214 0.6248 0.18 0.00 0.02 0.04 0.70 0.06
#> SP102529 5 0.1267 0.8265 0.06 0.00 0.00 0.00 0.94 0.00
#> SP102537 6 0.5029 0.6958 0.26 0.00 0.12 0.00 0.00 0.62
#> SP102541 5 0.2725 0.8082 0.00 0.00 0.02 0.04 0.88 0.06
#> SP102547 1 0.6242 0.1948 0.56 0.00 0.02 0.04 0.28 0.10
#> SP102557 6 0.4798 0.6855 0.30 0.00 0.08 0.00 0.00 0.62
#> SP102561 1 0.0000 0.9277 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102567 5 0.1267 0.8265 0.06 0.00 0.00 0.00 0.94 0.00
#> SP102573 5 0.5214 0.6248 0.18 0.00 0.02 0.04 0.70 0.06
#> SP102581 1 0.0000 0.9277 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102591 1 0.0000 0.9277 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102597 6 0.4798 0.6855 0.30 0.00 0.08 0.00 0.00 0.62
#> SP102605 1 0.0000 0.9277 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102611 5 0.3198 0.6227 0.00 0.26 0.00 0.00 0.74 0.00
#> SP102617 1 0.0937 0.8876 0.96 0.00 0.00 0.00 0.00 0.04
#> SP102620 4 0.2048 0.6280 0.00 0.00 0.00 0.88 0.00 0.12
#> SP102622 5 0.1267 0.8265 0.06 0.00 0.00 0.00 0.94 0.00
#> SP102626 4 0.0937 0.6140 0.00 0.00 0.00 0.96 0.00 0.04
#> SP102630 4 0.2631 0.4262 0.00 0.00 0.18 0.82 0.00 0.00
#> SP102633 4 0.2474 0.6322 0.04 0.00 0.00 0.88 0.00 0.08
#> SP102647 4 0.0937 0.6140 0.00 0.00 0.00 0.96 0.00 0.04
#> SP102652 4 0.3942 0.5043 0.00 0.00 0.04 0.80 0.10 0.06
#> SP102690 1 0.3711 0.5573 0.72 0.00 0.00 0.02 0.00 0.26
#> SP102716 1 0.0937 0.8875 0.96 0.00 0.00 0.00 0.00 0.04
#> SP102718 1 0.0000 0.9277 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102733 1 0.0000 0.9277 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102741 1 0.2631 0.6932 0.82 0.00 0.00 0.00 0.00 0.18
#> SP102747 1 0.0000 0.9277 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102755 1 0.0000 0.9277 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102759 1 0.0000 0.9277 1.00 0.00 0.00 0.00 0.00 0.00
#> [ reached 'max' / getOption("max.print") -- omitted 2626 rows ]
cbind(get_classes(res, k = 7), get_membership(res, k = 7))
#> class entropy silhouette p1 p2 p3 p4 p5 p6 p7
#> SP1003 2 0.3199 0.7894 0.00 0.80 0.00 0.00 0.06 0.00 0.14
#> SP10084 2 0.2569 0.8027 0.00 0.84 0.00 0.00 0.02 0.00 0.14
#> SP1009 6 0.6264 0.2179 0.00 0.00 0.04 0.30 0.00 0.36 0.30
#> SP10150 2 0.4945 0.3786 0.00 0.52 0.00 0.00 0.36 0.00 0.12
#> SP101515 3 0.5086 0.5469 0.00 0.00 0.64 0.06 0.00 0.08 0.22
#> SP101519 7 0.5332 0.2424 0.00 0.38 0.00 0.00 0.18 0.00 0.44
#> SP101521 2 0.0504 0.8168 0.00 0.98 0.00 0.00 0.00 0.00 0.02
#> SP101523 3 0.2569 0.7303 0.02 0.00 0.84 0.00 0.00 0.14 0.00
#> SP101526 2 0.2376 0.8085 0.00 0.86 0.00 0.00 0.02 0.00 0.12
#> SP101528 4 0.4535 0.4653 0.00 0.00 0.24 0.64 0.00 0.00 0.12
#> SP101532 6 0.5840 0.3050 0.02 0.00 0.12 0.04 0.00 0.56 0.26
#> SP101536 5 0.4487 0.0556 0.00 0.42 0.00 0.00 0.52 0.00 0.06
#> SP101540 2 0.0863 0.8168 0.00 0.96 0.00 0.00 0.00 0.00 0.04
#> SP101544 2 0.2569 0.8027 0.00 0.84 0.00 0.00 0.02 0.00 0.14
#> SP101548 6 0.6413 0.2310 0.00 0.00 0.06 0.30 0.00 0.38 0.26
#> SP101552 2 0.0504 0.8136 0.00 0.98 0.00 0.00 0.00 0.00 0.02
#> SP101558 3 0.2569 0.7303 0.02 0.00 0.84 0.00 0.00 0.14 0.00
#> SP101564 2 0.0504 0.8157 0.00 0.98 0.00 0.00 0.02 0.00 0.00
#> SP101572 2 0.4478 0.6811 0.00 0.66 0.00 0.00 0.20 0.00 0.14
#> SP101576 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101580 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101584 2 0.2569 0.8027 0.00 0.84 0.00 0.00 0.02 0.00 0.14
#> SP101588 2 0.4264 0.4397 0.00 0.62 0.00 0.00 0.06 0.00 0.32
#> SP101592 2 0.1664 0.8151 0.00 0.92 0.00 0.00 0.02 0.00 0.06
#> SP101596 2 0.2376 0.8083 0.00 0.86 0.00 0.00 0.02 0.00 0.12
#> SP101600 3 0.2569 0.7303 0.02 0.00 0.84 0.00 0.00 0.14 0.00
#> SP101604 4 0.5681 0.2548 0.00 0.00 0.08 0.58 0.00 0.12 0.22
#> SP101610 3 0.6952 0.1398 0.00 0.00 0.34 0.26 0.00 0.24 0.16
#> SP101616 2 0.3562 -0.0912 0.00 0.50 0.50 0.00 0.00 0.00 0.00
#> SP101622 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101628 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101634 6 0.5604 0.1524 0.00 0.00 0.26 0.02 0.00 0.52 0.20
#> SP101642 2 0.1671 0.7714 0.00 0.90 0.10 0.00 0.00 0.00 0.00
#> SP101648 3 0.2569 0.7343 0.00 0.00 0.84 0.02 0.00 0.14 0.00
#> SP101654 2 0.0863 0.8085 0.00 0.96 0.04 0.00 0.00 0.00 0.00
#> SP101658 6 0.5363 0.3682 0.22 0.00 0.00 0.14 0.00 0.60 0.04
#> SP101662 4 0.5317 0.1405 0.00 0.00 0.02 0.56 0.00 0.28 0.14
#> SP101666 6 0.3388 0.1428 0.00 0.00 0.20 0.04 0.00 0.76 0.00
#> SP101670 2 0.4970 0.3235 0.00 0.58 0.00 0.00 0.18 0.00 0.24
#> SP101674 2 0.1664 0.8151 0.00 0.92 0.00 0.00 0.02 0.00 0.06
#> SP101678 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101682 2 0.2376 0.8067 0.00 0.86 0.00 0.00 0.02 0.00 0.12
#> SP101686 2 0.2376 0.7521 0.00 0.86 0.00 0.00 0.12 0.00 0.02
#> SP101690 2 0.0504 0.8146 0.00 0.98 0.02 0.00 0.00 0.00 0.00
#> SP101694 2 0.4015 0.6395 0.00 0.68 0.00 0.00 0.26 0.00 0.06
#> SP101700 6 0.5259 -0.0152 0.00 0.00 0.22 0.00 0.00 0.52 0.26
#> SP101708 3 0.2569 0.7343 0.00 0.00 0.84 0.02 0.00 0.14 0.00
#> SP101716 3 0.3546 0.1866 0.00 0.46 0.54 0.00 0.00 0.00 0.00
#> SP101724 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101732 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101740 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101795 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101845 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101881 3 0.2259 0.7354 0.00 0.00 0.84 0.00 0.00 0.16 0.00
#> SP101891 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP101921 7 0.3459 0.6177 0.00 0.00 0.00 0.00 0.40 0.00 0.60
#> SP101931 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102015 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102035 3 0.5452 0.2084 0.00 0.00 0.46 0.00 0.00 0.30 0.24
#> SP102045 2 0.4015 0.6319 0.00 0.68 0.00 0.00 0.26 0.00 0.06
#> SP102055 3 0.6215 0.2122 0.00 0.00 0.46 0.06 0.00 0.26 0.22
#> SP102064 6 0.3867 0.1982 0.00 0.00 0.02 0.38 0.00 0.60 0.00
#> SP102074 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102084 3 0.2569 0.7343 0.00 0.00 0.84 0.02 0.00 0.14 0.00
#> SP102090 2 0.3562 -0.0654 0.00 0.50 0.50 0.00 0.00 0.00 0.00
#> SP102096 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102103 2 0.1433 0.7939 0.00 0.92 0.00 0.00 0.08 0.00 0.00
#> SP102113 3 0.2569 0.7349 0.00 0.00 0.84 0.00 0.00 0.14 0.02
#> SP102123 2 0.0000 0.8157 0.00 1.00 0.00 0.00 0.00 0.00 0.00
#> SP102133 2 0.1886 0.7721 0.00 0.88 0.00 0.00 0.12 0.00 0.00
#> SP102143 6 0.5416 0.1632 0.00 0.00 0.20 0.00 0.00 0.42 0.38
#> SP102161 5 0.1166 0.7217 0.00 0.00 0.00 0.00 0.94 0.00 0.06
#> SP102168 3 0.3449 0.7064 0.00 0.14 0.78 0.00 0.00 0.08 0.00
#> SP102174 2 0.0504 0.8188 0.00 0.98 0.00 0.00 0.00 0.00 0.02
#> SP102187 3 0.2569 0.7303 0.02 0.00 0.84 0.00 0.00 0.14 0.00
#> SP102485 1 0.0000 0.9525 1.00 0.00 0.00 0.00 0.00 0.00 0.00
#> SP102489 7 0.4873 0.5191 0.18 0.00 0.00 0.00 0.22 0.00 0.60
#> SP102499 6 0.5055 0.3741 0.26 0.00 0.00 0.18 0.00 0.56 0.00
#> SP102507 6 0.6627 0.2253 0.02 0.00 0.04 0.32 0.00 0.36 0.26
#> SP102511 6 0.5417 0.3750 0.24 0.00 0.00 0.18 0.02 0.56 0.00
#> SP102517 5 0.1433 0.7379 0.08 0.00 0.00 0.00 0.92 0.00 0.00
#> SP102523 7 0.4127 0.6323 0.04 0.00 0.00 0.00 0.36 0.00 0.60
#> SP102529 5 0.1166 0.7678 0.06 0.00 0.00 0.00 0.94 0.00 0.00
#> SP102537 6 0.4789 0.3727 0.26 0.00 0.00 0.14 0.00 0.60 0.00
#> SP102541 7 0.3496 0.5994 0.00 0.00 0.00 0.00 0.42 0.00 0.58
#> SP102547 1 0.6020 0.1998 0.54 0.00 0.00 0.10 0.22 0.00 0.14
#> SP102557 6 0.5417 0.3750 0.24 0.00 0.00 0.18 0.02 0.56 0.00
#> SP102561 1 0.0000 0.9525 1.00 0.00 0.00 0.00 0.00 0.00 0.00
#> SP102567 5 0.1166 0.7678 0.06 0.00 0.00 0.00 0.94 0.00 0.00
#> SP102573 7 0.4127 0.6323 0.04 0.00 0.00 0.00 0.36 0.00 0.60
#> SP102581 1 0.0000 0.9525 1.00 0.00 0.00 0.00 0.00 0.00 0.00
#> SP102591 1 0.0000 0.9525 1.00 0.00 0.00 0.00 0.00 0.00 0.00
#> SP102597 6 0.5055 0.3741 0.26 0.00 0.00 0.18 0.00 0.56 0.00
#> SP102605 1 0.0000 0.9525 1.00 0.00 0.00 0.00 0.00 0.00 0.00
#> SP102611 5 0.3139 0.5043 0.00 0.30 0.00 0.00 0.70 0.00 0.00
#> SP102617 1 0.2376 0.8109 0.86 0.00 0.00 0.12 0.00 0.02 0.00
#> SP102620 4 0.5579 -0.1574 0.00 0.00 0.00 0.38 0.00 0.36 0.26
#> SP102622 5 0.1166 0.7678 0.06 0.00 0.00 0.00 0.94 0.00 0.00
#> SP102626 6 0.6237 0.2161 0.00 0.00 0.04 0.34 0.00 0.36 0.26
#> SP102630 6 0.6752 0.2480 0.00 0.00 0.20 0.14 0.00 0.40 0.26
#> [ reached 'max' / getOption("max.print") -- omitted 2637 rows ]
cbind(get_classes(res, k = 8), get_membership(res, k = 8))
#> class entropy silhouette p1 p2 p3 p4 p5 p6 p7 p8
#> SP1003 2 0.286 0.6377 0.00 0.84 0.00 0.06 0.08 0.02 0.00 0.00
#> SP10084 2 0.141 0.6563 0.00 0.94 0.00 0.02 0.02 0.02 0.00 0.00
#> SP1009 8 0.128 0.7354 0.00 0.00 0.00 0.02 0.00 0.00 0.04 0.94
#> SP10150 2 0.467 0.4517 0.00 0.60 0.00 0.00 0.28 0.02 0.10 0.00
#> SP101515 3 0.512 0.3216 0.00 0.00 0.56 0.04 0.00 0.06 0.02 0.32
#> SP101519 7 0.504 0.4916 0.00 0.12 0.02 0.06 0.06 0.04 0.70 0.00
#> SP101521 2 0.434 0.6867 0.00 0.70 0.00 0.16 0.00 0.04 0.10 0.00
#> SP101523 3 0.272 0.6416 0.02 0.00 0.80 0.00 0.00 0.18 0.00 0.00
#> SP101526 2 0.174 0.6765 0.00 0.92 0.00 0.04 0.02 0.02 0.00 0.00
#> SP101528 4 0.573 0.1823 0.00 0.00 0.22 0.42 0.00 0.04 0.00 0.32
#> SP101532 8 0.387 0.6160 0.00 0.00 0.06 0.02 0.00 0.20 0.00 0.72
#> SP101536 5 0.357 0.2787 0.00 0.36 0.00 0.00 0.62 0.02 0.00 0.00
#> SP101540 2 0.505 0.6890 0.00 0.66 0.02 0.14 0.00 0.06 0.12 0.00
#> SP101544 2 0.262 0.6486 0.00 0.86 0.00 0.06 0.02 0.06 0.00 0.00
#> SP101548 8 0.259 0.7157 0.00 0.00 0.04 0.02 0.00 0.08 0.00 0.86
#> SP101552 2 0.577 0.6695 0.00 0.60 0.02 0.14 0.02 0.06 0.16 0.00
#> SP101558 3 0.272 0.6416 0.02 0.00 0.80 0.00 0.00 0.18 0.00 0.00
#> SP101564 2 0.483 0.6948 0.00 0.72 0.02 0.10 0.04 0.04 0.08 0.00
#> SP101572 2 0.317 0.5809 0.00 0.78 0.00 0.02 0.18 0.02 0.00 0.00
#> SP101576 2 0.452 0.6937 0.00 0.74 0.02 0.10 0.02 0.04 0.08 0.00
#> SP101580 2 0.506 0.6876 0.00 0.68 0.02 0.14 0.02 0.04 0.10 0.00
#> SP101584 2 0.156 0.6780 0.00 0.92 0.00 0.06 0.02 0.00 0.00 0.00
#> SP101588 7 0.629 -0.0418 0.00 0.24 0.02 0.16 0.02 0.06 0.50 0.00
#> SP101592 2 0.305 0.6807 0.00 0.84 0.00 0.04 0.08 0.02 0.02 0.00
#> SP101596 2 0.128 0.6828 0.00 0.94 0.00 0.00 0.02 0.00 0.04 0.00
#> SP101600 3 0.272 0.6416 0.02 0.00 0.80 0.00 0.00 0.18 0.00 0.00
#> SP101604 8 0.550 0.2570 0.00 0.00 0.06 0.22 0.00 0.18 0.00 0.54
#> SP101610 8 0.654 0.1285 0.00 0.00 0.26 0.20 0.00 0.20 0.00 0.34
#> SP101616 3 0.659 -0.2462 0.00 0.30 0.38 0.18 0.00 0.02 0.12 0.00
#> SP101622 2 0.549 0.6749 0.00 0.64 0.02 0.14 0.02 0.06 0.12 0.00
#> SP101628 2 0.549 0.6749 0.00 0.64 0.02 0.14 0.02 0.06 0.12 0.00
#> SP101634 8 0.490 0.3674 0.00 0.00 0.28 0.00 0.00 0.20 0.00 0.52
#> SP101642 2 0.683 0.6195 0.00 0.50 0.10 0.18 0.02 0.08 0.12 0.00
#> SP101648 3 0.286 0.6627 0.00 0.00 0.82 0.02 0.00 0.14 0.00 0.02
#> SP101654 2 0.591 0.6668 0.00 0.60 0.04 0.16 0.02 0.06 0.12 0.00
#> SP101658 6 0.428 0.6578 0.18 0.00 0.00 0.00 0.00 0.70 0.08 0.04
#> SP101662 6 0.623 -0.0516 0.00 0.00 0.10 0.22 0.00 0.36 0.00 0.32
#> SP101666 6 0.461 0.2746 0.00 0.00 0.16 0.00 0.00 0.58 0.00 0.26
#> SP101670 7 0.677 -0.0816 0.00 0.24 0.02 0.16 0.06 0.06 0.46 0.00
#> SP101674 2 0.422 0.6934 0.00 0.76 0.00 0.08 0.06 0.04 0.06 0.00
#> SP101678 2 0.562 0.6697 0.00 0.62 0.02 0.16 0.02 0.06 0.12 0.00
#> SP101682 2 0.128 0.6683 0.00 0.94 0.00 0.00 0.04 0.02 0.00 0.00
#> SP101686 2 0.671 0.5733 0.00 0.48 0.02 0.16 0.06 0.06 0.22 0.00
#> SP101690 2 0.531 0.6812 0.00 0.66 0.02 0.14 0.02 0.06 0.10 0.00
#> SP101694 2 0.294 0.5139 0.00 0.70 0.00 0.00 0.30 0.00 0.00 0.00
#> SP101700 6 0.527 0.2858 0.00 0.00 0.18 0.00 0.00 0.56 0.22 0.04
#> SP101708 3 0.286 0.6627 0.00 0.00 0.82 0.02 0.00 0.14 0.00 0.02
#> SP101716 3 0.660 -0.0514 0.00 0.24 0.44 0.18 0.00 0.04 0.10 0.00
#> SP101724 2 0.562 0.6697 0.00 0.62 0.02 0.16 0.02 0.06 0.12 0.00
#> SP101732 2 0.562 0.6697 0.00 0.62 0.02 0.16 0.02 0.06 0.12 0.00
#> SP101740 2 0.562 0.6697 0.00 0.62 0.02 0.16 0.02 0.06 0.12 0.00
#> SP101795 2 0.562 0.6697 0.00 0.62 0.02 0.16 0.02 0.06 0.12 0.00
#> SP101845 2 0.562 0.6697 0.00 0.62 0.02 0.16 0.02 0.06 0.12 0.00
#> SP101881 3 0.273 0.6642 0.00 0.00 0.82 0.00 0.00 0.14 0.00 0.04
#> SP101891 2 0.562 0.6697 0.00 0.62 0.02 0.16 0.02 0.06 0.12 0.00
#> SP101921 7 0.195 0.7073 0.00 0.00 0.00 0.00 0.14 0.00 0.86 0.00
#> SP101931 2 0.332 0.7033 0.00 0.82 0.00 0.08 0.02 0.02 0.06 0.00
#> SP102015 2 0.562 0.6697 0.00 0.62 0.02 0.16 0.02 0.06 0.12 0.00
#> SP102035 8 0.451 0.2145 0.00 0.00 0.38 0.00 0.00 0.10 0.00 0.52
#> SP102045 2 0.351 0.4557 0.00 0.64 0.00 0.00 0.34 0.02 0.00 0.00
#> SP102055 8 0.507 0.0357 0.00 0.00 0.44 0.04 0.00 0.08 0.00 0.44
#> SP102064 6 0.476 0.4204 0.00 0.00 0.08 0.22 0.00 0.64 0.00 0.06
#> SP102074 2 0.562 0.6697 0.00 0.62 0.02 0.16 0.02 0.06 0.12 0.00
#> SP102084 3 0.272 0.6503 0.00 0.00 0.80 0.02 0.00 0.18 0.00 0.00
#> SP102090 3 0.735 -0.1413 0.00 0.28 0.36 0.16 0.02 0.10 0.08 0.00
#> SP102096 2 0.490 0.6880 0.00 0.70 0.02 0.12 0.02 0.04 0.10 0.00
#> SP102103 2 0.630 0.6501 0.00 0.56 0.02 0.16 0.06 0.06 0.14 0.00
#> SP102113 3 0.286 0.6636 0.00 0.00 0.82 0.00 0.00 0.14 0.02 0.02
#> SP102123 2 0.577 0.6688 0.00 0.60 0.02 0.16 0.02 0.06 0.14 0.00
#> SP102133 2 0.681 0.6178 0.00 0.50 0.02 0.16 0.14 0.06 0.12 0.00
#> SP102143 8 0.536 0.4850 0.00 0.00 0.14 0.00 0.00 0.20 0.08 0.58
#> SP102161 5 0.156 0.7327 0.00 0.00 0.00 0.00 0.90 0.00 0.10 0.00
#> SP102168 3 0.395 0.6366 0.00 0.08 0.76 0.02 0.00 0.12 0.02 0.00
#> SP102174 2 0.452 0.6998 0.00 0.74 0.02 0.10 0.02 0.04 0.08 0.00
#> SP102187 3 0.272 0.6416 0.02 0.00 0.80 0.00 0.00 0.18 0.00 0.00
#> SP102485 1 0.000 0.9360 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
#> SP102489 7 0.241 0.6812 0.08 0.00 0.00 0.00 0.06 0.00 0.86 0.00
#> SP102499 6 0.470 0.6828 0.26 0.00 0.00 0.08 0.00 0.62 0.00 0.04
#> SP102507 8 0.134 0.7210 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.92
#> SP102511 6 0.524 0.6899 0.22 0.00 0.00 0.08 0.04 0.62 0.00 0.04
#> SP102517 5 0.156 0.7710 0.06 0.00 0.00 0.00 0.92 0.00 0.02 0.00
#> SP102523 7 0.195 0.7159 0.00 0.00 0.00 0.00 0.14 0.00 0.86 0.00
#> SP102529 5 0.156 0.7710 0.06 0.00 0.00 0.00 0.92 0.00 0.02 0.00
#> SP102537 6 0.376 0.6903 0.26 0.00 0.00 0.06 0.00 0.68 0.00 0.00
#> SP102541 7 0.211 0.7054 0.00 0.00 0.00 0.00 0.16 0.00 0.84 0.00
#> SP102547 1 0.575 -0.1648 0.42 0.00 0.00 0.06 0.06 0.04 0.42 0.00
#> SP102557 6 0.524 0.6899 0.22 0.00 0.00 0.08 0.04 0.62 0.00 0.04
#> SP102561 1 0.000 0.9360 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
#> SP102567 5 0.156 0.7710 0.06 0.00 0.00 0.00 0.92 0.00 0.02 0.00
#> SP102573 7 0.195 0.7159 0.00 0.00 0.00 0.00 0.14 0.00 0.86 0.00
#> [ reached 'max' / getOption("max.print") -- omitted 2647 rows ]
cbind(get_classes(res, k = 9), get_membership(res, k = 9))
#> class entropy silhouette p1 p2 p3 p4 p5 p6 p7 p8 p9
#> SP1003 2 0.1473 0.6274 0.00 0.92 0.00 0.00 0.02 0.00 0.00 0.00 0.06
#> SP10084 2 0.0446 0.6327 0.00 0.98 0.00 0.00 0.00 0.00 0.00 0.00 0.02
#> SP1009 8 0.1786 0.7280 0.00 0.00 0.00 0.06 0.00 0.00 0.04 0.90 0.00
#> SP10150 2 0.3758 0.5095 0.00 0.68 0.00 0.00 0.22 0.00 0.10 0.00 0.00
#> SP101515 3 0.5397 0.3803 0.00 0.00 0.58 0.06 0.00 0.08 0.02 0.24 0.02
#> SP101519 7 0.4758 0.4469 0.00 0.06 0.00 0.00 0.06 0.00 0.52 0.00 0.36
#> SP101521 9 0.3063 0.4113 0.00 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.60
#> SP101523 3 0.2431 0.6011 0.02 0.00 0.82 0.00 0.00 0.16 0.00 0.00 0.00
#> SP101526 2 0.1916 0.5898 0.00 0.88 0.00 0.00 0.02 0.00 0.00 0.00 0.10
#> SP101528 8 0.6764 -0.0534 0.00 0.00 0.14 0.34 0.00 0.10 0.06 0.34 0.02
#> SP101532 8 0.3637 0.5788 0.00 0.00 0.02 0.04 0.00 0.24 0.00 0.70 0.00
#> SP101536 5 0.4110 0.3805 0.00 0.18 0.00 0.00 0.64 0.00 0.00 0.00 0.18
#> SP101540 2 0.3140 -0.1669 0.00 0.54 0.00 0.00 0.00 0.00 0.00 0.00 0.46
#> SP101544 2 0.1670 0.5989 0.00 0.88 0.00 0.00 0.00 0.00 0.00 0.00 0.12
#> SP101548 8 0.4619 0.6349 0.00 0.00 0.02 0.10 0.00 0.14 0.06 0.68 0.00
#> SP101552 9 0.3424 0.4897 0.00 0.38 0.00 0.00 0.02 0.00 0.00 0.00 0.60
#> SP101558 3 0.2275 0.6086 0.02 0.00 0.84 0.00 0.00 0.14 0.00 0.00 0.00
#> SP101564 2 0.3997 -0.2230 0.00 0.48 0.00 0.00 0.06 0.00 0.00 0.00 0.46
#> SP101572 2 0.1843 0.6131 0.00 0.86 0.00 0.00 0.14 0.00 0.00 0.00 0.00
#> SP101576 2 0.3990 -0.1727 0.00 0.50 0.00 0.00 0.06 0.00 0.00 0.00 0.44
#> SP101580 9 0.3778 0.3858 0.00 0.44 0.00 0.00 0.04 0.00 0.00 0.00 0.52
#> SP101584 2 0.2001 0.5823 0.00 0.84 0.00 0.00 0.00 0.00 0.00 0.00 0.16
#> SP101588 9 0.2703 0.5220 0.00 0.02 0.00 0.00 0.00 0.00 0.20 0.00 0.78
#> SP101592 2 0.3122 0.4944 0.00 0.76 0.00 0.00 0.06 0.00 0.00 0.00 0.18
#> SP101596 2 0.2224 0.5888 0.00 0.86 0.00 0.00 0.04 0.00 0.00 0.00 0.10
#> SP101600 3 0.2275 0.6086 0.02 0.00 0.84 0.00 0.00 0.14 0.00 0.00 0.00
#> SP101604 8 0.6727 0.2359 0.00 0.00 0.14 0.22 0.00 0.22 0.06 0.36 0.00
#> SP101610 8 0.6833 0.2086 0.00 0.00 0.20 0.20 0.00 0.20 0.06 0.34 0.00
#> SP101616 9 0.3572 0.5514 0.00 0.08 0.22 0.00 0.00 0.00 0.00 0.00 0.70
#> SP101622 9 0.3478 0.5909 0.00 0.30 0.00 0.00 0.04 0.00 0.00 0.00 0.66
#> SP101628 9 0.3604 0.5521 0.00 0.34 0.00 0.00 0.04 0.00 0.00 0.00 0.62
#> SP101634 8 0.4858 0.0656 0.00 0.00 0.38 0.00 0.00 0.22 0.00 0.40 0.00
#> SP101642 9 0.3408 0.6463 0.00 0.16 0.06 0.00 0.02 0.00 0.00 0.00 0.76
#> SP101648 3 0.2537 0.6221 0.00 0.00 0.84 0.02 0.00 0.12 0.00 0.02 0.00
#> SP101654 9 0.3637 0.6301 0.00 0.24 0.02 0.00 0.04 0.00 0.00 0.00 0.70
#> SP101658 6 0.4105 0.5686 0.12 0.00 0.00 0.02 0.00 0.72 0.12 0.02 0.00
#> SP101662 8 0.6791 0.1861 0.00 0.00 0.14 0.22 0.00 0.26 0.06 0.32 0.00
#> SP101666 6 0.4635 0.1610 0.00 0.00 0.20 0.00 0.00 0.52 0.00 0.28 0.00
#> SP101670 9 0.3347 0.4584 0.00 0.02 0.00 0.00 0.02 0.00 0.24 0.00 0.72
#> SP101674 2 0.3919 0.0190 0.00 0.56 0.00 0.00 0.06 0.00 0.00 0.00 0.38
#> SP101678 9 0.3402 0.6073 0.00 0.28 0.00 0.00 0.04 0.00 0.00 0.00 0.68
#> SP101682 2 0.1473 0.6155 0.00 0.92 0.00 0.00 0.02 0.00 0.00 0.00 0.06
#> SP101686 9 0.3014 0.6037 0.00 0.04 0.00 0.00 0.08 0.00 0.06 0.00 0.82
#> SP101690 9 0.3789 0.3468 0.00 0.46 0.00 0.00 0.04 0.00 0.00 0.00 0.50
#> SP101694 2 0.3572 0.5221 0.00 0.70 0.00 0.00 0.22 0.00 0.00 0.00 0.08
#> SP101700 6 0.5772 0.1910 0.00 0.00 0.20 0.00 0.00 0.52 0.18 0.04 0.06
#> SP101708 3 0.2537 0.6221 0.00 0.00 0.84 0.02 0.00 0.12 0.00 0.02 0.00
#> SP101716 9 0.3712 0.4905 0.00 0.06 0.30 0.00 0.00 0.00 0.00 0.00 0.64
#> SP101724 9 0.3402 0.6073 0.00 0.28 0.00 0.00 0.04 0.00 0.00 0.00 0.68
#> SP101732 9 0.3316 0.6214 0.00 0.26 0.00 0.00 0.04 0.00 0.00 0.00 0.70
#> SP101740 9 0.2821 0.6412 0.00 0.22 0.00 0.00 0.02 0.00 0.00 0.00 0.76
#> SP101795 9 0.3221 0.6314 0.00 0.24 0.00 0.00 0.04 0.00 0.00 0.00 0.72
#> SP101845 9 0.3116 0.6390 0.00 0.22 0.00 0.00 0.04 0.00 0.00 0.00 0.74
#> SP101881 3 0.2537 0.6221 0.00 0.00 0.84 0.02 0.00 0.12 0.00 0.02 0.00
#> SP101891 9 0.3221 0.6314 0.00 0.24 0.00 0.00 0.04 0.00 0.00 0.00 0.72
#> SP101921 7 0.2890 0.7818 0.00 0.00 0.00 0.00 0.08 0.00 0.80 0.00 0.12
#> SP101931 2 0.3193 0.2610 0.00 0.68 0.00 0.00 0.02 0.00 0.00 0.00 0.30
#> SP102015 9 0.3316 0.6214 0.00 0.26 0.00 0.00 0.04 0.00 0.00 0.00 0.70
#> SP102035 8 0.4886 0.1667 0.00 0.00 0.36 0.02 0.00 0.14 0.00 0.48 0.00
#> SP102045 2 0.3478 0.4840 0.00 0.66 0.00 0.00 0.30 0.00 0.00 0.00 0.04
#> SP102055 3 0.5023 0.2365 0.00 0.00 0.52 0.06 0.00 0.10 0.00 0.32 0.00
#> SP102064 6 0.5620 0.1325 0.00 0.00 0.24 0.22 0.00 0.46 0.00 0.08 0.00
#> SP102074 9 0.3221 0.6314 0.00 0.24 0.00 0.00 0.04 0.00 0.00 0.00 0.72
#> SP102084 3 0.2001 0.6152 0.00 0.00 0.84 0.00 0.00 0.16 0.00 0.00 0.00
#> SP102090 9 0.5156 0.4469 0.00 0.10 0.18 0.00 0.00 0.06 0.00 0.04 0.62
#> SP102096 9 0.3997 0.2885 0.00 0.46 0.00 0.00 0.06 0.00 0.00 0.00 0.48
#> SP102103 9 0.3985 0.6335 0.00 0.22 0.00 0.00 0.08 0.00 0.02 0.00 0.68
#> SP102113 3 0.2537 0.6221 0.00 0.00 0.84 0.02 0.00 0.12 0.00 0.02 0.00
#> SP102123 9 0.2431 0.6499 0.00 0.16 0.00 0.00 0.02 0.00 0.00 0.00 0.82
#> SP102133 9 0.4099 0.5986 0.00 0.20 0.00 0.00 0.16 0.00 0.00 0.00 0.64
#> SP102143 8 0.5060 0.5183 0.00 0.00 0.14 0.00 0.00 0.16 0.06 0.62 0.02
#> SP102161 5 0.1670 0.7387 0.00 0.00 0.00 0.00 0.88 0.00 0.12 0.00 0.00
#> SP102168 3 0.3221 0.5201 0.00 0.00 0.72 0.00 0.00 0.04 0.00 0.00 0.24
#> SP102174 2 0.3545 0.1931 0.00 0.64 0.00 0.00 0.04 0.00 0.00 0.00 0.32
#> SP102187 3 0.2275 0.6086 0.02 0.00 0.84 0.00 0.00 0.14 0.00 0.00 0.00
#> SP102485 1 0.0000 0.9107 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
#> SP102489 7 0.1707 0.7421 0.08 0.00 0.00 0.02 0.00 0.00 0.90 0.00 0.00
#> SP102499 6 0.4477 0.6230 0.26 0.00 0.00 0.18 0.00 0.56 0.00 0.00 0.00
#> SP102507 8 0.2929 0.6612 0.00 0.00 0.00 0.24 0.00 0.02 0.00 0.74 0.00
#> SP102511 6 0.4985 0.6321 0.22 0.00 0.00 0.18 0.04 0.56 0.00 0.00 0.00
#> SP102517 5 0.1033 0.7890 0.06 0.00 0.00 0.00 0.94 0.00 0.00 0.00 0.00
#> SP102523 7 0.1269 0.7869 0.00 0.00 0.00 0.00 0.08 0.00 0.92 0.00 0.00
#> SP102529 5 0.1033 0.7890 0.06 0.00 0.00 0.00 0.94 0.00 0.00 0.00 0.00
#> [ reached 'max' / getOption("max.print") -- omitted 2654 rows ]
cbind(get_classes(res, k = 10), get_membership(res, k = 10))
#> class entropy silhouette p1 p2 p3 p4 p5 p6 p7 p8 p9 p10
#> SP1003 2 0.2121 0.63721 0.00 0.88 0.00 0.00 0.02 0.00 0.00 0.00 0.06 0.04
#> SP10084 2 0.0729 0.65688 0.00 0.96 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00
#> SP1009 8 0.0426 0.64740 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.98 0.00 0.00
#> SP10150 2 0.5540 0.45244 0.00 0.54 0.00 0.00 0.18 0.00 0.10 0.00 0.14 0.04
#> SP101515 3 0.4791 0.29702 0.00 0.00 0.50 0.28 0.00 0.00 0.02 0.20 0.00 0.00
#> SP101519 7 0.4814 0.53397 0.00 0.04 0.00 0.00 0.04 0.00 0.60 0.00 0.24 0.08
#> SP101521 9 0.3319 0.44752 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.66 0.04
#> SP101523 3 0.0986 0.69031 0.00 0.00 0.94 0.00 0.00 0.06 0.00 0.00 0.00 0.00
#> SP101526 2 0.2047 0.59509 0.00 0.82 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.00
#> SP101528 8 0.5620 0.31684 0.00 0.00 0.10 0.34 0.00 0.00 0.02 0.42 0.00 0.12
#> SP101532 8 0.6574 0.31277 0.00 0.02 0.10 0.12 0.02 0.24 0.00 0.44 0.00 0.06
#> SP101536 5 0.4702 0.22964 0.00 0.16 0.00 0.00 0.50 0.00 0.00 0.00 0.32 0.02
#> SP101540 2 0.3958 0.15896 0.00 0.52 0.00 0.00 0.02 0.00 0.00 0.00 0.42 0.04
#> SP101544 2 0.1759 0.62384 0.00 0.86 0.00 0.00 0.00 0.00 0.00 0.00 0.14 0.00
#> SP101548 8 0.4511 0.50185 0.00 0.00 0.06 0.28 0.00 0.04 0.02 0.60 0.00 0.00
#> SP101552 9 0.3638 0.40610 0.00 0.28 0.00 0.00 0.02 0.00 0.00 0.00 0.66 0.04
#> SP101558 3 0.1152 0.69336 0.02 0.00 0.94 0.00 0.00 0.04 0.00 0.00 0.00 0.00
#> SP101564 9 0.2972 0.43752 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.70 0.02
#> SP101572 2 0.2535 0.64271 0.00 0.84 0.00 0.00 0.10 0.00 0.00 0.00 0.02 0.04
#> SP101576 9 0.3114 0.36614 0.00 0.32 0.00 0.00 0.00 0.00 0.00 0.00 0.66 0.02
#> SP101580 9 0.2575 0.48690 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.72 0.00
#> SP101584 2 0.3155 0.58994 0.00 0.78 0.00 0.00 0.00 0.00 0.04 0.00 0.14 0.04
#> SP101588 9 0.4314 0.07887 0.00 0.02 0.00 0.00 0.00 0.00 0.40 0.00 0.50 0.08
#> SP101592 2 0.3488 0.37063 0.00 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.36 0.04
#> SP101596 2 0.3265 0.56534 0.00 0.74 0.00 0.00 0.02 0.00 0.00 0.00 0.20 0.04
#> SP101600 3 0.1152 0.69336 0.02 0.00 0.94 0.00 0.00 0.04 0.00 0.00 0.00 0.00
#> SP101604 8 0.5361 0.33749 0.00 0.00 0.16 0.38 0.00 0.04 0.02 0.40 0.00 0.00
#> SP101610 4 0.6744 -0.28403 0.00 0.02 0.26 0.36 0.02 0.04 0.02 0.24 0.00 0.04
#> SP101616 9 0.3165 0.53338 0.00 0.00 0.26 0.00 0.00 0.00 0.00 0.00 0.70 0.04
#> SP101622 9 0.2579 0.54974 0.00 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.78 0.02
#> SP101628 9 0.2692 0.53393 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.76 0.02
#> SP101634 8 0.6880 -0.03969 0.00 0.02 0.32 0.12 0.02 0.14 0.00 0.32 0.00 0.06
#> SP101642 9 0.2340 0.62999 0.00 0.02 0.08 0.00 0.00 0.00 0.00 0.00 0.86 0.04
#> SP101648 3 0.0729 0.69462 0.00 0.00 0.96 0.00 0.00 0.00 0.00 0.04 0.00 0.00
#> SP101654 9 0.2122 0.62869 0.00 0.10 0.04 0.00 0.00 0.00 0.00 0.00 0.86 0.00
#> SP101658 6 0.4980 0.52530 0.06 0.00 0.08 0.12 0.00 0.66 0.06 0.00 0.00 0.02
#> SP101662 8 0.5429 0.32614 0.00 0.00 0.18 0.36 0.00 0.04 0.02 0.40 0.00 0.00
#> SP101666 8 0.6927 -0.02398 0.00 0.02 0.28 0.12 0.02 0.24 0.00 0.28 0.00 0.04
#> SP101670 9 0.4679 0.04941 0.00 0.02 0.00 0.00 0.02 0.00 0.40 0.00 0.48 0.08
#> SP101674 9 0.3973 0.00642 0.00 0.44 0.00 0.00 0.02 0.00 0.00 0.00 0.50 0.04
#> SP101678 9 0.1594 0.61978 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.88 0.00
#> SP101682 2 0.1412 0.64486 0.00 0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00
#> SP101686 9 0.2870 0.57867 0.00 0.04 0.00 0.00 0.02 0.00 0.14 0.00 0.80 0.00
#> SP101690 9 0.3224 0.29819 0.00 0.36 0.00 0.00 0.02 0.00 0.00 0.00 0.62 0.00
#> SP101694 2 0.4748 0.38952 0.00 0.54 0.00 0.00 0.14 0.00 0.00 0.00 0.28 0.04
#> SP101700 6 0.7661 -0.03569 0.00 0.02 0.26 0.12 0.02 0.26 0.20 0.08 0.00 0.04
#> SP101708 3 0.0729 0.69462 0.00 0.00 0.96 0.00 0.00 0.00 0.00 0.04 0.00 0.00
#> SP101716 9 0.3559 0.51774 0.00 0.00 0.26 0.00 0.00 0.00 0.02 0.00 0.68 0.04
#> SP101724 9 0.1594 0.61978 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.88 0.00
#> SP101732 9 0.1594 0.61978 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.88 0.00
#> SP101740 9 0.1211 0.63484 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.92 0.00
#> SP101795 9 0.1211 0.63484 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.92 0.00
#> SP101845 9 0.1412 0.62986 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.90 0.00
#> SP101881 3 0.0729 0.69462 0.00 0.00 0.96 0.00 0.00 0.00 0.00 0.04 0.00 0.00
#> SP101891 9 0.1412 0.62730 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.90 0.00
#> SP101921 7 0.3867 0.66549 0.00 0.02 0.00 0.00 0.10 0.00 0.74 0.00 0.10 0.04
#> SP101931 2 0.2954 0.18467 0.00 0.58 0.00 0.00 0.00 0.00 0.00 0.00 0.42 0.00
#> SP102015 9 0.1412 0.62986 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.90 0.00
#> SP102035 8 0.6437 -0.06147 0.00 0.02 0.36 0.12 0.02 0.04 0.02 0.38 0.00 0.04
#> SP102045 2 0.4921 0.40576 0.00 0.52 0.00 0.00 0.24 0.00 0.00 0.00 0.20 0.04
#> SP102055 3 0.4820 0.27557 0.00 0.00 0.50 0.24 0.00 0.02 0.00 0.24 0.00 0.00
#> SP102064 3 0.5240 0.31088 0.00 0.00 0.46 0.32 0.00 0.12 0.00 0.10 0.00 0.00
#> SP102074 9 0.1412 0.62986 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.90 0.00
#> SP102084 3 0.0850 0.69670 0.00 0.00 0.96 0.02 0.00 0.02 0.00 0.00 0.00 0.00
#> SP102090 9 0.4297 0.44010 0.00 0.02 0.12 0.00 0.00 0.00 0.00 0.00 0.60 0.26
#> SP102096 9 0.3649 0.21965 0.00 0.38 0.00 0.00 0.02 0.00 0.00 0.00 0.58 0.02
#> SP102103 9 0.2586 0.62732 0.00 0.08 0.00 0.00 0.06 0.00 0.02 0.00 0.84 0.00
#> SP102113 3 0.0729 0.69462 0.00 0.00 0.96 0.00 0.00 0.00 0.00 0.04 0.00 0.00
#> SP102123 9 0.1955 0.62041 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.88 0.06
#> SP102133 9 0.2947 0.59075 0.00 0.10 0.00 0.00 0.12 0.00 0.00 0.00 0.78 0.00
#> SP102143 8 0.6627 0.35145 0.00 0.02 0.14 0.12 0.02 0.06 0.08 0.52 0.00 0.04
#> SP102161 5 0.0986 0.76860 0.00 0.00 0.00 0.00 0.94 0.00 0.06 0.00 0.00 0.00
#> SP102168 3 0.2489 0.50045 0.00 0.00 0.74 0.00 0.00 0.00 0.00 0.00 0.26 0.00
#> SP102174 9 0.2996 0.07534 0.00 0.46 0.00 0.00 0.00 0.00 0.00 0.00 0.54 0.00
#> SP102187 3 0.1152 0.69336 0.02 0.00 0.94 0.00 0.00 0.04 0.00 0.00 0.00 0.00
#> SP102485 1 0.0000 0.88326 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
#> [ reached 'max' / getOption("max.print") -- omitted 2661 rows ]
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)
consensus_heatmap(res, k = 7)
consensus_heatmap(res, k = 8)
consensus_heatmap(res, k = 9)
consensus_heatmap(res, k = 10)
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)
membership_heatmap(res, k = 7)
membership_heatmap(res, k = 8)
membership_heatmap(res, k = 9)
membership_heatmap(res, k = 10)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. 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)
get_signatures(res, k = 7)
get_signatures(res, k = 8)
get_signatures(res, k = 9)
get_signatures(res, k = 10)
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)
get_signatures(res, k = 7, scale_rows = FALSE)
get_signatures(res, k = 8, scale_rows = FALSE)
get_signatures(res, k = 9, scale_rows = FALSE)
get_signatures(res, k = 10, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
#> Error in fit_diagram(combinations, "euler", input, shape, control, ...): !any(duplicated(names(combinations))) is not TRUE
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 (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
t-SNE plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "t-SNE")
#> Error in Rtsne.default(X = structure(c(-3.3804321739366, -4.96756462224444, : Remove duplicates before running TSNE.
dimension_reduction(res, k = 3, method = "t-SNE")
#> Error in Rtsne.default(X = structure(c(-3.3804321739366, -4.96756462224444, : Remove duplicates before running TSNE.
dimension_reduction(res, k = 4, method = "t-SNE")
#> Error in Rtsne.default(X = structure(c(-3.3804321739366, -4.96756462224444, : Remove duplicates before running TSNE.
dimension_reduction(res, k = 5, method = "t-SNE")
#> Error in Rtsne.default(X = structure(c(-3.3804321739366, -4.96756462224444, : Remove duplicates before running TSNE.
dimension_reduction(res, k = 6, method = "t-SNE")
#> Error in Rtsne.default(X = structure(c(-3.3804321739366, -4.96756462224444, : Remove duplicates before running TSNE.
dimension_reduction(res, k = 7, method = "t-SNE")
#> Error in Rtsne.default(X = structure(c(-3.3804321739366, -4.96756462224444, : Remove duplicates before running TSNE.
dimension_reduction(res, k = 8, method = "t-SNE")
#> Error in Rtsne.default(X = structure(c(-3.3804321739366, -4.96756462224444, : Remove duplicates before running TSNE.
dimension_reduction(res, k = 9, method = "t-SNE")
#> Error in Rtsne.default(X = structure(c(-3.3804321739366, -4.96756462224444, : Remove duplicates before running TSNE.
dimension_reduction(res, k = 10, method = "t-SNE")
#> Error in Rtsne.default(X = structure(c(-3.3804321739366, -4.96756462224444, : Remove duplicates before running TSNE.
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 4.0.2 (2020-06-22)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS High Sierra 10.13.6
#>
#> Matrix products: default
#> BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] zh_CN.UTF-8/zh_CN.UTF-8/zh_CN.UTF-8/C/zh_CN.UTF-8/zh_CN.UTF-8
#>
#> attached base packages:
#> [1] grid parallel stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.70.0 ComplexHeatmap_2.7.2.1000 markdown_1.1
#> [4] knitr_1.30 forcats_0.5.0 stringr_1.4.0
#> [7] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
#> [10] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3
#> [13] tidyverse_1.3.0 cola_1.9.0.1013 synchronicity_1.3.5
#> [16] bigmemory_4.5.36 Biobase_2.48.0 BiocGenerics_0.34.0
#> [19] roxytest_0.0.1 pacman_0.5.1
#>
#> loaded via a namespace (and not attached):
#> [1] readxl_1.3.1 uuid_0.1-4 backports_1.2.1 circlize_0.4.11
#> [5] NMF_0.23.0 plyr_1.8.6 polylabelr_0.2.0 splines_4.0.2
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#> [13] foreach_1.5.1 htmltools_0.5.0 magick_2.5.2 fansi_0.4.1
#> [17] magrittr_2.0.1 memoise_1.1.0 cluster_2.1.0 doParallel_1.0.16
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#> [29] colorspace_2.0-0 blob_1.2.1 rvest_0.3.6 ggrepel_0.9.0
#> [33] haven_2.3.1 xfun_0.19 RCurl_1.98-1.2 jsonlite_1.7.2
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#> [41] roxygen2_7.1.1 impute_1.62.0 survival_3.2-7 brew_1.0-6
#> [45] iterators_1.0.13 glue_1.4.2 polyclip_1.10-0 registry_0.5-1
#> [49] gtable_0.3.0 GetoptLong_1.0.5 car_3.0-10 pkgbuild_1.2.0
#> [53] shape_1.4.5 abind_1.4-5 scales_1.1.1 DBI_1.1.0
#> [57] rngtools_1.5 rstatix_0.6.0 Rcpp_1.0.5 xtable_1.8-4
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#> [69] RColorBrewer_1.1-2 ellipsis_0.3.1 factoextra_1.0.7 XML_3.99-0.5
#> [73] pkgconfig_2.0.3 dbplyr_2.0.0 AnnotationDbi_1.50.3 tidyselect_1.1.0
#> [77] rlang_0.4.10 reshape2_1.4.4 munsell_0.5.0 cellranger_1.1.0
#> [81] tools_4.0.2 cli_2.2.0 RSQLite_2.2.1 generics_0.1.0
#> [85] devtools_2.3.2 broom_0.7.3 evaluate_0.14 yaml_2.2.1
#> [89] bit64_4.0.5 processx_3.4.5 fs_1.5.0 zip_2.1.1
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#> [97] rstudioapi_0.13 curl_4.3 png_0.1-7 testthat_3.0.1
#> [101] ggsignif_0.6.0 reprex_0.3.0 stringi_1.5.3 highr_0.8
#> [105] ps_1.5.0 desc_1.2.0 lattice_0.20-41 Matrix_1.3-2
#> [109] vctrs_0.3.6 pillar_1.4.7 lifecycle_0.2.0 furrr_0.2.1
#> [113] BiocManager_1.30.10 eulerr_6.1.0 GlobalOptions_0.1.2 bitops_1.0-6
#> [117] data.table_1.13.6 R6_2.5.0 rio_0.5.16 IRanges_2.22.2
#> [121] parallelly_1.23.0 sessioninfo_1.1.1 codetools_0.2-18 assertthat_0.2.1
#> [125] pkgload_1.1.0 pkgmaker_0.32.2 rprojroot_2.0.2 rjson_0.2.20
#> [129] withr_2.3.0 S4Vectors_0.26.1 hms_0.5.3 rmarkdown_2.6
#> [133] rvcheck_0.1.8 carData_3.0-4 sigminer_1.2.0.99 Rtsne_0.15
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