Supplementary file 6: Analysis of the TCGA GBM microarray dataset
Zuguang Gu (z.gu@dkfz.de)
2021-09-03
We read the matrix and perform normalization.
library(cola)
m = read.table("https://jokergoo.github.io/cola_examples/TCGA_GBM/unifiedScaled.txt",
header = TRUE, row.names = 1, check.names = FALSE)
m = as.matrix(m)
subtype = read.table("https://jokergoo.github.io/cola_examples/TCGA_GBM/TCGA_unified_CORE_ClaNC840.txt",
sep = "\t", header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
subtype = structure(unlist(subtype[1, -(1:2)]), names = colnames(subtype)[-(1:2)])
subtype_col = structure(seq_len(4), names = unique(subtype))
m = m[, names(subtype)]
m = adjust_matrix(m)
library(preprocessCore)
cn = colnames(m)
rn = rownames(m)
m = normalize.quantiles(m)
colnames(m) = cn
rownames(m) = rnFirst we apply standard consensus partitioning analysis with “ATC” as the top-value method and “skmeans” as partitioning method.
set.seed(123)
res = consensus_partition(m, top_value_method = "ATC", partition_method = "skmeans",
cores = 4, anno = subtype, anno_col = subtype_col)In the following plot, cola suggests 5 as the best number of subgroups, but we select 4 as the best k because it gives more stable classification.
suggest_best_k(res)## The best k suggested by this function might not reflect the real
## subgroups in the data (especially when you expect a large best k). It
## is recommended to directly look at the plots from
## select_partition_number() or other related plotting functions.## [1] 5
## attr(,"optional")
## [1] 2 3 4select_partition_number(res)
Figure S6.1. Select the best number of groups.
The signature heatmap with 4 subgroups.
get_signatures(res, k = 4)
Figure S6.2. Signature heatmap of CP classification with 4 subgroups.
Next we apply hierarchical consensus partitioning (HCP) on the same matrix:
set.seed(123)
rh = hierarchical_partition(m, cores = 4, anno = subtype, anno_col = subtype_col)The subgroup hierarchy:
collect_classes(rh)
Figure S6.3. Subgroup hierarchy under HCP.
And the signature heatmap under HCP classification:
get_signatures(rh)
Figure S6.4. Signature heatmap under HCP classification.
The statistics on each node:
df = node_info(rh)
df## id best_method depth best_k n_columns n_signatures p_signatures is_leaf
## 1 0 ATC:skmeans 1 4 173 9686 0.8596787077 FALSE
## 2 01 ATC:skmeans 2 3 52 4051 0.3595455756 FALSE
## 3 011 ATC:skmeans 3 2 17 55 0.0048815124 TRUE
## 4 012 ATC:skmeans 3 2 21 625 0.0554717316 FALSE
## 5 0121 not applied 4 NA 10 NA NA TRUE
## 6 0122 not applied 4 NA 11 NA NA TRUE
## 7 013 ATC:skmeans 3 2 14 8 0.0007100382 TRUE
## 8 02 ATC:skmeans 2 3 66 4781 0.4243365581 FALSE
## 9 021 ATC:skmeans 3 3 25 806 0.0715363451 FALSE
## 10 0211 not applied 4 NA 11 NA NA TRUE
## 11 0212 not applied 4 NA 9 NA NA TRUE
## 12 0213 not applied 4 NA 5 NA NA TRUE
## 13 022 ATC:skmeans 3 2 24 666 0.0591106772 FALSE
## 14 0221 ATC:skmeans 4 2 14 30 0.0026626431 TRUE
## 15 0222 not applied 4 NA 10 NA NA TRUE
## 16 023 ATC:skmeans 3 2 17 227 0.0201473329 TRUE
## 17 03 ATC:skmeans 2 4 24 1376 0.1221265643 FALSE
## 18 031 not applied 3 NA 7 NA NA TRUE
## 19 032 not applied 3 NA 6 NA NA TRUE
## 20 033 not applied 3 NA 6 NA NA TRUE
## 21 034 not applied 3 NA 5 NA NA TRUE
## 22 04 ATC:skmeans 2 2 31 1954 0.1734268217 FALSE
## 23 041 ATC:skmeans 3 2 18 257 0.0228099760 TRUE
## 24 042 ATC:skmeans 3 2 13 175 0.0155320848 TRUEAnd the statistics on non-leaf nodes:
df[!df$is_leaf, ]## id best_method depth best_k n_columns n_signatures p_signatures is_leaf
## 1 0 ATC:skmeans 1 4 173 9686 0.85967871 FALSE
## 2 01 ATC:skmeans 2 3 52 4051 0.35954558 FALSE
## 4 012 ATC:skmeans 3 2 21 625 0.05547173 FALSE
## 8 02 ATC:skmeans 2 3 66 4781 0.42433656 FALSE
## 9 021 ATC:skmeans 3 3 25 806 0.07153635 FALSE
## 13 022 ATC:skmeans 3 2 24 666 0.05911068 FALSE
## 17 03 ATC:skmeans 2 4 24 1376 0.12212656 FALSE
## 22 04 ATC:skmeans 2 2 31 1954 0.17342682 FALSESession Info
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] C/UTF-8/C/C/C/C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.33 rmarkdown_2.9 preprocessCore_1.54.0
## [4] RColorBrewer_1.1-2 cola_1.9.4 BiocManager_1.30.16
## [7] colorout_1.2-2
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-2 rjson_0.2.20 ellipsis_0.3.2
## [4] mclust_5.4.7 circlize_0.4.13 markdown_1.1
## [7] XVector_0.32.0 GlobalOptions_0.1.2 clue_0.3-59
## [10] rstudioapi_0.13 bit64_4.0.5 AnnotationDbi_1.54.1
## [13] Polychrome_1.2.6 fansi_0.5.0 xml2_1.3.2
## [16] codetools_0.2-18 splines_4.1.0 doParallel_1.0.16
## [19] cachem_1.0.5 impute_1.66.0 jsonlite_1.7.2
## [22] Cairo_1.5-12.2 annotate_1.70.0 cluster_2.1.2
## [25] png_0.1-7 data.tree_1.0.0 compiler_4.1.0
## [28] httr_1.4.2 assertthat_0.2.1 Matrix_1.3-4
## [31] fastmap_1.1.0 htmltools_0.5.1.1 tools_4.1.0
## [34] gtable_0.3.0 glue_1.4.2 GenomeInfoDbData_1.2.6
## [37] dplyr_1.0.7 Rcpp_1.0.7 slam_0.1-48
## [40] Biobase_2.52.0 eulerr_6.1.0 jquerylib_0.1.4
## [43] vctrs_0.3.8 Biostrings_2.60.1 iterators_1.0.13
## [46] xfun_0.24 stringr_1.4.0 lifecycle_1.0.0
## [49] irlba_2.3.3 XML_3.99-0.6 dendextend_1.15.1
## [52] zlibbioc_1.38.0 scales_1.1.1 microbenchmark_1.4-7
## [55] parallel_4.1.0 ComplexHeatmap_2.9.3 yaml_2.2.1
## [58] memoise_2.0.0 gridExtra_2.3 ggplot2_3.3.5
## [61] sass_0.4.0 stringi_1.6.2 RSQLite_2.2.7
## [64] highr_0.9 genefilter_1.74.0 S4Vectors_0.30.0
## [67] foreach_1.5.1 BiocGenerics_0.38.0 shape_1.4.6
## [70] GenomeInfoDb_1.28.0 rlang_0.4.11 pkgconfig_2.0.3
## [73] matrixStats_0.59.0 bitops_1.0-7 evaluate_0.14
## [76] lattice_0.20-44 purrr_0.3.4 bit_4.0.4
## [79] tidyselect_1.1.1 magrittr_2.0.1 R6_2.5.0
## [82] IRanges_2.26.0 magick_2.7.2 generics_0.1.0
## [85] DBI_1.1.1 pillar_1.6.1 survival_3.2-11
## [88] KEGGREST_1.32.0 scatterplot3d_0.3-41 RCurl_1.98-1.3
## [91] tibble_3.1.2 crayon_1.4.1 utf8_1.2.1
## [94] skmeans_0.2-13 viridis_0.6.1 GetoptLong_1.0.3
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