Last updated: 2024-06-20
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library(viper)
library(EnsDb.Hsapiens.v86)
library(foreach)
library(doParallel)
cluster <- makeCluster(5)
registerDoParallel(cluster)
dir.create("output/regulon/",showWarnings = FALSE)
set.seed(0)for (name in c("kirc", "hnsc", "lusc", "luad", "paad")) {
df <- read.csv(paste("output/MUT/", name, "_fc.csv", sep = ""))
df$ID <- paste(df$SYMBOL, "_R", sep = "")
# Group by Regulator and concatenate elements of each group into
# strings
df$SYMBOL <- paste(df$SYMBOL, "1",sep = "\t")
result <- aggregate(ID ~ SYMBOL, data = df, FUN = function(x) paste(x,
collapse = "\t"))
# Print the result
file <- file(paste("output/regulon/CNV_", name, ".adj", sep = ""))
writeLines(paste(result$ID, result$SYMBOL, sep = "\t"), file)
close(file)
}set.seed(0)
for (name in c("kirc", "hnsc", "lusc", "luad", "paad")) {
adjfile <- paste("output/regulon/regulon", name, ".adj", sep = "")
cnv_normal <- readRDS(paste("output/cnv/count_matrix_", name, "_normal.RDS",
sep = ""))
meta <- read.csv(paste("output/MUT/", name, "_fc_0.01.csv",sep = ""))
cnv_normal <- cnv_normal[rownames(cnv_normal) %in% meta$SYMBOL,]
gex_normal <- readRDS(paste("output/expr/count_matrix_", name, "_normal.RDS",
sep = ""))
cnv_tumor <- readRDS(paste("output/cnv/count_matrix_", name, "_tumor.RDS",
sep = ""))
cnv_tumor <- cnv_tumor[rownames(cnv_tumor) %in% meta$SYMBOL,]
gex_tumor <- readRDS(paste("output/expr/count_matrix_", name, "_tumor.RDS",
sep = ""))
cnv <- merge(cnv_normal, cnv_tumor, by = "row.names", all = FALSE)
gex <- merge(gex_normal, gex_tumor, by = "row.names", all = FALSE)
nor_sam <- colnames(gex)[1:ncol(gex_normal)]
tur_sam <- colnames(gex)[(ncol(gex_normal)+1):ncol(gex)]
rownames(cnv) <- paste(cnv$Row.names,"_P", sep = "")
cnv$Row.names <- NULL
rownames(gex) <- gex$Row.names
gex$Row.names <- NULL
common_terms <- intersect(colnames(gex),colnames(cnv))
cnv <- cnv[,common_terms]
gex <- gex[,common_terms]
expr <- rbind(gex,cnv)
signature <- c(rep("normal", sum(common_terms %in% nor_sam)), rep("tumor", sum(common_terms %in% tur_sam)))
dset <- ExpressionSet(assayData = as.matrix(expr))
dset$description <- factor(signature)
regul <- aracne2regulon(adjfile, dset, verbose = TRUE)
saveRDS(dset, paste("output/cnv/dset_", name, ".RDS", sep = ""))
saveRDS(regul, paste("output/cnv/regul_", name, ".RDS", sep = ""))
}foreach(name = c("kirc", "hnsc", "lusc", "luad", "paad")) %dopar% {
library(viper)
dset <- readRDS(paste("output/cnv/dset_", name, ".RDS",
sep = ""))
regulon <- readRDS(paste("output/cnv/regul_", name, ".RDS", sep = ""))
signature <- rowTtest(dset, "description", "tumor", "normal")
signature <- (qnorm(signature$p.value/2, lower.tail = FALSE) * sign(signature$statistic))[,
1]
# NULL model by sample permutations
nullmodel <- ttestNull(dset, "description", "tumor", "normal", per = 1000,
repos = TRUE, verbose = FALSE)
## Normalized Enrichment Score (NES)
mrs <- msviper(signature, regulon, nullmodel, verbose = FALSE)
temp <- summary(mrs, length(mrs$regulon))
temp$Sign <- ifelse(temp$NES > 0, "+", "-")
temp <- temp[temp$FDR < 0.1, ]
temp <- temp[, c("Regulon", "NES", "Sign")]
temp$NES <- abs(temp$NES)
dir.create(paste("output/cnv/",
name, "/", sep = ""),showWarnings = F)
write.table(temp, paste("output/cnv/",
name, "/cnv_regulators.txt", sep = ""), quote = F, sep = "\t", row.names = F,
col.names = F)
}[[1]]
NULL
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NULL
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NULL
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NULL
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NULL
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] doParallel_1.0.17 iterators_1.0.14
[3] foreach_1.5.2 EnsDb.Hsapiens.v86_2.99.0
[5] ensembldb_2.28.0 AnnotationFilter_1.28.0
[7] GenomicFeatures_1.56.0 AnnotationDbi_1.66.0
[9] GenomicRanges_1.56.1 GenomeInfoDb_1.40.1
[11] IRanges_2.38.0 S4Vectors_0.42.0
[13] viper_1.38.0 Biobase_2.64.0
[15] BiocGenerics_0.50.0
loaded via a namespace (and not attached):
[1] DBI_1.2.3 bitops_1.0-7
[3] rlang_1.1.4 magrittr_2.0.3
[5] git2r_0.33.0 matrixStats_1.3.0
[7] e1071_1.7-14 compiler_4.4.0
[9] RSQLite_2.3.7 png_0.1-8
[11] vctrs_0.6.5 ProtGenerics_1.36.0
[13] stringr_1.5.1 pkgconfig_2.0.3
[15] crayon_1.5.2 fastmap_1.2.0
[17] XVector_0.44.0 utf8_1.2.4
[19] Rsamtools_2.20.0 promises_1.3.0
[21] rmarkdown_2.27 UCSC.utils_1.0.0
[23] purrr_1.0.2 bit_4.0.5
[25] xfun_0.45 zlibbioc_1.50.0
[27] cachem_1.1.0 jsonlite_1.8.8
[29] blob_1.2.4 later_1.3.2
[31] DelayedArray_0.30.1 BiocParallel_1.38.0
[33] R6_2.5.1 bslib_0.7.0
[35] stringi_1.8.4 rtracklayer_1.64.0
[37] jquerylib_0.1.4 SummarizedExperiment_1.34.0
[39] Rcpp_1.0.12 knitr_1.47
[41] mixtools_2.0.0 httpuv_1.6.15
[43] Matrix_1.7-0 splines_4.4.0
[45] tidyselect_1.2.1 abind_1.4-5
[47] rstudioapi_0.16.0 yaml_2.3.8
[49] codetools_0.2-20 curl_5.2.1
[51] lattice_0.22-6 tibble_3.2.1
[53] KEGGREST_1.44.0 evaluate_0.24.0
[55] survival_3.7-0 proxy_0.4-27
[57] kernlab_0.9-32 Biostrings_2.72.1
[59] pillar_1.9.0 MatrixGenerics_1.16.0
[61] KernSmooth_2.23-24 plotly_4.10.4
[63] generics_0.1.3 rprojroot_2.0.4
[65] RCurl_1.98-1.14 ggplot2_3.5.1
[67] munsell_0.5.1 scales_1.3.0
[69] class_7.3-22 glue_1.7.0
[71] lazyeval_0.2.2 tools_4.4.0
[73] BiocIO_1.14.0 data.table_1.15.4
[75] GenomicAlignments_1.40.0 fs_1.6.4
[77] XML_3.99-0.16.1 grid_4.4.0
[79] tidyr_1.3.1 colorspace_2.1-0
[81] nlme_3.1-165 GenomeInfoDbData_1.2.12
[83] restfulr_0.0.15 cli_3.6.2
[85] workflowr_1.7.1 fansi_1.0.6
[87] S4Arrays_1.4.1 segmented_2.1-0
[89] viridisLite_0.4.2 dplyr_1.1.4
[91] gtable_0.3.5 sass_0.4.9
[93] digest_0.6.35 SparseArray_1.4.8
[95] rjson_0.2.21 htmlwidgets_1.6.4
[97] memoise_2.0.1 htmltools_0.5.8.1
[99] lifecycle_1.0.4 httr_1.4.7
[101] bit64_4.0.5 MASS_7.3-61