Last updated: 2024-05-22
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| File | Version | Author | Date | Message |
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| Rmd | 94fd282 | Zhen Zuo | 2024-05-22 | update viper input prepare |
| html | 94fd282 | Zhen Zuo | 2024-05-22 | update viper input prepare |
The VIPER (Virtual Inference of Protein-activity by Enriched Regulon analysis) algorithm allows computational inference of protein activity, on an individual sample basis, from gene expression profile data. It uses the expression of genes that are most directly regulated by a given protein, such as the targets of a transcription factor (TF), as an accurate reporter of its activity.
We have shown that analysis of TF targets inferred by the ARACNe algorithm, using the Master Regulator Inference algorithm (MARINA), is effective in identifying drivers of specific cellular phenotype which could be experimentally validated.
library(viper)
library(aracne.networks)
library(dplyr)
library(plyr)
library(stringr)
library(Biobase)
library(EnsDb.Hsapiens.v86)ARACNe-AP was run on RNA-Seq datasets normalized using Variance-Stabilizing Transformation. The raw data was downloaded on April 15th, 2015 from the TCGA official website.
items <- data(package="aracne.networks")$results[, "Item"]
print(items) [1] "regulonblca" "regulonbrca" "reguloncesc" "reguloncoad" "regulonesca"
[6] "regulongbm" "regulonhnsc" "regulonkirc" "regulonkirp" "regulonlaml"
[11] "regulonlihc" "regulonluad" "regulonlusc" "regulonnet" "regulonov"
[16] "regulonpaad" "regulonpcpg" "regulonprad" "regulonread" "regulonsarc"
[21] "regulonstad" "regulontgct" "regulonthca" "regulonthym" "regulonucec"df <- read.csv("data/viper/aracne.networks.csv")
knitr::kable(df)| regulon | description |
|---|---|
| regulonblca | Human Bladder Carcinoma context-specific ARACNe interactome |
| regulonbrca | Human Breast Carcinoma context-specific ARACNe interactome |
| reguloncesc | Human Cervical Squamous Carcinoma context-specific ARACNe interactome |
| reguloncoad | Human Colon Adenocarcinoma context-specific ARACNe interactome |
| regulonesca | Human Esophageal Carcinoma context-specific ARACNe interactome |
| regulongbm | Human Glioblastoma context-specific ARACNe interactome |
| regulonhnsc | Human Head and Neck Squamous Carcinoma context-specific ARACNe interactome |
| regulonkirc | Human Kidney Renal Clear Cell Carcinoma context-specific ARACNe interactome |
| regulonkirp | Human Kidney Papillary Carcinoma context-specific ARACNe interactome |
| regulonlaml | Human Acute Myeloid Leukemia context-specific ARACNe interactome |
| regulonlihc | Human Liver Hepatocellular Carcinoma context-specific ARACNe interactome |
| regulonluad | Human Lung Adenocarcinoma context-specific ARACNe interactome |
| regulonlusc | Human Lung Squamous Carcinoma context-specific ARACNe interactome |
| regulonnet | Human Neuroendocrine tumor context-specific ARACNe interactome |
| regulonov | Human Ovarian Carcinoma context-specific ARACNe interactome |
| regulonpaad | Human Pancreas Carcinoma context-specific ARACNe interactome |
| regulonpcpg | Human Pheochromocytoma and Paraganglioma context-specific ARACNe interactome |
| regulonprad | Human Prostate Carcinoma context-specific ARACNe interactome |
| regulonread | Human Rectal Adenocarcinoma context-specific ARACNe interactome |
| regulonsarc | Human Sarcoma context-specific ARACNe interactome |
| regulonstad | Human Stomach Adenocarcinoma context-specific ARACNe interactome |
| regulontgct | Human Testicular Cancer context-specific ARACNe interactome |
| regulonthca | Human Thyroid Carcinoma context-specific ARACNe interactome |
| regulonthym | Human Thymoma context-specific ARACNe interactome |
| regulonucec | Human Utherine Corpus Endometroid Carcinoma context-specific ARACNe interactome |
Export network to adj files
for (item in items) {
if (!file.exists(paste("data/viper/regulon/", item, ".adj",
sep = ""))) {
get(item)
write.regulon(get(item), file = paste("data/viper/regulon/", item, ".adj",
sep = ""))
}
}Convert Entrez Gene ids to SYMBOL.
for (item in items) {
if (!file.exists(paste("data/viper/regulon/", item, "_SYMBOL.adj", sep = ""))) {
df <- read.csv(paste("data/viper/regulon/", item, ".adj", sep = ""),
sep = "\t")
geneID <- ensembldb::select(EnsDb.Hsapiens.v86, keys = as.character(df$Regulator),
keytype = "ENTREZID", columns = c("SYMBOL", "ENTREZID", "GENEID"))
df$Regulator <- plyr::mapvalues(df$Regulator, from = geneID$ENTREZID,
to = geneID$SYMBOL, warn_missing = FALSE)
geneID <- ensembldb::select(EnsDb.Hsapiens.v86, keys = as.character(df$Target),
keytype = "ENTREZID", columns = c("SYMBOL", "ENTREZID", "GENEID"))
df$Target <- plyr::mapvalues(df$Target, from = geneID$ENTREZID,
to = geneID$SYMBOL, warn_missing = FALSE)
can_be_integer <- function(x) {
suppressWarnings(!is.na(as.integer(x)))
}
f1 <- !sapply(df$Regulator, can_be_integer)
f2 <- !sapply(df$Target, can_be_integer)
df <- df[f1 & f2, ]
write.table(df, file = paste("data/viper/regulon/", item, "_SYMBOL.adj",
sep = ""),sep = "\t",quote = FALSE, row.names = FALSE)
}
}df <- read.csv("data/viper/RNA_regulon_pairs.csv")
knitr::kable(df)| Lable | RNA | regulon | description |
|---|---|---|---|
| kirc_normal | data/viper/RNA_BCM_v1/ccRCC_RNAseq_circRNA_RSEM_UQ_log2_Normal.txt | regulonkirc | Human Kidney Renal Clear Cell Carcinoma context-specific ARACNe interactome |
| kirc_tumor | data/viper/RNA_BCM_v1/ccRCC_RNAseq_circRNA_RSEM_UQ_log2_Tumor.txt | regulonkirc | Human Kidney Renal Clear Cell Carcinoma context-specific ARACNe interactome |
| hnsc_normal | data/viper/RNA_BCM_v1/HNSCC_RNAseq_gene_RSEM_coding_UQ_1500_log2_Normal.txt | regulonhnsc | Human Head and Neck Squamous Carcinoma context-specific ARACNe interactome |
| hnsc_tumor | data/viper/RNA_BCM_v1/HNSCC_RNAseq_gene_RSEM_coding_UQ_1500_log2_Tumor.txt | regulonhnsc | Human Head and Neck Squamous Carcinoma context-specific ARACNe interactome |
| lusc_normal | data/viper/RNA_BCM_v1/LSCC_RNAseq_gene_RSEM_coding_UQ_1500_log2_Normal.txt | regulonlusc | Human Lung Squamous Carcinoma context-specific ARACNe interactome |
| lusc_tumor | data/viper/RNA_BCM_v1/LSCC_RNAseq_gene_RSEM_coding_UQ_1500_log2_Tumor.txt | regulonlusc | Human Lung Squamous Carcinoma context-specific ARACNe interactome |
| luad_normal | data/viper/RNA_BCM_v1/LUAD_RNAseq_gene_RSEM_coding_UQ_1500_log2_Normal.txt | regulonluad | Human Lung Adenocarcinoma context-specific ARACNe interactome |
| luad_tumor | data/viper/RNA_BCM_v1/LUAD_RNAseq_gene_RSEM_coding_UQ_1500_log2_Tumor.txt | regulonluad | Human Lung Adenocarcinoma context-specific ARACNe interactome |
| paad_normal | data/viper/RNA_BCM_v1/PDAC_RNAseq_gene_RSEM_coding_UQ_1500_log2_Normal.txt | regulonpaad | Human Pancreas Carcinoma context-specific ARACNe interactome |
| paad_tumor | data/viper/RNA_BCM_v1/PDAC_RNAseq_gene_RSEM_coding_UQ_1500_log2_Tumor.txt | regulonpaad | Human Pancreas Carcinoma context-specific ARACNe interactome |
for (i in 1:nrow(df)) {
exprsFile <- df$RNA[i]
adjfile <- paste("data/viper/regulon/", df$regulon[1], "_SYMBOL.adj", sep = "")
exprs <- as.matrix(read.table(exprsFile, header = TRUE, sep = "\t",
row.names = 1, as.is = TRUE))
rownames(exprs) <- sub("\\..*$", "", rownames(exprs))
geneID <- ensembldb::select(EnsDb.Hsapiens.v86, keys = rownames(exprs),
keytype = "GENEID", columns = c("SYMBOL", "ENTREZID", "GENEID"))
rownames(exprs) <- plyr::mapvalues(rownames(exprs), from = geneID$GENEID,
to = geneID$SYMBOL, warn_missing = FALSE)
print(table(sapply(rownames(exprs), function(x) startsWith(x, "ENSG"))))
exprs <- exprs[!sapply(rownames(exprs), function(x) startsWith(x, "ENSG")),
]
# Identify duplicated row names
duplicated_rows <- duplicated(rownames(exprs))
# Remove rows with duplicated row names
cleaned_exprs <- exprs[!duplicated_rows, , drop = FALSE]
saveRDS(cleaned_exprs, paste("data/viper/exprs/", df$Lable[i], ".RDS", sep = ""))
}
FALSE
6913
FALSE
6913
FALSE TRUE
57207 3462
FALSE TRUE
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FALSE TRUE
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FALSE TRUE
57207 3462
FALSE TRUE
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FALSE TRUE
57207 3462
FALSE TRUE
57207 3462
FALSE TRUE
57207 3462
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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.28.0
[3] AnnotationFilter_1.28.0 GenomicFeatures_1.56.0
[5] AnnotationDbi_1.66.0 GenomicRanges_1.56.0
[7] GenomeInfoDb_1.40.0 IRanges_2.38.0
[9] S4Vectors_0.42.0 stringr_1.5.1
[11] plyr_1.8.9 dplyr_1.1.4
[13] aracne.networks_1.30.0 viper_1.38.0
[15] Biobase_2.64.0 BiocGenerics_0.50.0
[17] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] bitops_1.0-7 DBI_1.2.2
[3] rlang_1.1.3 magrittr_2.0.3
[5] git2r_0.33.0 matrixStats_1.3.0
[7] e1071_1.7-14 compiler_4.4.0
[9] RSQLite_2.3.6 getPass_0.2-4
[11] png_0.1-8 callr_3.7.6
[13] vctrs_0.6.5 ProtGenerics_1.36.0
[15] pkgconfig_2.0.3 crayon_1.5.2
[17] fastmap_1.2.0 XVector_0.44.0
[19] utf8_1.2.4 Rsamtools_2.20.0
[21] promises_1.3.0 rmarkdown_2.27
[23] UCSC.utils_1.0.0 ps_1.7.6
[25] purrr_1.0.2 bit_4.0.5
[27] xfun_0.44 zlibbioc_1.50.0
[29] cachem_1.1.0 jsonlite_1.8.8
[31] blob_1.2.4 later_1.3.2
[33] DelayedArray_0.30.0 BiocParallel_1.38.0
[35] parallel_4.4.0 R6_2.5.1
[37] bslib_0.7.0 stringi_1.8.4
[39] rtracklayer_1.64.0 jquerylib_0.1.4
[41] SummarizedExperiment_1.34.0 Rcpp_1.0.12
[43] knitr_1.46 mixtools_2.0.0
[45] httpuv_1.6.15 Matrix_1.7-0
[47] splines_4.4.0 tidyselect_1.2.1
[49] abind_1.4-5 rstudioapi_0.16.0
[51] yaml_2.3.8 codetools_0.2-20
[53] curl_5.2.1 processx_3.8.4
[55] lattice_0.22-6 tibble_3.2.1
[57] KEGGREST_1.44.0 evaluate_0.23
[59] survival_3.6-4 proxy_0.4-27
[61] kernlab_0.9-32 Biostrings_2.72.0
[63] pillar_1.9.0 MatrixGenerics_1.16.0
[65] whisker_0.4.1 KernSmooth_2.23-24
[67] plotly_4.10.4 generics_0.1.3
[69] RCurl_1.98-1.14 rprojroot_2.0.4
[71] ggplot2_3.5.1 munsell_0.5.1
[73] scales_1.3.0 class_7.3-22
[75] glue_1.7.0 lazyeval_0.2.2
[77] tools_4.4.0 BiocIO_1.14.0
[79] data.table_1.15.4 GenomicAlignments_1.40.0
[81] XML_3.99-0.16.1 fs_1.6.4
[83] grid_4.4.0 tidyr_1.3.1
[85] colorspace_2.1-0 nlme_3.1-164
[87] GenomeInfoDbData_1.2.12 restfulr_0.0.15
[89] cli_3.6.2 fansi_1.0.6
[91] S4Arrays_1.4.1 segmented_2.1-0
[93] viridisLite_0.4.2 gtable_0.3.5
[95] sass_0.4.9 digest_0.6.35
[97] SparseArray_1.4.1 rjson_0.2.21
[99] htmlwidgets_1.6.4 memoise_2.0.1
[101] htmltools_0.5.8.1 lifecycle_1.0.4
[103] httr_1.4.7 bit64_4.0.5
[105] MASS_7.3-60.2