In this tutorial, we run iEDGE on the TCGA PAAD dataset. To achieve reasonable run time, we restrict the run to the first 3 copy number alterations in the dataset.
For this tutorial, we use a processed iEDGE object. See iEDGE analysis of a simulated dataset for a description of the object's components. Also, see scripts_TCGA_pancancer/1.preprocess_all_TCGA.R in the vignette folder for an illustration of how to datasets were preprocessed for the TCGA pancancer analysis.
f_in <- "./tcga_sample_data/TCGA_PAAD.RDS" header <- paste(gsub(".RDS", "", basename(f_in)), sep = "") cat(paste("header: ", header, "\n", sep = "")) dat <- readRDS(f_in) # subset dat to first 3 copy number alterations names(dat) cn <- dat$cn[1:3,] gep <- dat$gep cisgenes <- dat$cisgenes[1:3] dat <- list(cn = cn, gep = gep, cisgenes = cisgenes) # read in geneset annotation files for pathway enrichment analysis gs.dir <- "./tcga_sample_data" gs.names <- c("h.all.v5.0.symbols.gmt","c2.cp.v5.0.symbols.gmt", "c3.tft.v5.0.symbols.gmt") gs.names <- paste(gs.dir, gs.names, sep = "/") # directory for output reports f_out <- "." res <- run_iEDGE(dat, header, f_out, gs.file = gs.names, gepid = "gene_symbol", cnid = "Unique.Name", cndir = "alteration_direction", fdr.cis.cutoff = 0.25, fdr.trans.cutoff = 0.01, fc.cis = 1.2, fc.trans = 1.5, min.drawsize = 3, onesided.cis = TRUE, onesided.trans = FALSE, uptest = "Amplification", downtest = "Deletion", min.group = 2, prune.col = "fdr", prune.thres = 0.05, hyperthres = 0.25, cis.boxplot = TRUE, trans.boxplot = FALSE, bipartite = TRUE, enrichment = TRUE, html = TRUE)
The HTML report of this run is contained here
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