## ---- eval=FALSE--------------------------------------------------------- ## source("http://www.bioconductor.org/biocLite.R") ## biocLite("iGC") ## ------------------------------------------------------------------------ library(iGC) ## ------------------------------------------------------------------------ sample_desc_pth <- system.file("extdata", "sample_desc.csv", package = "iGC") sample_desc <- create_sample_desc(sample_desc_pth) ## ---- eval=FALSE--------------------------------------------------------- ## sample_desc <- create_sample_desc( ## sample_names = sample_desc$Sample, ## cna_filepaths = sample_desc$CNA_filepath, ## ge_filepaths = sample_desc$GE_filepath ## ) ## ------------------------------------------------------------------------ head(sample_desc) ## ------------------------------------------------------------------------ gene_exp <- create_gene_exp(sample_desc, progress = FALSE) ## ---- eval=FALSE--------------------------------------------------------- ## gene_exp <- create_gene_exp( ## sample_desc, ## read_fun = read.table, ## progress = TRUE, progress_width = 60, ## # arugments passed to the customized read_fun (here is read.table) ## header = FALSE, ## skip = 2, ## na.strings = "null", ## colClasses = c("character", "double") ## ) ## ------------------------------------------------------------------------ gene_exp[GENE %in% c('TP53', 'BRCA1', 'NFKB1'), 1:10, with=FALSE] ## ------------------------------------------------------------------------ my_cna_reader <- function(cna_filepath) { cna <- data.table::fread(cna_filepath, sep = '\t', header = TRUE) cna[, .(Chromosome, Start, End, Segment_Mean)] } gain_loss = log2(c(2.4, 1.6)) - 1 gene_cna <- create_gene_cna( sample_desc, gain_threshold = gain_loss[1], loss_threshold = gain_loss[2], read_fun = my_cna_reader, progress = FALSE ) gene_cna[GENE %in% c('TP53', 'BRCA1', 'NFKB1'), 1:10, with=FALSE] ## ---- eval=FALSE--------------------------------------------------------- ## # Change 4 to match one's total CPU cores ## doMC::registerDoMC(cores = 4) ## gene_cna <- faster_gene_cna( ## sample_desc, gain_loss[[1]], gain_loss[[2]], parallel = TRUE ## ) ## ------------------------------------------------------------------------ cna_driven_genes <- find_cna_driven_gene( gene_cna, gene_exp, gain_prop = 0.15, loss_prop = 0.15, progress = FALSE, parallel = FALSE ) head(cna_driven_genes$gain_driven) head(cna_driven_genes$loss_driven) head(cna_driven_genes$both) ## ------------------------------------------------------------------------ cna_driven_genes$loss_driven[GENE %in% c('BRCA1')]