params <- list(seed = 2924) ## ---- eval=FALSE----------------------------------------------------------- # if (!require("BiocManager")) # install.packages("BiocManager") # BiocManager::install("glmSparseNet") ## ----packages, message=FALSE, warning=FALSE-------------------------------- library(dplyr) library(ggplot2) library(survival) library(loose.rock) library(futile.logger) library(curatedTCGAData) library(TCGAutils) # library(glmSparseNet) # # Some general options for futile.logger the debugging package .Last.value <- flog.layout(layout.format('[~l] ~m')) .Last.value <- loose.rock::show.message(FALSE) # Setting ggplot2 default theme as minimal theme_set(ggplot2::theme_minimal()) ## ----curated_data, include=FALSE------------------------------------------- # chunk not included as it produces to many unnecessary messages prad <- curatedTCGAData(diseaseCode = "PRAD", assays = "RNASeq2GeneNorm", FALSE) ## ----curated_data_non_eval, eval=FALSE------------------------------------- # prad <- curatedTCGAData(diseaseCode = "PRAD", assays = "RNASeq2GeneNorm", FALSE) ## ----data.show, warning=FALSE, error=FALSE--------------------------------- # keep only solid tumour (code: 01) prad.primary.solid.tumor <- TCGAutils::splitAssays(prad, '01') xdata.raw <- t(assay(prad.primary.solid.tumor[[1]])) # Get survival information ydata.raw <- colData(prad.primary.solid.tumor) %>% as.data.frame %>% # Find max time between all days (ignoring missings) rowwise %>% mutate(time = max(days_to_last_followup, days_to_death, na.rm = TRUE)) %>% # Keep only survival variables and codes select(patientID, status = vital_status, time) %>% # Discard individuals with survival time less or equal to 0 filter(!is.na(time) & time > 0) %>% as.data.frame # Set index as the patientID rownames(ydata.raw) <- ydata.raw$patientID # keep only features that have standard deviation > 0 xdata.raw <- xdata.raw[TCGAbarcode(rownames(xdata.raw)) %in% rownames(ydata.raw),] xdata.raw <- xdata.raw %>% { (apply(., 2, sd) != 0) } %>% { xdata.raw[, .] } %>% scale # Order ydata the same as assay ydata.raw <- ydata.raw[TCGAbarcode(rownames(xdata.raw)), ] set.seed(params$seed) small.subset <- c(geneNames(c('ENSG00000103091', 'ENSG00000064787', 'ENSG00000119915', 'ENSG00000120158', 'ENSG00000114491', 'ENSG00000204176', 'ENSG00000138399'))$external_gene_name, sample(colnames(xdata.raw), 100)) %>% unique %>% sort xdata <- xdata.raw[, small.subset[small.subset %in% colnames(xdata.raw)]] ydata <- ydata.raw %>% select(time, status) ## ----fit------------------------------------------------------------------- set.seed(params$seed) fitted <- cv.glmHub(xdata, Surv(ydata$time, ydata$status), family = 'cox', nlambda = 1000, network = 'correlation', network.options = networkOptions(cutoff = .6, min.degree = .2)) ## ----results--------------------------------------------------------------- plot(fitted) ## ----show_coefs------------------------------------------------------------ coefs.v <- coef(fitted, s = 'lambda.min')[,1] %>% { .[. != 0]} coefs.v %>% { data.frame(ensembl.id = names(.), gene.name = geneNames(names(.))$external_gene_name, coefficient = ., stringsAsFactors = FALSE) } %>% arrange(gene.name) %>% knitr::kable() ## ----hallmarks------------------------------------------------------------- geneNames(names(coefs.v)) %>% { hallmarks(.$external_gene_name)$heatmap } ## -------------------------------------------------------------------------- separate2GroupsCox(as.vector(coefs.v), xdata[, names(coefs.v)], ydata, plot.title = 'Full dataset', legend.outside = FALSE)