#' survAnal: runs cox proportional hazard models against patients with top and bottom n percent
#' expression values
#'
#' derived from the runCPH function will process three separate file one with sample ids one with
#' survival data and one with expression values. In test mode if a list of sample ids are supplied
#' but not the other two files it assumes the ids are from the TCGA SKCM data set and uses a
#' cached version of the normalised expression values from the TCGA SKCM data set and a currated
#' set of survival data.
#'
#' @param geneData names of the genes to be tested (must be in the rownames of exprData)
#' @param survData survival data must contain a time and status
#' @param exprData normalised gene expression data
#' @param exprRange top and bottom quantiles of expression values to select samples for testing
#' @param survStatus name of suvival censoring column
#' @param survTime name of survival time column
#' @param mcores number of cores for bioparallel
#'
#' @return the function creates several csv output files with the Cox PH results, the Limma DGE
#' results, a combined set of both results and a set of those genes for which the DGE result is
#' significant at 0.05 FDR
#'
#'
#' @export
survAnal2 <- function(survData=NULL, exprData=NULL,survTime="time", exprRange=0.33,
survStatus="status",mcores=4,summarise=TRUE,geneList=NULL){
# Filter by samples in sample data just incase
exprSamples <- colnames(exprData)
survSamples <- rownames(survData)
# Sanity check the sample ids are the same
if(!identical(unique(sort(exprSamples)), sort(survSamples))){
stop(paste0("The samples in ",paste0(survSamples,collapse=",")," do not match those in the ",
"expression and survival data files"))
}
if(!identical(sort(exprSamples), sort(survSamples))){
warning(paste0("The samples in ",paste(survSamples,collapse=","),
" potentially have more than one expression sample"))
}
mcParam <- BiocParallel::MulticoreParam(workers=mcores)
exprData <- as.matrix(exprData)
if(is.null(geneList)){
geneRun <- rownames(exprData)
}else{
geneRun <- geneList
}
# run cox proportional hazard on all expressed genes
#resList <- BiocParallel::bplapply(geneRun,function(geneid){
resList <- lapply(geneRun,function(geneid){
# get Hi samples expression >= n centile
hlim <- as.numeric(quantile(exprData[geneid,],1-exprRange))
hi <- survData[names(which(exprData[geneid,]>=hlim)),]
hi$Class <- "high"
# get Lo samples expression <= 1-n centile
llim <- as.numeric(quantile(exprData[geneid,],exprRange))
lo <- survData[names(which(exprData[geneid,]<=llim)),]
lo$Class <- "low"
# make survival data frame
survData2 <- rbind(hi,lo)
# set expression class
survData2$Expr <- exprData[geneid,match(rownames(survData2),colnames(exprData))]
survData2$Class <- as.factor(survData2$Class)
survData2$time <- as.numeric(survData2[,survTime])
survData2$status <- survData2[,survStatus]
survData2$survival <- with(survData2,survival::Surv(time,status))
# fit cox proportional hazards (non-parametric) survival model
coxfit <- survival::coxph(survival~Expr, data=survData2)
diffit <- survival::survdiff(survival~Class, data=survData2)
surfit <- survival::survfit(survival~Class, data=survData2)
if(summarise){
c(summary(coxfit)$conf.int,summary(coxfit)$sctest["pvalue"],
signif(pchisq(diffit$chisq, length(diffit$n)-1, lower.tail=FALSE),4))
}else{
plt <- survminer::ggsurvplot(surfit, data=survData2, pval=T,risk.table=T, title=geneid)
list(cph=coxfit,dif=diffit,kmf=surfit,plt=plt)
}
})#, BPPARAM=mcParam)
names(resList) <- geneRun
# convert results to data frame
if(summarise){
resSurv<- plyr::ldply(resList)
rownames(resSurv) <- resSurv[,1]
resSurv <- resSurv[,-1]
colnames(resSurv) <- c("HR","1/HR","lower95CI","upper95CI","cph.pvalue","km.pvalue")
resSurv[order(resSurv$km.pvalue),]
}else{
resList
}
}
.testSurvAnal <- function(){
survTime="time"
exprRange=0.33
survStatus="status"
mcores=4
summarise=TRUE
geneid=rownames(exprData)[1]
}
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