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# source("subscripts/postBT_small_ideas/calc.coxmodel.R")

cox.univariate <- function(exprmat
						   ,pheno.df
						   ,survtimename
						   ,surveventname
						   ,corr.formulastring=c("+SEX", "+(Alter>45)", "+Stadium", "+IPS")
						   ,corr.formulastring.name=c("Sex", "AgeD", "Stadium", "IPS")
						   ,whichmodel="allCovariates"){
	
	univariate.models <- apply(cbind(seq(1, nrow(exprmat)), exprmat), 1, function(geneX){
		calc.coxmodel(response = geneX[-1]
					  ,pheno.df = pheno.df
					  ,survtimename = survtimename
					  ,surveventname = surveventname
					  ,corr.formulastring = corr.formulastring
					  ,corr.formulastring.name = corr.formulastring.name
					  ,all.corrections = TRUE
					  ,responsename = rownames(exprmat)[geneX[1]]
					  ,corrections.univariat = TRUE)
	})
	cox.per.gene <- lapply(univariate.models, function(coxGeneX) coxGeneX[[whichmodel]]) 
	# pvalues from the first coefficient (the genescore)
	pval.per.gene <- unlist(lapply(cox.per.gene, function(coxModelX)summary(coxModelX)$coef[1, 5])) 
	pval.df <- tibble::tibble("Gene"=names(pval.per.gene), "pValue"=pval.per.gene, "pValBonferroni"=pval.per.gene*length(pval.per.gene))
	return(pval.df)
}