# dichotGSAR(): dichotomize p-value matrix to 0-1 matrix for each PW across datasetsĀ£Ā»output metaP and metaFDR as well.
# INPUT input2PWcTalk: file name of the CSV output file from prior metaGSAR analysis.
### includes k p-values for each pathway represented in rows (k: number of repetitive datasets).
### Several columns of meta-P values follow the dataset-specific p-value columns.
dichotGSAR <- function(input2PWcTalk,pTh.dataset=0.01) { #,meta=c('glmm','inverse')[1]
if (is.null(nrow(input2PWcTalk))) {# input is a file name, rather the data frame
input2PWcTalk <- read.csv(input2PWcTalk,as.is=TRUE,row.names=1)
}
metaGSAR <- input2PWcTalk
GSAR <- metaGSAR[,-seq_along(c(1:3))]
dichotP <- GSAR<=pTh.dataset
dichotP[is.na(dichotP)] <- 0
metaP <- metaGSAR[,'bootstrap.p']
names(metaP) <- rownames(metaGSAR)
res <- list(dichotP=dichotP,metaP=metaP)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.