#' This function returns p-values assessing the difference between two bootstrap distributions of correlation coefficients based on the Kolmogorov-Smirnov test
#'
#' @param corrng1 Table 1 with annotations and correlation coefficients
#' @param corrng2 Table 2 with annotations and correlation coefficients
#' @return Matrix with p-values
#' @export
compareCorDistributions<-function(corrng1, corrng2) {
# check input arguments
if(!is.list(corrng1)) stop("corrng1 must be a list")
if(!is.list(corrng2)) stop("corrng2 must be a list")
# retrieve array dimensions
nrois <- dim(corrng1[[4]])[1]
# perform KS test
pval <- matrix(1, nrow=nrois, ncol=nrois)
for(x in 1:nrois) {
for(y in 1:nrois) {
pval[x,y] <- ks.test(corrng1[[4]][x,y,is.finite(corrng1[[4]][x,y,])], corrng2[[4]][x,y,is.finite(corrng2[[4]][x,y,])])$p
}
}
# return matrix with (approximated) p-values
return(list(corrng1[[1]],corrng1[[2]],corrng1[[3]],pval))
}
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