R/getCorMatrix.R

In JordanVeldboom/compartmap: A/B compartment inference from ATAC-seq and methylation array data

#' Calculate Pearson correlations of a binned matrix
#' 
#' This function is used to generate a list object to be passed to getABSignal
#'
#' @param binmat      A binned matrix list object from getBinMatrix
#' @param squeeze    Whether to squeeze the matrix for Fisher's Z transformation
#' 
#' @return    A list object to pass to getABSignal
#' 
#' @import    GenomicRanges
#' 
#' @export 
#' 
#' @examples
#' 
#' library(GenomicRanges)
#' library(Homo.sapiens)
#' 
#' #Generate random genomic intervals of 1-1000 bp on chr1-22
#' #Modified from https://www.biostars.org/p/225520/
#' random_genomic_int <- data.frame(chr = rep("chr14", 100))
#' random_genomic_int$start <- apply(random_genomic_int, 1, function(x) { round(runif(1, 0, getSeqLengths(chr = x)[[1]]), 0) })
#' random_genomic_int$end <- random_genomic_int$start + runif(1, 1, 1000)
#' random_genomic_int$strand <- "*"
#' 
#' #Generate random counts
#' counts <- rnbinom(1000, 1.2, 0.4)
#' 
#' #Build random counts for 10 samples
#' count.mat <- matrix(sample(counts, nrow(random_genomic_int) * 10, replace = FALSE), ncol = 10)
#' colnames(count.mat) <- paste0("sample_", seq(1:10))
#' 
#' #Bin counts
#' bin.counts <- getBinMatrix(count.mat, makeGRangesFromDataFrame(random_genomic_int), chr = "chr14", genome = "hg19")
#' 
#' #Calculate correlations
#' bin.cor.counts <- getCorMatrix(bin.counts)

getCorMatrix <- function(binmat, squeeze = FALSE) {
  #Calculate correlations
  message("Calculating correlations...")
  #bind back up the global means and shrunken bins
  binmat$x <- cbind(binmat$x, binmat$gmeans)
  binmat.cor <- suppressWarnings(cor(t(binmat$x)))
  gr.cor  <- binmat$gr
  if (squeeze) {
    binmat.cor <- fisherZ(binmat.cor)
    }
  message("Done...")
  return(list(gr.cor=gr.cor, binmat.cor=binmat.cor))
}


#Helper function to squeeze binary matrix for transformation
.squeezeit <- function(cormat) {
  cormat * 0.999999
}