R/med.norm.R

In jmonlong/PopSV: Population-based detection of structural variants from Read-Depth signal

##' Global median normalization of bin counts. After such normalization, each sample has the same median bin count.
##' @title Global median normalization
##' @param bc.df a data.frame with 'chr', 'start', 'end' columns and then one column per sample with its bin counts.
##' @param nb.cores the number of cores to use. Default is 1.
##' @param norm.stats.comp Should some statistics on the normalized bin count be computed (mean, sd, outliers). Default is TRUE.
##' @return a list with
##' \item{norm.stats}{a data.frame witht some metrics about the normalization of each
##' bin (row) : coverage average and standard deviation; number of outlier samples}
##' \item{bc.norm}{a data.frame, similar to the input 'bc.df', with the normalized bin counts.}
##' @author Jean Monlong
##' @export
med.norm <- function(bc.df, nb.cores = 1, norm.stats.comp = TRUE) {
    all.samples = setdiff(colnames(bc.df), c("chr", "start", "end"))
    rownames(bc.df) = bins = paste(bc.df$chr, as.integer(bc.df$start), as.integer(bc.df$end),
        sep = "-")

    bc.norm = createEmptyDF(c("character", rep("integer", 2), rep("numeric", length(all.samples))),
        length(bins))
    colnames(bc.norm) = c("chr", "start", "end", all.samples)
    bc.norm$chr = bc.df$chr
    bc.norm$start = bc.df$start
    bc.norm$end = bc.df$end

    bc.mat = as.matrix(bc.df[, all.samples])
    bc.cov = as.numeric(parallel::mclapply(1:ncol(bc.mat), function(cc) stats::median(bc.mat[,
        cc], na.rm = TRUE), mc.cores = nb.cores))
    bc.norm[, -(1:3)] = round((bc.mat * stats::median(bc.cov)) %*% diag(1/bc.cov), 2)

    if (norm.stats.comp) {
      norm.stats = createEmptyDF(c("character", rep("integer", 2), rep("numeric", 3)),
        length(bins))
      colnames(norm.stats) = c("chr", "start", "end", "m", "sd", "nb.remove")
      norm.stats$chr = bc.df$chr
      norm.stats$start = bc.df$start
      norm.stats$end = bc.df$end
      norm.stats[, 4:6] = matrix(as.numeric(unlist(parallel::mclapply(1:nrow(bc.norm),
                  function(rr) {
                    msd = mean.sd.outlierR(as.numeric(bc.norm[rr, all.samples]))
                return(c(msd$m, msd$sd, msd$nb.remove))
            }, mc.cores = nb.cores))), nrow(bc.norm))
    } else {
        norm.stats = NULL
    }

    return(list(norm.stats = norm.stats, bc.norm = bc.norm))
}