R/normalization.R

In const-ae/proDD: Identifying Differentially Abundant Proteins from Label-Free Mass Spectrometry Data

#' Column wise median normalization of the data matrix
#'
#' The method calculates for each sample the median change (i.e. the difference
#' between the observed value and the row average) and substracts it from
#' each row. Missing values are ignored in the procedure. The method is based
#' on the assumption that a majority of the rows did not change.
#'
#'
#' @param X a matrix of proteins and samples
#' @return the normalized matrix
#' @export
median_normalization <- function(X){
    stopifnot(length(dim(X)) == 2)
    Xnorm <- X
    for(idx in 1:ncol(X)){
        Xnorm[, idx] <- X[, idx, drop=FALSE] -
            median(X[, idx, drop=FALSE] - rowMeans(X, na.rm=TRUE), na.rm=TRUE )
    }
    Xnorm
}


setGeneric("median_normalization")

#' @describeIn median_normalization S4 method of \code{median_normalization} for
#'   \code{SummarizedExperiment}
setMethod("median_normalization",
          c(X = "SummarizedExperiment"),
          function(X){
              new_assay <- median_normalization(SummarizedExperiment::assay(X))
              SummarizedExperiment::assay(X) <- new_assay
              X
          })

#' @describeIn median_normalization S4 method of \code{median_normalization} for
#'   \code{MSnSet}
setMethod("median_normalization",
          c(X = "MSnSet"),
          function(X){
              new_assay <- median_normalization(Biobase::exprs(X))
              Biobase::exprs(X) <- new_assay
              X
          })