R/findVariableFeatures.R

In msImpute: Imputation of label-free mass spectrometry peptides

#' Find highly variable peptides
#'
#' For each peptide, the total variance is decomposed into biological and technical variance using package \code{scran}
#' @param y numeric matrix giving log-intensity. Can contain NA values. Peptides with insufficient observations will be ignored.
#'
#' @return A data frame where rows are peptides and columns contain estimates of biological and technical variances. Peptides are ordered by biological variance.
#'
#' @details A loess trend is fitted to total sample variances and mean intensities. For each peptide, the biological variance is then
#' computed by subtracting the estimated technical variance from the loess fit from the total sample variance.
#'
#' @examples
#' data(pxd007959)
#' findVariableFeatures(data.matrix(log2(pxd007959$y)))
#'
#' @seealso computeStructuralMetrics
#'
#' @export
#' @importFrom scran modelGeneVar
#' @importFrom graphics lines plot
findVariableFeatures <- function(y){
  if(any(colMeans(is.na(y)) > 0.90)){message("Samples with more than 90% missing values detected")}
  results <- modelGeneVar(y)
  plot(results$mean, results$total)
  o <- order(results$mean)
  lines(results$mean[o], results$tech[o], col="red", lwd=2)
  results <- as.data.frame(results)
  top.dec <- results[order(results$bio, decreasing=TRUE), ]
  return(top.dec)

}