R/DawnNormalize.R

In MartinFXP/DawnRank: What the package does (short line)

#' DawnNormalize
#' 
#' DawnNormalize takes a tumor and normal expression matrix and returns a
#' standardized absolute differential expression matrix
#' 
#' 
#' @param tumorMat, a matrix representing the tumor expression
#' @param normalMat, a matrix representing the normal expression
#' @return the differential expression matrix, it is absolute standarized
#' matrix
#' @examples
#' 
#' ###using a small subset of the TCGA dataset and a small KEGG gene interaction network,
#' ###We will show how to get DawnRank Results
#' 
#' library(DawnRank)
#' 
#' #load the mutation data
#' data(brcaExampleMutation)
#' 
#' #load the tumor expression data
#' data(brcaExampleTumorExpression)
#' 
#' #load the normal expression data
#' data(brcaExampleNormalExpression)
#' 
#' #load the pathway data
#' data(brcaExamplePathway)
#' 
#' #load the gold standard
#' data(goldStandard)
#' 
#' #normalize the tumor and normal data to get the differential expression
#' normalizedDawn<-DawnNormalize(tumorMat=brcaExampleTumorExpression,normalMat=brcaExampleNormalExpression)
#' 
#' #get the DawnRank Score
#' dawnRankScore<-DawnRank(adjMatrix=brcaExamplePathway,mutationMatrix=brcaExampleMutation,expressionMatrix=normalizedDawn, mu=3,goldStandard=goldStandard)
#' 
#' #look at the DawnRank scores for a few patients
#' dawnRankFrame<-dawnRankScore[[3]]
#' head(dawnRankFrame)
#' 
#' #get the aggregate DawnRank scores
#' aggregateDawnRankScore<-condorcetRanking(scoreMatrix=dawnRankScore[[2]],mutationMatrix=brcaExampleMutation)
#' 
#' #look at top 10 ranked genes
#' top10<-aggregateDawnRankScore[[2]][1:10]
#' top10
#' 
#' #get the individual cutoff for patient TCGA-A2-A04P
#' dawnRankFrame$isCGC<-dawnRankFrame$isGoldStandard
#' library(maxstat)
#' patspeccutoff(patient="TCGA-A2-A04P",ms=dawnRankFrame,default=95)->significance
#' 
#' @export DawnNormalize
DawnNormalize <-
function(tumorMat, normalMat){
  indicies<-colnames(tumorMat)
  
  #use mean imputation to handle missing values
  tumorMat[is.na(tumorMat)]<-mean(tumorMat[!is.na(tumorMat)])
  normalMat[is.na(normalMat)]<-mean(normalMat[!is.na(normalMat)])
  
  #get gene expression for genes with a matched normal
  matchedNorm<-intersect(colnames(tumorMat),colnames(normalMat))
  matchedDiffExpression<-tumorMat[,matchedNorm]-normalMat[,matchedNorm]
  
  #get the gene expression for genes with no match normal
  generalExpression<-apply(normalMat,1,mean)
  unmatchedDiffExpression<-tumorMat-generalExpression
  
  #combine the remaining expressions
  combinedExpressionMatrix<-cbind(matchedDiffExpression,unmatchedDiffExpression)
  combinedExpressionMatrix<-combinedExpressionMatrix[,indicies]
  
  #scale and absolute the expressions
  scaledExpressionMatrix<-scale(combinedExpressionMatrix)
  return(abs(scaledExpressionMatrix))
}