R/SignatureDistance.R

In zhilongjia/GeneExpressionSignature: Gene Expression Signature based Similarity Metric

#' Compute pairwise distances comprehensively.
#' Compute pairwise distances comprehensively.
#' 
#' This function integrated the function for rank merging and distance scoring,
#' we can do the rank merging and distance scoring simply with it.
#' 
#' 
#' @param exprSet an ExpressionSet object, each column of assay data represents
#' a ranked list obtained by preprocessing the corresponding gene expression
#' profile, and phenotypic data represents the short description
#' (characteristics of gene expression profile, such as the drug type, the
#' disease state) about the assay data.
#' @param SignatureLength the length of "gene signature". In order to compute
#' pairwise distances among samples, genes lists are ranked according to the
#' gene expression ratio (fold change). And the "gene signature" includes the
#' most up-regulated genes (near the top of the list) and the most
#' down-regulated genes (near the bottom of the list).
#' @param MergingDistance distance to be used which "measures" the similarity
#' of ordered lists, Spearman or Kendall
#' @param ScoringMethod method to be used to perform distance scoring, GSEA or
#' PGSEA
#' @param ScoringDistance the distance measurements between PRLs: the Average
#' Enrichment Score Distance (avg), and the Maximum Enrichment Score Distance
#' (max).
#' @param weighted there are tow rank merging approaches for two cases: if
#' weighted=FALSE, all ranked list with the same biological state are treated
#' equally important, a simple but useful method average ranking technique is
#' selected; otherwise, weighted=TRUE, each individual ranked lists has its own
#' ranked weights, this takes the iterative rank-aggregating algorithm, default
#' is TRUE.
#' @param \dots additional arguments can be passed to the internal procedures
#' @seealso \code{\link{RankMerging}},\code{\link{ScoreGSEA}},
#' \code{\link{ScorePGSEA}}
#' @examples
#' 
#' #load the sample expressionSet
#' data(exampleSet)
#' 
#' #distance scoring
#' SignatureDistance(exampleSet,SignatureLength=250,MergingDistance="Spearman", 
#' ScoringMethod="GSEA",ScoringDistance="avg",weighted=TRUE)
#' 
#' 
#' @export SignatureDistance
SignatureDistance <-
function(exprSet,SignatureLength,MergingDistance=c("Spearman", "Kendall"),
         ScoringMethod=c("GSEA","PGSEA"),ScoringDistance=c("avg","max"),weighted=TRUE,...){
         MergingDistance=match.arg(MergingDistance, c("Spearman", "Kendall"))
         ScoringMethod=match.arg(ScoringMethod,c("GSEA","PGSEA"))
         ScoringDistance=match.arg(ScoringDistance,c("avg","max"))
         MergedRank=RankMerging(exprSet,MergingDistance,weighted)
         if (ScoringMethod=="GSEA")
             return(ScoreGSEA(MergedRank,SignatureLength,ScoringDistance,...))
         else
             return(ScorePGSEA(MergedRank,SignatureLength,ScoringDistance,...))
}