Compute pairwise distances comprehensively.

Description

This function integrated the function for rank merging and distance scoring, we can do the rank merging and distance scoring simply with it.

Usage

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SignatureDistance(exprSet, SignatureLength, MergingDistance = c("Spearman", "Kendall"), ScoringMethod = c("GSEA", "PGSEA"), ScoringDistance = c("avg", "max"), weighted = TRUE, ...)

Arguments

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.

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).

MergingDistance

distance to be used which "measures" the similarity of ordered lists, Spearman or Kendall

ScoringMethod

method to be used to perform distance scoring, GSEA or PGSEA

ScoringDistance

the distance measurements between PRLs: the Average Enrichment Score Distance (avg), and the Maximum Enrichment Score Distance (max).

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.

...

additional arguments can be passed to the internal procedures

See Also

RankMerging,ScoreGSEA, ScorePGSEA

Examples

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#load the sample expressionSet
data(exampleSet)

#distance scoring
SignatureDistance(exampleSet,SignatureLength=250,MergingDistance="Spearman", ScoringMethod="GSEA",ScoringDistance="avg",weighted=TRUE)