ScoreGSEA: Compute pairwise distances between samples with method in package GSEA

Description

Compute pairwise distances between sample according to their (Prototype Ranked List) PRL, a N x N distance matrix is generated by calling this function, N is the length of PRL.

Usage

ScoreGSEA(MergingSet, SignatureLength, ScoringDistance = c("avg", "max"), p.value = F)

Arguments

MergingSet

an ExpressionSet object. The assay data represents the PRLs of the samples, each column represents one PRL. The number of sample of this argument must be greater than 1, otherwise, this function is not meaningful.

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).

ScoringDistance

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

p.value

logical, if TRUE return a matrix of p.values of the distance matrix, default FALSE

Value

an distance-matrix, the max distance is more sensitive to weak similarities, providing a lower precision but a larger recall.If p.value is set to TRUE, then a list is returned that consists of the distance matrix as well as their p.values, otherwise, without p.vlues in the result.

Details

Once the PRL obtained for each sample, the distances between samples are calculated base on gene signature, including the expression of genes that seemed to consistently vary in response to the across different experimental conditions (e.g., different cell lines and different dosages). We take two distance measurements between PRLs: the Average Enrichment-Score Distance Davg=(TESx,y+TESy,x)/2, and the Maximum Enrichment-Score Distance Dmax=Min(TESx,y,TESy,x)/2.The avg is more stringent than max, where max is more sensitive to weak similarities, with lower precision but large recall.

Examples

Run this code

# load the sample expressionSet
data(exampleSet)

# Merging each group of the ranked lists in the exampleSet with the same phenotypic data into a single PRL
MergingSet=RankMerging(exampleSet,"Spearman")

# get the distance matrix
ds=ScoreGSEA(MergingSet,250,"avg")