estimateRedundancy: Estimates the redundancy of enriched gene sets

In saeyslab/triwise: Gene expression changes in three biological conditions

Description

Wrapper around the 'mgsa' method

Usage

estimateRedundancy(scores, gsets, Gdiffexp)

Arguments

scores	Dataframe from 'r packagedocs::rd_link(testUnidirectionality())' containing the filtered enrichment scores for every gene set
gsets	Named list containing character vectors for each gene set
Gdiffexp	Character vector of differentially expressed genes

Value

Numeric vector containing for every gene set (in the same order the scores dataframe) its relevance compared with other gene sets, higher is better

Examples

Eoi = matrix(rnorm(1000*3, sd=0.5), 1000, 3, dimnames=list(1:1000, c(1,2,3)))
Eoi[1:100,1] = Eoi[1:100,1] + 4 # the first 100 genes are more upregulated in the first condition
barycoords = transformBarycentric(Eoi)
Gdiffexp = (1:1000)[barycoords$r > 1]

# a and b are redundant, but a is stronger enriched
gsets = list(a=1:50, b=c(1:50, 100:110), c=200:500)
scores = testUnidirectionality(barycoords, gsets, Gdiffexp=(1:1000)[barycoords$r > 1])
scores$redundancy = estimateRedundancy(scores, gsets, Gdiffexp)
scores[scores$gsetid == "a", "redundancy"] > scores[scores$gsetid == "b", "redundancy"]

saeyslab/triwise documentation built on May 29, 2019, 12:56 p.m.