findCorrPartners: Determines Genes with Highly Correlated Expression Profiles...

In jmarlab/attract: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape

Description

This function finds genes with expression profiles highly correlated to a synexpression group.

Usage

findCorrPartners(mySynExpressionSet, myEset, removeGenes = NULL, cor.cutoff = 0.85, ...)

Arguments

mySynExpressionSet	SynExpressionSet object.
myEset	ExpressionSet object.
removeGenes	vector of probes that specify those genes who demonstrate little variability across the different celltypes and thus should be removed from downstream analysis.
cor.cutoff	numeric value specifying the correlation cut-off.
...	additional arguments.

Details

Genes with highly correlated profiles to the synexpression groups (e.g. R > 0.85) are also likely to be integral in maintaining cell type-specific differences, however due to their lack of inclusion in resources like KEGG, would not have been picked up by the first GSEA step using findAttractors.

Value

A SynExpressionSet object which stores the genes that are highly correlated with the synexpression group provided, and their average expression profile.

Author(s)

Jessica Mar

Examples

data(subset.loring.eset)
attractor.states <- findAttractors(subset.loring.eset, "celltype", annotation="illuminaHumanv1.db")
remove.these.genes <- removeFlatGenes(subset.loring.eset, "celltype", contrasts=NULL, limma.cutoff=0.05)
mapk.syn <- findSynexprs("04010", attractor.states, remove.these.genes)
mapk.cor <- findCorrPartners(mapk.syn, subset.loring.eset, remove.these.genes)

jmarlab/attract documentation built on May 23, 2019, 9:02 p.m.