findCorrPartners {attract} | R Documentation

## Determines Genes with Highly Correlated Expression Profiles to a Synexpression Group

### 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)

[Package attract version 1.42.0 Index]