attract (version 1.24.0)

findSynexprs: This function finds the synexpression groups present within a core attractor pathway module.

Description

This function takes the modules that were inferred from the GSEA step using (findAttractors) and finds a set of transcriptionally coherent set of genes associated with a particular core attractor pathway, i.e. the synexpression groups.

Usage

findSynexprs(myIDs, myDataSet, cellTypeTag, removeGenes = NULL, min.clustersize = 5, ...)

Arguments

myIDs

either a single character string or vector of character strings denoting the KEGG or reactome IDs of the pathway modules to be analyzed. It may also be a character codevector of gene names of a pathway if defining a custom pathway.

myDataSet

AttractorModuleSet object, output of the findAttractors step. This could also be an ExpressionSet object if using a custom pathway.

cellTypeTag

character string of the variable name which stores the cell-lineages or experimental groups of interest for the samples in the data set (this string should be one of the column names of pData(myEset)).

removeGenes

vector of gene names that specify those genes who demonstrate little variability across the different celltypes and thus should be removed from downstream analysis.

min.clustersize

integer specifying the minimum number of genes that must be present in clusters that are inferred.

...

additional arguments.

Value

If a single KEGG or reactome ID is specified in pwayIds, then a SynExpressionSet object is returned. If a multiple KEGG or reactome IDs are specified, then an environment object is returned where the keys are labeled "pwayIDsynexprs" (e.g. for MAPK KEGGID = 04010, the key is pway04010synexprs). The value associated with each key is a SynExpressionSet object.

Details

This function performs a hierarichical cluster analysis of the genes in a core attractor pathway module, and uses an informativeness metric to determine the number of optimal clusters (syenxpression groups) that describe the data.

References

Mar, J., C. Wells, and J. Quackenbush, Identifying the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape. to appear, 2010.

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, "celltype", remove.these.genes)
top5.syn <- findSynexprs(attractor.states@rankedPathways[1:5,1], attractor.states, "celltype", removeGenes=remove.these.genes)
vec.geneid <- c("GI_17999531-S","GI_17978503-A")
custom.syn <- findSynexprs(vec.geneid, subset.loring.eset, "celltype", removeGenes=remove.these.genes)