attract (version 1.24.0)

calcFuncSynexprs: Functional enrichmental analysis for a set of synexpression groups.

Description

This function performs functional enrichment for a given set of synexpression groups.

Usage

calcFuncSynexprs(mySynExpressionSet, myAttractorModuleSet, ontology = "BP", min.pvalue = 0.05, min.pwaysize = 5, annotation = "illuminaHumanv2.db", analysis="microarray", ...)

Arguments

mySynExpressionSet

SynExpressionSet object.

myAttractorModuleSet

AttractorModuleSet object.

ontology

character string specifying which GO ontology to use, either "MF", "BP", or "CC"; defaults to "BP".

min.pvalue

numeric value specifying adjusted P-value cut-off to use, categories with P-values

min.pwaysize

integer specifying minimum size of the pathway or category to consider for enrichment analysis.

annotation

character string specifying the annotation package that corresponds to the chip platform the data was generated from.

analysis

a character string specifying what type of experiment you performed, microarray or RNAseq.

...

additional arguments.

Value

A list object.

Details

This function performs a functional enrichment analysis on each synexpression group using the hyperGTest from the GOstats package. P-values are adjusted using the Benjamini-Hochberg correction method. Results are returned only if they satisfy the minimum P-value level, as specified by the min.pvalue argument.

References

Falcon, S. and R. Gentleman, Using GOstats to test gene lists for GO term association. Bioinformatics, 2007. 23(2): p. 257-8.

Examples

data(subset.loring.eset)
attractor.states <- findAttractors(subset.loring.eset, "celltype", nperm=10, annotation="illuminaHumanv1.db",analysis="microarray")
remove.these.genes <- removeFlatGenes(subset.loring.eset, "celltype", contrasts=NULL, limma.cutoff=0.05)
mapk.syn <- findSynexprs("04010", attractor.states, remove.these.genes)
mapk.func <- calcFuncSynexprs(mapk.syn, attractor.states, "CC", annotation="illuminaHumanv1.db")