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

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

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", expressionSetGeneFormat=NULL, ...)

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.pvalue will be reported.
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.
expressionSetGeneFormat	a character string specifying the type of identifier for a gene in your expression data set. The default value is NULL. (ex. SYMBOL, ENTREZID, REFSEQ, ENSEMBL)
...	additional arguments.

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.

Value

A list object.

Author(s)

Jessica Mar

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", analysis="microarray", expressionSetGeneFormat=NULL) 

attract documentation built on Nov. 8, 2020, 8:04 p.m.