calcFuncSynexprs {attract} | R Documentation

## 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", 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)

[Package attract version 1.42.0 Index]