SMITE (version 1.0.2)

runBioNet: Perform BioNet Analysis on a PvalueAnnotation

Description

With BioNet, a researcher can find a single interconnected gene module using the highest scoring genes generated in a PvalueAnnotation. This function will load the module into the PvalueAnnotation for visualization and downstream analysis.

Usage

runBioNet(pvalue_annotation, network, alpha = 0.05)

Arguments

pvalue_annotation

An S4 object of class PvalueAnnotation that has already had scores generated.

network

An interaction network of class graphNEL or igraph.

alpha

A numeric specfiying a cutoff for high scoring genes to be return with the highScores function.

Value

A PvalueAnnotation with a loaded module.

Details

The input of p-values to BioNet discussed in the BioNet vignette involves first modeling p-values as a Beta-uniform mixture model to obtain the actual corresponding probability function values. Since our scoring method produces p-values/scores that are uniform in distribution, we input them directly into the BioNet algorithm. For more details on BioNet see the reference or runFastHeinz in the BioNet package.

References

Beisser et al. BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics. 2010 Apr 15;26(8):1129-30. doi: 10.1093/bioinformatics/btq089. Epub 2010 Feb 25.

See Also

plotModule extractModule runGOseq

Examples

## load test data ##
data(test_annotation_score_data)

 
## NOTE: commented out for example.  See vignette for better explanation ##

#load reactome network with gene symbols ##
#load(system.file("data","Reactome.Symbol.Igraph.rda", package="SMITE"))

## run BioNet ##
#test_annotation<-runBioNet(pvalue_annotation=test_annotation,
#network = REACTOME)

## view module ##
#extractModules(pvalue_annotation=test_annotation, 1)

## plot module ##
#plotModule(pvalue_annotation=test_annotation, which.network=1)