runBioNet: Perform BioNet Analysis on a PvalueAnnotation

In GreallyLab/SMITE: Significance-based Modules Integrating the Transcriptome and Epigenome

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.

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.

Value

A PvalueAnnotation with a loaded module.

Note

This is a wrapper function to run BioNet. The actual BioNet code was created by Beisser et al.

Author(s)

N. Ari Wijetunga

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)

GreallyLab/SMITE documentation built on May 6, 2019, 6:30 p.m.