runBioNet {SMITE} | R Documentation |
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.
runBioNet(pvalue_annotation, network, alpha = 0.05)
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. |
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.
A PvalueAnnotation with a loaded module.
This is a wrapper function to run BioNet. The actual BioNet code was created by Beisser et al.
N. Ari Wijetunga
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.
plotModule extractModule runGOseq
## 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)