| globalNullModel {netboxr} | R Documentation |
Randomly select the same number of nodes in the largest component of netbox result as a new gene candidate list and repeat multiple times to produce a distribution of node size and edge numbers. This distribution will be used to produce global p-value of netbox result based on the node size or edge numbers of largest component in the final network result.
globalNullModel( netboxGraph, networkGraph, directed, iterations = 30, numOfGenes = NULL, pValueAdj = "BH", pValueCutoff = 0.05 )
netboxGraph |
A vector containing candidate gene list |
networkGraph |
An igraph graph object |
directed |
TRUE or FALSE |
iterations |
TRUE of FALSE |
numOfGenes |
A numeric value |
pValueAdj |
A string for p-value correction method c('BH, 'Bonferroni') |
pValueCutoff |
A numeric value c(0,1) |
a list with four lists (i.e. netboxOutput, nodeType, moduleMembership, neighborData)
Eric Minwei Liu, emliu.research@gmail.com
data(netbox2010)
sifNetwork<-netbox2010$network
graphReduced <- networkSimplify(sifNetwork,directed = FALSE)
geneList<-as.character(netbox2010$geneList)
results<-geneConnector(geneList=geneList,networkGraph=graphReduced,
pValueAdj='BH',pValueCutoff=0.05,
communityMethod='lec',keepIsolatedNodes=FALSE)
names(results)
# Suggested 100 iterations.
# Use 5 interations in the exampel to save running time.
# globalTest <- globalNullModel(netboxGraph=results$netboxGraph,
# networkGraph=graphReduced,
# iterations=5, numOfGenes = 274)