globalNullModel {netboxr} | R Documentation |
Randomly select the same number of nodes in the largest connected 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 |
igraph network graph object. This igraph object contains
NetBox algorithm identified network from |
networkGraph |
igraph network graph object. This igraph object contains curated network information |
directed |
boolean value indicating whether the input network is directed or undirected (default = FALSE) |
iterations |
numeric value for number of iterations |
numOfGenes |
numeric value for number of genes mapped in the initial network |
pValueAdj |
string for p-value correction method c("BH", "Bonferroni") as described in the details section (default = "BH") |
pValueCutoff |
numeric value of p-value cutoff for linker nodes (default = 0.05) |
P-value correction methods include the Bonferroni correction ("bonferroni") or Benjamini & Hochberg ("BH").
a list of returned results
globalNull: data frame of global randomization results
globalNodesResult: data frame of global null tested results based on nodes
globalEdgesResult: data frame of global null tested results based on edges
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)