Skip to contents

Generate global null model p-value

globalNullModel.Rd

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.

Usage

globalNullModel(
  netboxGraph,
  networkGraph,
  directed,
  iterations = 30,
  numOfGenes = NULL,
  pValueAdj = "BH",
  pValueCutoff = 0.05
)

Arguments

netboxGraph

igraph network graph object. This igraph object contains NetBox algorithm identified network from geneConnector function

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)

Value

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

Details

P-value correction methods include the Bonferroni correction ("bonferroni") or Benjamini & Hochberg ("BH").

Author

Eric Minwei Liu, emliu.research@gmail.com

Examples

data(netbox2010)

sifNetwork<-netbox2010$network
graphReduced <- networkSimplify(sifNetwork,directed = FALSE) 
#> Loading network of 9264 nodes and 157780 interactions
#> Treated as undirected network 
#> Removing multiple interactions and loops
#> Returning network of 9264 nodes and 68111 interactions

geneList<-as.character(netbox2010$geneList)

results<-geneConnector(geneList=geneList,networkGraph=graphReduced,
                      pValueAdj='BH',pValueCutoff=0.05,
                      communityMethod='lec',keepIsolatedNodes=FALSE)
#> 274 / 517 candidate nodes match the name in the network of 9264 
#>                 nodes 
#> Only test neighbor nodes with local degree equals or exceeds 2
#> Multiple hypothesis corrections for 892 neighbor nodes in the network
#> For p-value 0.05 cut-off, 6 nodes were included as linker nodes
#> Connecting 274 candidate nodes and 6 linker nodes
#> Remove 208 isolated candidate nodes from the input
#> Final network contains 72 nodes and 152 interactions
#> Detecting modules using "leading eigenvector" method

names(results)
#> [1] "netboxGraph"      "netboxCommunity"  "netboxOutput"     "nodeType"        
#> [5] "moduleMembership" "neighborData"    

# Suggested 100 iterations. 
# Use 5 interations in the exampel to save running time.
# globalTest <- globalNullModel(netboxGraph=results$netboxGraph, 
#                              networkGraph=graphReduced, 
#                              iterations=5, numOfGenes = 274)