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Generate sub-network mapping from a list of candidate genes

geneConnector.Rd

This function generates sub-network mapping from a list of candidate genes

Usage

geneConnector(
  geneList,
  networkGraph,
  directed = FALSE,
  pValueAdj = "BH",
  pValueCutoff = 0.05,
  communityMethod = "ebc",
  keepIsolatedNodes = FALSE
)

Arguments

geneList

character vector containing a list of candidate genes

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)

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)

communityMethod

string for community detection method c("ebc","lec") as described in the details section (default = "ebc")

keepIsolatedNodes

A boolean value indicating whether to keep isolated nodes in the netboxr result (default = FALSE)

Value

a list of returned netboxr results

  • netboxGraph: igraph object of NetBox algorithm identified network nodes and connections

  • netboxCommunity: igraph object of network community assignment

  • netboxOutput: data frame of NetBox algorithm identified network nodes and connections

  • nodeType: data frame of node types ("candidate" or "linker") in the NetBox algorithm indentified network.

  • moduleMembership: data frame of module (community) membership.

  • neighborData: data frame of information of nodes directly connected to candidate gene nodes.

Details

P-value correction methods include the Bonferroni correction ("bonferroni") or Benjamini & Hochberg ("BH"). Community detection methods include using edge betweeness score ("ebc") or using leading eigenvector method ("lec)

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"    

plot(results$netboxGraph, layout = layout_with_fr)



write.table(results$netboxOutput,
  file = "network.sif", sep = "  ",
  quote = FALSE, col.names = FALSE, row.names = FALSE
)

write.table(results$neighborData,
  file = "neighborList.txt", sep = "  ",
  quote = FALSE, col.names = TRUE, row.names = FALSE
)

write.table(results$moduleMembership,
  file = "memb.ebc.txt", sep = "  ",
  quote = FALSE, col.names = FALSE, row.names = FALSE
)
#
write.table(results$nodeType,
  file = "nodeType.txt", sep = "  ", quote = FALSE,
  col.names = FALSE, row.names = FALSE
)
#