tool.graph {Mergeomics} | R Documentation |
tool.graph
translates an edge list including TAIL, HEAD and WEIGHT
information into a graph representation-adapted data list. It also
provides in-degree and out-degree statistics for nodes.
tool.graph(edges)
edges |
a data frame with three columns TAIL, HEAD and WEIGHT |
a datalist including following components:
nodes |
N-element array of node names |
tails |
K-element array of node indices |
heads |
K-element array of node indices |
weights |
K-element array of edge weights |
tail2edge |
N-element list of adjacent edge indices |
head2edge |
N-element list of adjacent edge indices |
outstats |
N-row data frame of out-degree node statistics |
instats |
N-row data frame of in-degree node statistics |
stats |
N-row data frame of node statistics |
Ville-Petteri Makinen
job.kda <- list() job.kda$label<-"HDLC" ## parent folder for results job.kda$folder<-"Results" ## Input a network ## columns: TAIL HEAD WEIGHT job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt", package="Mergeomics") ## module file: job.kda$modfile<- system.file("extdata","mergedModules.txt", package="Mergeomics") ## "0" means we do not consider edge weights while 1 is opposite. job.kda$edgefactor<-0.0 ## The searching depth for the KDA job.kda$depth<-1 ## 0 means we do not consider the directions of the regulatory interactions ## while 1 is opposite. job.kda$direction <- 1 job.kda$nperm <- 20 # the default value is 2000, use 20 for unit tests ## kda.start() process takes long time while seeking hubs in the given net ## Here, we used a very small subset of the module list (1st 10 mods ## from the original module file): moddata <- tool.read(job.kda$modfile) mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)), 10)] moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),] ## save this to a temporary file and set its path as new job.kda$modfile: tool.save(moddata, "subsetof.supersets.txt") job.kda$modfile <- "subsetof.supersets.txt" job.kda <- kda.configure(job.kda) ## Import data for weighted key driver analysis: ## Import topology. edgdata <- kda.start.edges(job.kda) ## Create an indexed graph structure. job.kda$graph <- tool.graph(edgdata) ## Remove the temporary files used for the test: file.remove("subsetof.supersets.txt")