tool.graph.degree: Find degrees of the nodes

Description

tool.graph.degree finds in-degree and out-degree statistics of the network by using edge lists of the nodes. It also obtains the strenghts of the degrees by using edge weights.

Usage

tool.graph.degree(node2edge, weights)

Arguments

node2edge

edge list of each node

weights

strengths of the edges

Value

res: a data list including degree and its strength for each node

Details

Degree of a node means number of the neighbors belonging to that node. Hence, out-degree statistics are applicable for tail nodes; while in-degree statistics are applicable for the heads.

Examples

Run this code

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.
edges <- kda.start.edges(job.kda)
## Create an indexed graph structure.
tails <- as.character(edges$TAIL)
heads <- as.character(edges$HEAD)
wdata <- as.double(edges$WEIGHT)

nedges <- length(tails)
# Create factorized representation.
labels <- as.character(c(tails, heads))
labels <- as.factor(labels)
labelsT <- as.integer(labels[1:nedges])
labelsH <- as.integer(labels[(nedges+1):(2*nedges)])
# Create edge lists.
nodnames <- levels(labels)
nnodes <- length(nodnames)
elistT <- tool.graph.list(labelsT, nnodes)
elistH <- tool.graph.list(labelsH, nnodes)
## Collect edge degree stats:
res <- list()
res$nodes <- as.character(nodnames)
res$outstats <- tool.graph.degree(elistT, wdata) ## out degrees
res$instats <- tool.graph.degree(elistH, wdata)  ## in degrees
res$stats <- (res$outstats + res$instats) 

## Remove the temporary files used for the test:
file.remove("subsetof.supersets.txt")