tool.graph.degree {Mergeomics} | R Documentation

## 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

### 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.

### Value

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

### Author(s)

Ville-Petteri Makinen

### See Also

`tool.graph`

### Examples

    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")

[Package Mergeomics version 1.15.0 Index]