tool.graph.degree: Find degrees of the nodes

In Mergeomics: Integrative network analysis of omics data

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")

Example output

Writing to file... 
Saved 1346 rows in 'subsetof.supersets.txt'.
[1] "subsetof.supersets.txt"

KDA Version:12.7.2015

Parameters:
  Search depth: 1
  Search direction: 1
  Maximum overlap: 0.33
  Minimum module size: 20
  Minimum degree: automatic
  Maximum degree: automatic
  Edge factor: 0
  Random seed: 1

Importing edges...
     TAIL               HEAD               WEIGHT 
 Length:140663      Length:140663      Min.   :1  
 Class :character   Class :character   1st Qu.:1  
 Mode  :character   Mode  :character   Median :1  
                                       Mean   :1  
                                       3rd Qu.:1  
                                       Max.   :1  
[1] TRUE

Mergeomics documentation built on Nov. 8, 2020, 6:58 p.m.