tool.graph: Convert an edge list to a graph representation

In Mergeomics: Integrative network analysis of omics data

Description

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.

Usage

tool.graph(edges)

Arguments

edges

a data frame with three columns TAIL, HEAD and WEIGHT

Value

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

Author(s)

Ville-Petteri Makinen

See Also

tool.subgraph

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.
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")

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.