kda.prepare.screen: Prepare hubs and hubnets

Description

kda.prepare.screen finds hubs and their neighborhoods (hubnets) from the given graph.

Usage

kda.prepare.screen(graph, depth, direction, efactor, dmin, dmax)

Arguments

graph

entire graph, whose hubs and hubnets will be obtained

depth

search depth for subgraph search

direction

the direction of the interactions among graph components. 0 for undirected, negative for downstream, and positive for upstream

efactor

influence of node strengths (weights): 0.0 no influence, 1.0 full influence

dmin

minimum hub degree to include

dmax

maximum hub degree to include

Value

graph

Updated graph including obtained hubs and hubnets:

hubs: hub nodes list
hubnets: neighborhoods of hubs (hubnets)

References

Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B, Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems. bioRxiv doi: http://dx.doi.org/10.1101/036012

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$netfile<-"network.mouseliver.mouse.txt"
## Gene sets derived from ModuleMerge, containing two columns, MODULE, 
## NODE, delimited by tab 
job.kda$modfile<- "mergedModules.txt"
## 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

## Configure the parameters for KDA:
# job.kda <- kda.configure(job.kda)
## Create the object properly
# job.kda <- kda.start(job.kda)

## Find the hubs, co-hubs, and hub neighborhoods (hubnets) by kda.prepare()
## and its auxiliary functions kda.prepare.screen and kda.prepare.overlap
## First, determine the minimum and maximum hub degrees:
# nnodes <- length(job.kda$graph$nodes)
# if (job.kda$mindegree == "automatic") {
#   dmin <- as.numeric(quantile(job.kda$graph$stats$DEGREE,0.75))
#   job.kda$mindegree <- dmin
# }
# if (job.kda$maxdegree == "automatic") {
#   dmax <- as.numeric(quantile(job.kda$graph$stats$DEGREE,1))
#   job.kda$maxdegree <- dmax
# } 
## Collect neighbors.
# job.kda$graph <- kda.prepare.screen(job.kda$graph, job.kda$depth, 
# job.kda$direction, job.kda$edgefactor, job.kda$mindegree, job.kda$maxdegree)

## Then, extract overlapping co-hubs by kda.prepare.overlap()