kda.prepare.screen: Prepare hubs and hubnets

In zeynebkurtUCLA/Mergeomics: Integrative network analysis of omics data

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)

Author(s)

Ville-Petteri Makinen

References

Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD, Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC genomics. 2016;17(1):874.

See Also

kda.analyze, kda.prepare

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

zeynebkurtUCLA/Mergeomics documentation built on May 14, 2019, 1:59 a.m.