kda.prepare: Prepare graph topology for weighted key driver analysis
In Mergeomics: Integrative network analysis of omics data

Description Usage Arguments Details Value Author(s) References See Also Examples

kda.prepare gets graph topology required by wKDA process, then provides the information including hub list, hubnets, and overlapping co-hubs.

1	kda.prepare(job)

job

a parameter including restirictions while determining the graph topology information (such as hubs, hubnets, co-hubs, etc.), which is required by the wKDA process:

graph: graph of the dataset
depth: search depth for subgraph search
direction: use 0 for undirected, negative for downstream 
and positive for upstream
maxoverlap: maximum allowed overlap between two key driver
neighborhoods
mindegree: minimum hub degree to include
edgefactor: influence of node strengths; 0.0 no influence,
1.0 full influence

kda.prepare determines minimum hub degree if it is not specified by the user, finds hubs and their neighborhoods (hubnets), extracts overlapping co-hubs, returns this information to user, and prints it to the screen.

job

Updated data frame including information about the graph topology in terms of hubs, hubnets, and overlapping co-hubs:

hubs: hub nodes list
hubnets: neighborhoods of hubs (hubnets)
cohubsets: overlapping hubs (co-hubs)

Ville-Petteri Makinen

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.

kda.analyze, kda.prepare.overlap, kda.prepare.screen

job.kda <- list()
job.kda$label<-"HDLC"
## parent folder for results
job.kda$folder<- "Results"
## Input a network
## columns: TAIL HEAD WEIGHT
job.kda$netfile<-system.file("extdata","network.mouseliver.mouse.txt", 
package="Mergeomics")
## Gene sets derived from ModuleMerge, containing two columns, MODULE, 
## NODE, delimited by tab 
job.kda$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"

## 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), etc.:
job.kda <- kda.prepare(job.kda)
## After that, we need to call kda.analyze() and kda.finish()

## Remove the temporary files used for the test:
file.remove("subsetof.supersets.txt")
## remove the results folder
unlink("Results", recursive = TRUE)