kda.start: Import data for weighted key driver analysis
In zeynebkurtUCLA/Mergeomics: Integrative network analysis of omics data

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

kda.start converts identities (such as module descriptions, module identifiers, and module nodes) to indices. It prepares graph topology and module information for wKDA process.

1	kda.start(job)

job

a data frame including fields for edges and nodes information of the graph (TAIL, HEAD, WEIGHT). It also involves path of input files including module descriptions and module-gene lists.

kda.start imports graph and relevant module descriptor input files, creates an indexed graph structure, and converts identities to indices from module descriptions and module-gene lists. Hence, it concludes with a graph structure and a module set involving member gene IDs for each module.

job

Updated data frame including indexed graph topology, modules, and nodes information:

graph:     indexed topology
modules:   module identities
modinfo:   module descriptions (indexed)
moddata:   module data (indexed)
module2nodes:  lists of node indices for each module
modulesizes:   module sizes

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.finish, kda.prepare, kda.start.edges, kda.start.identify, kda.start.modules

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"

job.kda <- kda.configure(job.kda)
## Import data for weighted key driver analysis:
job.kda <- kda.start(job.kda) 

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