kda2himmeli.drivers {Mergeomics} | R Documentation |
kda2himmeli.drivers
finds maximally top ndriv
key drivers
for each module with respect to the significance level of the drivers.
kda2himmeli.drivers(data, modules, ndriv)
data |
data frame including information of the modules (key driver list, p-values, node list, false discovery rates (fdr), and so on.) |
modules |
top scoring modules among KDA results |
ndriv |
maximum number of drivers that can be chosen for per module |
data |
top key drivers (maximally |
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.
## get the prepared and KDA applied dataset:(see kda.analyze for details) data(job_kda_analyze) ## set the relevant parameters: 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") job.kda$nodfile <- system.file("extdata","msea2kda.nodes.txt", package="Mergeomics") ## "0" means we do not consider edge weights while 1 is opposite. job.kda$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 ## Finish the KDA process job.kda <- kda.finish(job.kda) ## Select top key drivers from each module. ## First, take module names from kda results modules <- unique(job.kda$results$MODULE) ## Take top 2 KDs: drivers <- kda2himmeli.drivers(job.kda$results, modules, ndriv=2) ## remove the results folder unlink("Results", recursive = TRUE)