kda2himmeli.drivers {Mergeomics} | R Documentation

## Select top key drivers for each module

### Description

`kda2himmeli.drivers` finds maximally top `ndriv` key drivers for each module with respect to the significance level of the drivers.

### Usage

    kda2himmeli.drivers(data, modules, ndriv)

### Arguments

`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

### Value

`data` | top key drivers (maximally `ndriv` drivers for each module) for top modules (if module significance levels are given)

### 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

`kda2himmeli`

### Examples

    ## 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)

[Package Mergeomics version 1.18.0 Index]