kda2himmeli {Mergeomics} | R Documentation |
kda2himmeli
generates input files for Himmeli to visualize the
graph and hubnets after the wKDA process finished. The network visualization
is a streamlined depiction of the module enrichment in hub neighborhoods.
kda2himmeli(job, modules = NULL, ndrivers = 5)
job |
KDA result data list as returned by |
modules |
array of module names to be visualized |
ndrivers |
maximum number of drivers per module |
kda2himmeli
first, selects top scoring key drivers for each
module; then, assigns a colormap to modules, processes each module
separately, finds key nodes' neighborhoods, and saves the edge and node
lists of the modules to the specified output folder. Besides, it returns
this configuration data to the user.
job |
updated data list including the node and edge information of the modules converted to Himmeli format |
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$netfile<-system.file("extdata","network.mouseliver.mouse.txt", package="Mergeomics") job.kda$nodfile <- system.file("extdata","msea2kda.nodes.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 ## finish the KDA process job.kda <- kda.finish(job.kda) ## prepare the cytoscape-ready files: job.kda <- kda2himmeli(job.kda) ## remove the results folder unlink("Results", recursive = TRUE)