kda2himmeli.exec: Evaluate each module separately for visualization

Description

kda2himmeli.exec deals with the modules individually; takes a particular amount of top key drivers of the given module in company with the top key driver lists and colormap of all modules; traces module memberships and produces colormap, it finds the edge and node lists for the top key drivers and their neighborhood for a given module.

Usage

kda2himmeli.exec(job, valdata, drivers, modpool, palette)

Arguments

job

data list including entire graph, nodes, modules information

valdata

GWAS pvalues of top loci of the nodes - if this information is available, sizes of the nodes in the figure will be correlated with the p-value of the top loci of the nodes -

drivers

top key drivers of the specified module

modpool

unique key driver list for all modules

palette

assigned unique color map for all modules

Value

res: uniquely identified node and edge lists of the members belonging to the given module

References

Shu L, Zhao Y, Kurt Z, Byars S, Tukiainen T, Kettunen J, Ripatti S, Zhang B, Inouye M, Makinen VP, Yang X. Mergeomics: integration of diverse genomics resources to identify pathogenic perturbations to biological systems. bioRxiv doi: http://dx.doi.org/10.1101/036012

Examples

Run this code

## 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)

## Get valdata including marker pvals
valdata <- tool.read(job.kda$nodfile)
z <- as.double(valdata$VALUE)
z <- (z/quantile(z, 0.95) + rank(z)/length(z))
valdata$SIZE <- pmin(4.0, z)
## Select subset of genes.
valdata <- kda2himmeli.identify(valdata, "NODE", job.kda$graph$nodes)

## 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)
drivers <- as.data.frame(drivers)
colnames(drivers) <- c("MODULE" , "NODE")

mods <- unique(drivers$MODULE)
modnames <- job.kda$modules[mods] 
modnames[which(mods == 0)] <- "NON.MODULE"
palette <- kda2himmeli.colormap(length(mods)) 
palette[,which(mods == 0)] <- c(90,90,90)
drivers$MODNAMES <- modnames[match(drivers$MODULE, mods)]
drivers$NODNAMES <- job.kda$graph$nodes[drivers$NODE] 
for(i in 1:nrow(drivers))
drivers$COLOR[i] <- paste(palette[1, match(drivers$MODULE[i], mods)],
palette[2, match(drivers$MODULE[i], mods)],
palette[3, match(drivers$MODULE[i], mods)], collapse=" ")
## Process each module separately. Just perform for the 1st module:
i <- 1
rows <- which(drivers$MODULE == mods[i])
if(length(rows) > 0)
tmp <- kda2himmeli.exec(job.kda, valdata, drivers[rows,], mods, palette)

## remove the results folder
unlink("Results", recursive = TRUE)