kda.prepare {Mergeomics} | R Documentation

## Prepare graph topology for weighted key driver analysis

### Description

`kda.prepare` gets graph topology required by wKDA process, then provides the information including hub list, hubnets, and overlapping co-hubs.

### Usage

    kda.prepare(job)

### Arguments

`job` | a parameter including restirictions while determining the graph topology information (such as hubs, hubnets, co-hubs, etc.), which is required by the wKDA process:
    graph: graph of the dataset
    depth: search depth for subgraph search
    direction: use 0 for undirected, negative for downstream
    and positive for upstream
    maxoverlap: maximum allowed overlap between two key driver
    neighborhoods
    mindegree: minimum hub degree to include
    edgefactor: influence of node strengths; 0.0 no influence,
    1.0 full influence

### Details

`kda.prepare` determines minimum hub degree if it is not specified by the user, finds hubs and their neighborhoods (hubnets), extracts overlapping co-hubs, returns this information to user, and prints it to the screen.

### Value

`job` | Updated data frame including information about the graph topology in terms of hubs, hubnets, and overlapping co-hubs:
    hubs: hub nodes list
    hubnets: neighborhoods of hubs (hubnets)
    cohubsets: overlapping hubs (co-hubs)

### 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

`kda.analyze`, `kda.prepare.overlap`, `kda.prepare.screen`

### Examples

    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"

    ## Configure the parameters for KDA:
    job.kda <- kda.configure(job.kda)
    ## Create the object properly
    job.kda <- kda.start(job.kda)
    ## Find the hubs, co-hubs, and hub neighborhoods (hubnets), etc.:
    job.kda <- kda.prepare(job.kda)
    ## After that, we need to call kda.analyze() and kda.finish()

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

[Package Mergeomics version 1.10.0 Index]