kda.prepare.screen {Mergeomics} | R Documentation

## Prepare hubs and hubnets

### Description

`kda.prepare.screen` finds hubs and their neighborhoods (hubnets) from the given graph.

### Usage

    kda.prepare.screen(graph, depth, direction, efactor, dmin, dmax)

### Arguments

`graph` | entire graph, whose hubs and hubnets will be obtained
`depth` | search depth for subgraph search
`direction` | the direction of the interactions among graph components. 0 for undirected, negative for downstream, and positive for upstream
`efactor` | influence of node strengths (weights): 0.0 no influence, 1.0 full influence
`dmin` | minimum hub degree to include
`dmax` | maximum hub degree to include

### Value

`graph` | Updated graph including obtained hubs and hubnets:
    hubs: hub nodes list
    hubnets: neighborhoods of hubs (hubnets)

### 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`

### 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$netfile<-"network.mouseliver.mouse.txt"
    ## Gene sets derived from ModuleMerge, containing two columns, MODULE,
    ## NODE, delimited by tab
    job.kda$modfile<- "mergedModules.txt"
    ## 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

    ## 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) by kda.prepare()
    ## and its auxiliary functions kda.prepare.screen and kda.prepare.overlap
    ## First, determine the minimum and maximum hub degrees:
    # nnodes <- length(job.kda$graph$nodes)
    # if (job.kda$mindegree == "automatic") {
    #   dmin <- as.numeric(quantile(job.kda$graph$stats$DEGREE,0.75))
    #   job.kda$mindegree <- dmin
    # }
    # if (job.kda$maxdegree == "automatic") {
    #   dmax <- as.numeric(quantile(job.kda$graph$stats$DEGREE,1))
    #   job.kda$maxdegree <- dmax
    # }
    ## Collect neighbors.
    # job.kda$graph <- kda.prepare.screen(job.kda$graph, job.kda$depth,
    # job.kda$direction, job.kda$edgefactor, job.kda$mindegree, job.kda$maxdegree)

    ## Then, extract overlapping co-hubs by kda.prepare.overlap()

[Package Mergeomics version 1.18.0 Index]