kda.analyze.simulate {Mergeomics} | R Documentation

## Weighted key driver analysis (wKDA) simulation

### Description

Generates simulations for permutation test, which is performed to obtain the p-value for the enrichment score of a given hub for a specified module during the wKDA process.

### Usage

    kda.analyze.simulate(o, g, nmemb, nnodes, nsim)

### Arguments

`o` | Observed enrichment score of a hub node assigned for a given module.
`g` | Sub-graph of a given hub and its neighbors (hubnet).
`nmemb` | Number of the members included in a given module.
`nnodes` | Number of the nodes in the whole graph (network) of the dataset.
`nsim` | Number of the iterations (simulations) performed for the permutation test.

### Details

`kda.analyze.simulate` performs permutation tests to obtain p-values for the enrichment score of a given hub node for a given module. It takes the observed enrichment score of the given hub, hubnet (subgraph of the hub and its neighbors), number of the members of the given module, total number of the nodes in the entire graph of the dataset, and number of the simulations for the permutation test. In each iteration (simulation), it samples `nmemb` nodes randomly among the entire nodes of the graph. Then, it tests the overlapped nodes among the randomly chosen nodes and the given node's neigborhood. At the end, it obtains an enrichment score for each simulation and evaluates these permuted enrichment scores with respect to the observed enrichment score of the hub. Among `nsim` random simulations; maximally, enrichment scores of 10 iterations are allowed to be greater than the observed (actual) enrichment score of the hub. If this limitation is exceeded, simulation will be finalized at that point and the enrichment score list of the iterations will be returned.

### Value

`x` | A list containing enrichment scores of the simulation's iterations

### 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.analyze.exec`, `kda.analyze.test`

### 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"

    ## Let's prepare KDA object for KDA:
    job.kda <- kda.configure(job.kda)
    job.kda <- kda.start(job.kda)
    job.kda <- kda.prepare(job.kda)
    set.seed(job.kda$seed)
    i = 1 ## index of the module, whose p-val is calculated:
    memb <- job.kda$module2nodes[[i]]
    graph <- job.kda$graph  ## we need to import a network
    nsim <- job.kda$nperm   ## number of simulations
    ## This auxiliary function is called by kda.analyze.exec(), which is called
    ## by kda.analyze() main function, see this main function for more details

    hubs <- graph$hubs
    hubnets <- graph$hubnets
    nhubs <- length(hubs)
    nnodes <- length(graph$nodes)
    nmemb <- length(memb)

    ## Observed enrichment scores.
    # obs <- rep(NA, nhubs)
    # k <- 1 ## actual using: for(k in 1:nhubs){}, for unit test, use the 1st hub
    # g <- hubnets[[hubs[k]]]
    # obs[k] <- kda.analyze.test(g$RANK, g$STRENG, memb, nnodes)

    ## Estimate P-values.
    # pvals <- rep(NA, nhubs)
    # for(k in which(obs > 0)) {
    # g <- hubnets[[hubs[k]]]
    ## First pass:
    # x <- kda.analyze.simulate(obs[k], g, nmemb, nnodes, 200)
    ## Then, use x to estimate preliminary and final P-values.
    ## See kda.analyze() for more detail

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

[Package Mergeomics version 1.18.0 Index]