ssea.prepare.structure {Mergeomics} | R Documentation

## Construct hierarchical representation of components

### Description

`ssea.prepare.structure` represents modules, genes, and markers in a hierarchical structure.

### Usage

    ssea.prepare.structure(moddata, gendata, nmods, ngens)

### Arguments

`moddata` | module data (indexed identities)
`gendata` | mapping data (indexed identities)
`nmods` | number of modules
`ngens` | number of all genes

### Details

`ssea.prepare.structure` finds member genes of modules and marker lists of genes; counts distinct markers within each module and obtains module's density from this count; at the end, it returns hierarchically structured results.

### Value

`res` | a data list with the following components:
    modulesizes: module size
    modulelengths: module length
    moduledensities: module densities
    genesizes: gene sizes of module
    module2genesgene: list of module
    gene2loci: markers lists of genes

### 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

`ssea.prepare`

### Examples

    job.msea <- list()
    job.msea$label <- "hdlc"
    job.msea$folder <- "Results"
    job.msea$genfile <- system.file("extdata",
    "genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
    job.msea$marfile <- system.file("extdata",
    "marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
    job.msea$modfile <- system.file("extdata",
    "modules.mousecoexpr.liver.human.txt", package="Mergeomics")
    job.msea$inffile <- system.file("extdata",
    "coexpr.info.txt", package="Mergeomics")
    job.msea$nperm <- 100 ## default value is 20000

    ## ssea.start() process takes long time while merging the genes sharing high
    ## amounts of markers (e.g. loci). it is performed with full module list in
    ## the vignettes. Here, we used a very subset of the module list (1st 10 mods
    ## from the original module file) and we collected the corresponding genes
    ## and markers belonging to these modules:
    moddata <- tool.read(job.msea$modfile)
    gendata <- tool.read(job.msea$genfile)
    mardata <- tool.read(job.msea$marfile)
    mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
    10)]
    moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
    gendata <- gendata[which(!is.na(match(gendata$GENE,
    unique(moddata$GENE)))),]
    mardata <- mardata[which(!is.na(match(mardata$MARKER,
    unique(gendata$MARKER)))),]

    ## save this to a temporary file and set its path as new job.msea$modfile:
    tool.save(moddata, "subsetof.coexpr.modules.txt")
    tool.save(gendata, "subsetof.genfile.txt")
    tool.save(mardata, "subsetof.marfile.txt")
    job.msea$modfile <- "subsetof.coexpr.modules.txt"
    job.msea$genfile <- "subsetof.genfile.txt"
    job.msea$marfile <- "subsetof.marfile.txt"
    ## run ssea.start() and prepare for this small set: (due to the huge runtime)
    job.msea <- ssea.start(job.msea)

    ## Remove extremely big or small modules:
    st <- tool.aggregate(job.msea$moddata$MODULE)
    mask <- which((st$lengths >= job.msea$mingenes) &
    (st$lengths <= job.msea$maxgenes))
    pos <- match(job.msea$moddata$MODULE, st$labels[mask])
    job.msea$moddata <- job.msea$moddata[which(pos > 0),]

    ## Construct hierarchical representation for modules, genes, and markers:
    ngens <- length(job.msea$genes)
    nmods <- length(job.msea$modules)
    db <- ssea.prepare.structure(job.msea$moddata, job.msea$gendata,
    nmods, ngens)

    ## Remove the temporary files used for the test:
    file.remove("subsetof.coexpr.modules.txt")
    file.remove("subsetof.genfile.txt")
    file.remove("subsetof.marfile.txt")

[Package Mergeomics version 1.18.0 Index]