ssea.meta {Mergeomics} | R Documentation

## Merge multiple MSEA results into meta MSEA

### Description

`ssea.meta` merges MSEA results of modules, genes, and markers, constructs hierarchical representation of genes and modules, calculates meta P-values of the modules (based on z-scores), and save all statistics results.

### Usage

    ssea.meta(jobs, label, folder)

### Arguments

`jobs` | data list including information and statistics about genes, markers, and modules
`label` | label (unique identifier) for meta job
`folder` | parent folder for meta job

### Value

`meta` | data list including meta-analyzing results for the modules, which enables analyzing the multiple MSEA results for the modules.

### 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.

### Examples

    ## Create an object for multiple MSEAs:
    job.multiple.msea <- list()
    set.seed(1)
    for(i in 1:3){
    ## make 3 trials, each time pick 10 random modules among the first 20 modules
    mod.indices <- sample(20, 10)
    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 <- 30 ## 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)[mod.indices]
    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)
    job.msea <- ssea.prepare(job.msea)
    job.msea <- ssea.control(job.msea)
    job.msea <- ssea.analyze(job.msea)
    job.msea <- ssea.finish(job.msea)

    ## Remove the temporary files used for the test:
    file.remove("subsetof.coexpr.modules.txt")
    file.remove("subsetof.genfile.txt")
    file.remove("subsetof.marfile.txt")
    job.multiple.msea[[i]] <- job.msea
    }

    meta.results <- ssea.meta(job.multiple.msea, job.multiple.msea[[1]]$label,
    job.multiple.msea[[1]]$folder)

[Package Mergeomics version 1.12.0 Index]