ssea2kda.analyze {Mergeomics} | R Documentation

## Apply second MSEA after merging the modules

### Description

`ssea2kda.analyze` performs a second MSEA for the updated modules after merging the highly overlapped modules (according to a specified overlapping ratio)

### Usage

    ssea2kda.analyze(job, moddata)

### Arguments

`job` | the data list including the information of modules, genes, and markers, and also involving the database that uses indexed identities for modules, genes, and markers `(job$database)`.
`moddata` | merged modules including MODULE, GENE, and OVERLAP information

### Details

`ssea2kda.analyze` constructs new gene lists for merged modules and updates module database including module sizes, lengths, densities (based on marker sizes), and gene list. Then, it runs a second MSEA and returns the enrichment scores of the updated module database.

### Value

`res` | data list including updated information (after merge) such as, enrichment scores of merged 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.

### See Also

`ssea2kda`

### 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)
    job.msea <- ssea.prepare(job.msea)
    job.msea <- ssea.control(job.msea)
    job.msea <- ssea.analyze(job.msea)
    job.msea <- ssea.finish(job.msea)

    ###############  Create intermediary datasets for KDA ##################
    syms <- tool.read(system.file("extdata", "symbols.txt",
    package="Mergeomics"))
    syms <- syms[,c("HUMAN", "MOUSE")]
    names(syms) <- c("FROM", "TO")
    ## Collect genes and top markers from original files.
    noddata <- ssea2kda.import(job.msea$genfile, job.msea$locfile)

    ## Select candidate modules (significant ones according to FDRs)
    res <- job.msea$results
    res <- res[order(res$P),]
    rows <- which(res$FDR < 0.25)
    res <- res[rows,]

    ## Collect member genes.
    moddata <- job.msea$moddata
    pos <- match(moddata$MODULE, res$MODULE)
    moddata <- moddata[which(pos > 0),]

    ## Restore original identities.
    modinfo <- job.msea$modinfo
    modinfo$MODULE <- job.msea$modules[modinfo$MODULE]
    moddata$MODULE <- job.msea$modules[moddata$MODULE]
    moddata$GENE <- job.msea$genes[moddata$GENE]

    ## Merge and trim overlapping modules.
    moddata$OVERLAP <- moddata$MODULE
    rmax <- 0.33
    moddata <- tool.coalesce(items=moddata$GENE, groups=moddata$MODULE,
    rcutoff=rmax)
    moddata$MODULE <- moddata$CLUSTER
    moddata$GENE <- moddata$ITEM
    moddata$OVERLAP <- moddata$GROUPS
    moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")]
    moddata <- unique(moddata)

    ## Calculate enrichment scores for merged modules.
    tmp <- unique(moddata[,c("MODULE","OVERLAP")])
    res <- ssea2kda.analyze(job.msea, moddata)

    ## 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 1.12.0 Index]