ssea2kda {Mergeomics} | R Documentation

## Generate inputs for wKDA

### Description

`ssea2kda` forwards MSEA results to weighted key driver analysis (wKDA) from the first MSEA results, merges the overlapped modules according to a given overlapping ratio to obtain a relatively independent module set, apply a second MSEA on the merged modules (supersets), updates and saves the second MSEA results properly for wKDA process.

### Usage

    ssea2kda(job, symbols = NULL, rmax = NULL, min.module.count=NULL)

### Arguments

`job` | data list including the organized results of MSEA process. It has following components:
    results: updated information of modules including:
    number of distinct member genes (NGENES),
    number of distinct member markers (NLOCI),
    ratio of distinct to non-distinct markers (DENSITY),
    false discovery rates (FDR).
    generesults: updated gene-specific information including:
    indexed gene identity (GENE),
    gene size (NLOCI),
    unadjusted enrichment score (SCORE),
    marker with max value (LOCUS),
    marker value (VALUE).
`symbols` | dataframe for translating gene symbols
`rmax` | maximum allowed overlap ratio between gene sets
`min.module.count` | minimum number of the pathways to be taken from the MSEA results to merge. Default value is NULL. If it is not specified, all the pathways having MSEA-FDR value less than 0.25 will be considered for merging if they are overlapping with the given ratio `rmax`.

### Details

`ssea2kda` gets genes and top markers from input files, selects significant modules with respect to ordered p-values, gets identities of modules and genes, merges and trims the overlapping modules (either having FDR less than 0.25 or top `min.module.count` modules when ranked up to the P-values), obtains enrichment scores for merged modules, translates the gene symbols (between species) if needed, and finally saves the module, gene, node, and marker information into relevant output files.

### Value

`plan` | an updated data list with the following components:
    label: unique identifier for the analysis.
    parent: parent folder for results.
    modfile: path of module file (columns: MODULE NODE).
    inffile: path of module information file
    (columns: MODULE DESCR).
    nodfile: path of node selection file (columns: NODE).

### 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.analyze`, `kda.analyze`

### 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")
    job.ssea2kda <- ssea2kda(job.msea, symbols=syms)

    ## 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]