inst/utils/createGenomeScaleModel.R

In PriceLab/trena: Fit transcriptional regulatory networks using gene expression, priors, machine learning

# Grab ensemble score table for all genes in a gene list
#----------------------------------------------------------------------------------------------------
createGenomeScaleModel <- function(mtx.assay, gene.list, genome.db.uri, project.db.uri,
                                   size.upstream=1000, size.downstream=1000, num.cores = NULL,
                                   extraArgs = list()){

    footprint.filter <- FootprintFilter(mtx.assay = mtx.assay)
    trena <- TReNA(mtx.assay, solver = "ensemble")

    # Setup the parallel structure with a default of half the cores
    if(is.null(num.cores)){
        num.cores <- detectCores()/2}  
    cl <- makeForkCluster(nnodes = num.cores)
    registerDoParallel(cl)
    
    full.result.list <- foreach(i = 1:length(gene.list), .packages='TReNA') %dopar% {

        # Designate the target gene and grab the tfs
        target.gene <- gene.list[[i]]        
        out.list <- try(getCandidates(footprint.filter,extraArgs = list(
                                                  "target.gene" = target.gene,
                                                  "genome.db.uri" = genome.db.uri,
                                                  "project.db.uri" = project.db.uri,
                                                  "size.upstream" = size.upstream,                                          
                                                  "size.downstream" = size.downstream)),
                        silent = TRUE)

        # Solve the trena problem using the supplied values and the ensemble solver

        if(!(class(out.list) == "try-error")){
            if(length(out.list$tfs) > 0){
                
                solve(trena, target.gene, out.list$tfs, extraArgs = extraArgs)}
            
            else{NULL}

            
        }
        else{NULL}
}
    # Stop the cluster
    stopCluster(cl)
    
    # Name the list after the genes supplied
    names(full.result.list) <- gene.list
    return(full.result.list)

} # createGenomeScaleModel
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