ssea.prepare.counts {Mergeomics} | R Documentation |
Counts unique loci in a module, maps the marker data of a module to the all available markers by creating a bit matrix for values above the given quantiles. Created bit matrix contains either TRUE (above quantiles) or FALSE (below or equals to quantiles) values as a resuls of these comparisons. It returns the results (marker mapping and bit matrix)
ssea.prepare.counts(locdata, nloci, quantiles)
locdata |
marker data |
nloci |
number of elements in markers list |
quantiles |
quantile points for test statistic |
res |
a data list with the following components: locus2row: mapped marker information observed: bit matrix that involves TRUEs and FALSEs |
Ville-Petteri Makinen
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.
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) ## Determine test cutoffs: if(is.null(job.msea$quantiles)) { lengths <- db$modulelengths mu <- median(lengths[which(lengths > 0)]) job.msea$quantiles <- seq(0.5, (1.0 - 1.0/mu), length.out=10) } ## Calculate hit counts: nloci <- length(job.msea$loci) hits <- ssea.prepare.counts(job.msea$locdata, nloci, job.msea$quantiles) db <- c(db, hits) ## Remove the temporary files used for the test: file.remove("subsetof.coexpr.modules.txt") file.remove("subsetof.genfile.txt") file.remove("subsetof.marfile.txt")