ssea.analyze.randloci {Mergeomics} | R Documentation |
ssea.analyze.randloci
simulates enrichment scores by
randomizing the marker that mapped to genes from all modules
(from database, db)
ssea.analyze.randloci(db, targets)
db |
database including the indexed identities for modules, genes and markers: modulesizes: gene counts for modules. modulelengths: distinct marker counts for modules. moduledensities: ratio between distinct and non-distinct markers. genesizes: marker count for each gene. module2genes: gene lists for each module. gene2loci: marker lists for each gene . locus2row: row indices in the marker data frame for each marker. observed: matrix of observed counts of values that exceed each quantile point for each marker. expected: 1.0 - quantile points. |
targets |
all modules |
scores |
randomly simulated enrichment scores |
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$marfile <- "subsetof.marfile.txt" ## run ssea.start() and prepare for this small set: (due to the huge runtime) job.msea <- ssea.start(job.msea) job.msea <- ssea.prepare(job.msea) job.msea <- ssea.control(job.msea) ## Observed enrichment scores. db <- job.msea$database scores <- ssea.analyze.observe(db) nmods <- length(scores) ## Simulated scores. nperm <- job.msea$nperm observ <- scores ## Include only non-empty modules for simulation. nmods <- length(db$modulesizes) targets <- which(db$modulesizes > 0) hits <- rep(NA, nmods) hits[targets] <- 0 ## Prepare data structures to hold null samples. keys <- rep(0, nperm) scores <- rep(NA, nperm) scoresets <- list() for(i in 1:nmods) scoresets[[i]] <- double() ## Simulate random scores. ## within a for loop: check capacity, find new statistics, update snull ## distribution (simulated null distr.) by permuting loci snull <- ssea.analyze.randloci(db, targets) ## Remove the temporary files used for the test: file.remove("subsetof.coexpr.modules.txt") file.remove("subsetof.genfile.txt") file.remove("subsetof.marfile.txt")