ssea.meta {Mergeomics} | R Documentation |
ssea.meta
merges MSEA results of modules, genes, and markers,
constructs hierarchical representation of genes and modules, calculates
meta P-values of the modules (based on z-scores), and save all statistics
results.
ssea.meta(jobs, label, folder)
jobs |
data list including information and statistics about genes, markers, and modules |
label |
label (unique identifier) for meta job |
folder |
parent folder for meta job |
meta |
data list including meta-analyzing results for the modules, which enables analyzing the multiple MSEA results for the modules. |
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.
## Create an object for multiple MSEAs: job.multiple.msea <- list() set.seed(1) for(i in 1:3){ ## make 3 trials, each time pick 10 random modules among the first 20 modules mod.indices <- sample(20, 10) 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$nperm <- 30 ## default value is 20000 ## ssea.start() process takes long time while merging the genes sharing high ## amounts of markers (e.g. loci). it is performed with full module list in ## the vignettes. Here, we used a very subset of the module list (1st 10 mods ## from the original module file) and we collected the corresponding genes ## and markers belonging to these modules: moddata <- tool.read(job.msea$modfile) gendata <- tool.read(job.msea$genfile) mardata <- tool.read(job.msea$marfile) mod.names <- unique(moddata$MODULE)[mod.indices] moddata <- moddata[which(!is.na(match(moddata$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) ## Remove the temporary files used for the test: file.remove("subsetof.coexpr.modules.txt") file.remove("subsetof.genfile.txt") file.remove("subsetof.marfile.txt") job.multiple.msea[[i]] <- job.msea } meta.results <- ssea.meta(job.multiple.msea, job.multiple.msea[[1]]$label, job.multiple.msea[[1]]$folder)