ssea.start {Mergeomics} | R Documentation |
Creates identities (for modules, member genes, and loci) to start MSEA process.
ssea.start(plan)
plan |
a data list with the following components: label: unique identifier for the analysis folder: output folder for results modfile: path to module file (cols: MODULE GENE) marfile: path to marker file (cols: MARKER VALUE) genfile: path to gene file (cols: GENE LOCUS) inffile: path to module info file (cols: MODULE DESCR) seed: seed for random number generator permtype: gene for gene-level, locus for marker-level nperm: max number of random permutations mingenes: min number of genes per module (after merging) maxgenes: max number of genes per module quantiles: cutoffs for test statistic maxoverlap: max overlap allowed between genes |
ssea.start
imports modules, genes-locus mapping, and locus values;
removes the genes with no locus values from the list, find identities for
modules, genes, loci components, and excludes missing data and factorize
identities for these components.
job |
a data list with the following components: modules: module identities as characters. genes: gene identities as characters. loci: marker identities as characters. moddata: preprocessed module data (indexed identities) modinfo: description of the modules. gendata: preprocessed mapping data between genes and markers (indexed identities). locdata: preprocessed marker data (indexed identities) geneclusters: genes with shared markers. |
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.
ssea.analyze
, ssea.control
,
ssea.finish
, ssea.prepare
,
ssea2kda
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() for this small set:(due to the huge runtime we did not use ## full sets of modules, genes, and markers) job.msea <- ssea.start(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")