ssea.prepare {Mergeomics} | R Documentation |
ssea.prepare
prepares a database that includes hierarchical for
modules, i.e. it collects gene list and unique marker list of the modules
for MSEA process
ssea.prepare(job)
job |
a data list with the following components: modules: module identities as characters. genesgene: identities as characters. loci: marker identities as characters. moddata: preprocessed module data (indexed identities). gendata: preprocessed mapping data (indexed identities). locdata: preprocessed marker data (indexed identities). mingenes: minimum module size allowed. maxgenes: maximum module size allowed. maxoverlap: maximum module overlap allowed (1.0 to skip). quantiles: quantile points for test statistic. |
ssea.prepare
removes extreme-sized modules, constructs a
hierarchical representation of genes and modules, obtains hit counts for
markers, and returns the finalized module, genes, markers, database
information.
job |
an updated data list with the following components: modules: finalized module names. moddata: finalized module data. gendata: finalized mapping data. locdata: finalized marker data. quantiles: verified quantile points. database$modulesizes: gene counts for modules. database$modulelengths: distinct markers counts for modules. database$moduledensities: ratio between distinct and non-distinct markers. database$genesizeslocus: count for each gene. database$module2genes: gene lists for each module. database$gene2locilocus: lists for each gene. database$locus2row: indices in the marker data frame for each marker. database$observed: matrix of observed counts of values that exceed each quantile point for each marker. database$expected: 1.0 - quantile points. |
Ville-Petteri Makinen
Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD, Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC genomics. 2016;17(1):874.
ssea.analyze
, ssea.control
,
ssea.finish
, ssea.start
,
ssea2kda
job.msea <- list() job.msea$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() and prepare for this small set: (due to the huge runtime) job.msea <- ssea.start(job.msea) job.msea <- ssea.prepare(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")