## ----setup, echo=FALSE-------------------------------------------------------- suppressPackageStartupMessages({ library(GSEABenchmarkeR) library(EnrichmentBrowser) }) ## ----lib---------------------------------------------------------------------- library(GSEABenchmarkeR) ## ----maComp------------------------------------------------------------------- geo2kegg <- loadEData("geo2kegg") names(geo2kegg) ## ----getDatasetProbe---------------------------------------------------------- geo2kegg[[1]] ## ----maPreproc---------------------------------------------------------------- geo2kegg <- maPreproc(geo2kegg[1:5]) ## ----getDatasetGene----------------------------------------------------------- geo2kegg[[1]] ## ----maGroups----------------------------------------------------------------- se <- geo2kegg[[1]] table(se$GROUP) ## ----rseqComp----------------------------------------------------------------- tcga <- loadEData("tcga", nr.datasets=2) names(tcga) ## ----brca--------------------------------------------------------------------- brca <- tcga[[2]] brca table(brca$GROUP) ## ----userComp----------------------------------------------------------------- data.dir <- system.file("extdata", package="GSEABenchmarkeR") edat.dir <- file.path(data.dir, "myEData") edat <- loadEData(edat.dir) names(edat) edat[[1]] ## ----deAna-------------------------------------------------------------------- geo2kegg <- runDE(geo2kegg, de.method="limma", padj.method="flexible") rowData(geo2kegg[[1]], use.names=TRUE) ## ----getGS-------------------------------------------------------------------- library(EnrichmentBrowser) kegg.gs <- getGenesets(org="hsa", db="kegg") ## ----runEA-------------------------------------------------------------------- kegg.ora.res <- runEA(geo2kegg[[1]], method="ora", gs=kegg.gs, perm=0) kegg.ora.res ## ----eaAll-------------------------------------------------------------------- res.dir <- tempdir() res <- runEA(geo2kegg, methods=c("ora", "camera"), gs=kegg.gs, perm=0, save2file=TRUE, out.dir=res.dir) res$ora[1:2] ## ----------------------------------------------------------------------------- method <- function(se, gs) { ps <- runif(length(gs)) names(ps) <- names(gs) return(ps) } ## ----------------------------------------------------------------------------- res <- runEA(geo2kegg[1:2], method, kegg.gs) res ## ----readRT------------------------------------------------------------------- ea.rtimes <- readResults(res.dir, names(geo2kegg), methods=c("ora", "camera"), type="runtime") ea.rtimes ## ----plotRuntime, fig.width=6, fig.height=6----------------------------------- bpPlot(ea.rtimes, what="runtime") ## ----runtimeORAvsCAMERA, fig.width=6, fig.height=6---------------------------- mean(ea.rtimes$ora) / mean(ea.rtimes$camera) ## ----readRankings------------------------------------------------------------- ea.ranks <- readResults(res.dir, names(geo2kegg), methods=c("ora", "camera"), type="ranking") lengths(ea.ranks) ea.ranks$ora[1:2] ## ----plotAdjSigSets, fig.width=6, fig.height=6-------------------------------- sig.sets <- evalNrSigSets(ea.ranks, alpha=0.05, padj="BH") sig.sets bpPlot(sig.sets, what="sig.sets") ## ----malaRankings------------------------------------------------------------- mala.kegg.file <- file.path(data.dir, "malacards", "KEGG.rds") mala.kegg <- readRDS(mala.kegg.file) sapply(mala.kegg, nrow) mala.kegg$ALZ mala.kegg$BRCA ## ----data2dis----------------------------------------------------------------- d2d.file <- file.path(data.dir, "malacards", "GseId2Disease.txt") d2d.map <- readDataId2diseaseCodeMap(d2d.file) head(d2d.map) ## ----evalRelevance------------------------------------------------------------ ea.ranks$ora$GSE1297 obs.score <- evalRelevance(ea.ranks$ora$GSE1297, mala.kegg$ALZ) obs.score ## ----compRand----------------------------------------------------------------- gs.names <- ea.ranks$ora$GSE1297$GENE.SET gs.ids <- substring(gs.names, 1, 8) rand.scores <- compRand(mala.kegg$ALZ, gs.ids, perm=50) summary(rand.scores) (sum(rand.scores >= obs.score) + 1) / 51 ## ----compOpt------------------------------------------------------------------ opt.score <- compOpt(mala.kegg$ALZ, gs.ids) opt.score round(obs.score / opt.score * 100, digits=2) ## ----evalAll, fig.width=6, fig.height=6--------------------------------------- all.kegg.res <- evalRelevance(ea.ranks, mala.kegg, d2d.map[names(geo2kegg)]) bpPlot(all.kegg.res, what="rel.sets") ## ----------------------------------------------------------------------------- rel.ranks <- mala.kegg$ALZ[,1:2] rel.ranks$REL.SCORE <- runif(nrow(rel.ranks), min=1, max=100) rel.ranks$REL.SCORE <- round(rel.ranks$REL.SCORE, digits = 2) ind <- order(rel.ranks$REL.SCORE, decreasing = TRUE) rel.ranks <- rel.ranks[ind,] rel.ranks ## ----------------------------------------------------------------------------- evalRelevance(ea.ranks$ora$GSE1297, rel.ranks) ## ----cacheRes----------------------------------------------------------------- cacheResource(geo2kegg, rname="geo2kegg") ## ----getRes------------------------------------------------------------------- geo2kegg <- loadEData("geo2kegg", cache=TRUE) names(geo2kegg) ## ----clearCache, eval=FALSE--------------------------------------------------- # cache.dir <- rappdirs::user_cache_dir("GSEABenchmarkeR") # bfc <- BiocFileCache::BiocFileCache(cache.dir) # BiocFileCache::removebfc(bfc) ## ----bpRegister--------------------------------------------------------------- BiocParallel::registered() ## ----bpParam------------------------------------------------------------------ bp.par <- BiocParallel::registered()[[1]] BiocParallel::bpprogressbar(bp.par) <- TRUE ## ----runDEBP------------------------------------------------------------------ geo2kegg <- runDE(geo2kegg, parallel=bp.par)