{ "_id": "6a1ae0a11d7bb097a09ed227", "Package": "GSABenchmark", "Type": "Package", "Title": "Tools for benchmarking single-cell gene set analysis methods", "Version": "1.1.0", "Authors@R": "person('Andrei-Florian', 'Stoica', \nemail='andreistoica@foxmail.com',\nrole=c('aut','cre'),\ncomment=c(ORCID='0000-0002-5253-0826'))", "Description": "GSABenchmark is a package designed for benchmarking\nscRNA-seq gene set analysis (scGSA) methods. It provides both\ntraditional and novel benchmark metrics, as well as\nvisualization tools. Currently, GSABenchmark supports 17 scGSA\nmethods.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "biocViews": "Software, SingleCell, GeneSetEnrichment, GeneExpression,\nVisualization", "VignetteBuilder": "knitr", "URL": "https://github.com/andrei-stoica26/GSABenchmark", "BugReports": "https://github.com/andrei-stoica26/GSABenchmark/issues", "Config/testthat/edition": "3", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev git\nlibglpk-dev make libmagick++-dev gsfonts libgit2-dev libicu-dev\nlibpng-dev libuv1-dev libxml2-dev libssl-dev python3 libx11-dev\nzlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 13:06:52 UTC", "RemoteUrl": "https://github.com/bioc/GSABenchmark", "RemoteRef": "HEAD", "RemoteSha": "692fda1401e03883658e448d3250f6419e6ab947", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-30 09:18:04 UTC", "User": "root" }, "Author": "Andrei-Florian Stoica [aut, cre] (ORCID:\n)", "Maintainer": "Andrei-Florian Stoica ", "MD5sum": "a93d7d037aae811cf1c8ff9e5a5861e9", "_user": "bioc", "_type": "src", "_file": "GSABenchmark_1.1.0.tar.gz", "_fileid": "953bcc51a66ae737e075889d816ac1cf501da13e00f1b681c34406dd904beead", "_filesize": 2779833, "_sha256": "953bcc51a66ae737e075889d816ac1cf501da13e00f1b681c34406dd904beead", "_created": "2026-05-30T09:18:04.000Z", "_published": "2026-05-30T13:05:37.140Z", "_jobs": [ { "job": 78639205100, "time": 354, "config": "bioc-checks", "r": "4.6.0", "check": "NOTE", "artifact": "7307207314" }, { "job": 78639205108, "time": 712, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7307241801" }, { "job": 78639205106, "time": 657, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7307236340" }, { "job": 78639205103, "time": 441, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7308389430" }, { "job": 78639205102, "time": 390, "config": "macos-release-arm64", "r": 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"https://github.com/andrei-stoica26/gsabenchmark", "_realowner": "bioc", "_cranurl": false, "_exports": [ "aggregateRankPlot", "allBenchmarkPlots", "allBenchmarkResults", "benchmarkPlots", "corrPlots", "corrSummary", "efficiencyBenchmark", "geneSetRankPlots", "mdsPlots", "memoryPlot", "metricRankPlots", "predJaccardPlots", "ratioPlot", "runAddModuleScore", "runAUCell", "runBenchmark", "runBenchmarkShuffle", "runGSAMethods", "runGSVA", "runJASMINE", "runMDT", "runMethodShuffle", "runMLM", "runORA", "runPagoda2", "runPLAGE", "runSingscore", "runSiPSiC", "runssGSEA", "runUCell", "runUDT", "runVAM", "runZscore", "scorePlot", "supportedMethods", "timePlot" ], "_help": [ { "page": "aggregateRankPlot", "title": "Create an aggregate rank plot from a summary object", "topics": [ "aggregateRankPlot" ] }, { "page": "allBenchmarkPlots", "title": "Plot the complete list of benchmark summaries", "topics": [ "allBenchmarkPlots" ] }, { "page": "allBenchmarkResults", "title": "Generate all benchmark results with some precomputed arguments. This function generates all benchmark results by using precomputed values for 'normSilDF', 'dimMat' and 'maxDist'.", "topics": [ "allBenchmarkResults" ] }, { "page": "benchmarkPlots", "title": "Plot a list of summary data frames", "topics": [ "benchmarkPlots" ] }, { "page": "corrPlots", "title": "Create correlation plots for method results", "topics": [ "corrPlots" ] }, { "page": "corrSummary", "title": "Calculate correlation matrix for method results", "topics": [ "corrSummary" ] }, { "page": "efficiencyBenchmark", "title": "Extract running times and peak memory usage for gene set analysis methods", "topics": [ "efficiencyBenchmark" ] }, { "page": "geneSetRankPlots", "title": "Create gene set rank plots for the method results", "topics": [ "geneSetRankPlots" ] }, { "page": "mdsPlots", "title": "Create MDS plots for method results", "topics": [ "mdsPlots" ] }, { "page": "memoryPlot", "title": "Plot a data frame consisting of gene set analysis method peak memory usage This function plots data frame consisting of method peak memory usages with methods as rows, gene sets and the gene set average as columns.", "topics": [ "memoryPlot" ] }, { "page": "metricRankPlots", "title": "Create metric rank plots for the method results", "topics": [ "metricRankPlots" ] }, { "page": "predJaccardPlots", "title": "Create Jaccard tile plots for method binary predictions", "topics": [ "predJaccardPlots" ] }, { "page": "ratioPlot", "title": "Create a ratio rank plot for the method results", "topics": [ "ratioPlot" ] }, { "page": "runAddModuleScore", "title": "Run AddModuleScore", "topics": [ "runAddModuleScore" ] }, { "page": "runAUCell", "title": "Run AUCell", "topics": [ "runAUCell" ] }, { "page": "runBenchmark", "title": "Generate all benchmark results This function performs the entire 'GSABenchmark' pipeline.", "topics": [ "runBenchmark" ] }, { "page": "runBenchmarkShuffle", "title": "Generate all benchmark results for shuffled gene sets This function generates all benchmark results for shuffled gene sets.", "topics": [ "runBenchmarkShuffle" ] }, { "page": "runGSAMethods", "title": "Run gene set analysis methods", "topics": [ "runGSAMethods" ] }, { "page": "runGSVA", "title": "Run GSVA", "topics": [ "runGSVA" ] }, { "page": "runJASMINE", "title": "Run JASMINE", "topics": [ "runJASMINE" ] }, { "page": "runMDT", "title": "Run MDT using decoupleR", "topics": [ "runMDT" ] }, { "page": "runMethodShuffle", "title": "Run gene set analysis method on shuffled gene sets", "topics": [ "runMethodShuffle" ] }, { "page": "runMLM", "title": "Run MLM using decoupleR", "topics": [ "runMLM" ] }, { "page": "runORA", "title": "Run ORA using decoupleR", "topics": [ "runORA" ] }, { "page": "runPagoda2", "title": "Run pagoda2", "topics": [ "runPagoda2" ] }, { "page": "runPLAGE", "title": "Run PLAGE", "topics": [ "runPLAGE" ] }, { "page": "runSingscore", "title": "Run Singscore", "topics": [ "runSingscore" ] }, { "page": "runSiPSiC", "title": "Run SiPSiC", "topics": [ "runSiPSiC" ] }, { "page": "runssGSEA", "title": "Run ssGSEA", "topics": [ "runssGSEA" ] }, { "page": "runUCell", "title": "Run UCell", "topics": [ "runUCell" ] }, { "page": "runUDT", "title": "Run UDT using decoupleR", "topics": [ "runUDT" ] }, { "page": "runVAM", "title": "Run VAM", "topics": [ "runVAM" ] }, { "page": "runZscore", "title": "Run Zscore", "topics": [ "runZscore" ] }, { "page": "scorePlot", "title": "Plot a data frame consisting of gene set analysis method scores This function plots a data frame consisting of method scores with methods as rows, gene sets and the gene set average as columns.", "topics": [ "scorePlot" ] }, { "page": "supportedMethods", "title": "Show supported methods", "topics": [ "supportedMethods" ] }, { "page": "timePlot", "title": "Plot a data frame consisting of gene set analysis method running times This function plots data frame consisting of method running times with methods as rows, gene sets and the gene set average as columns.", "topics": [ "timePlot" ] } 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