Package: GSEABenchmarkeR 1.27.0
Ludwig Geistlinger
GSEABenchmarkeR: Reproducible GSEA Benchmarking
The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated.
Authors:
GSEABenchmarkeR_1.27.0.tar.gz
GSEABenchmarkeR_1.27.0.zip(r-4.5)GSEABenchmarkeR_1.27.0.zip(r-4.4)GSEABenchmarkeR_1.27.0.zip(r-4.3)
GSEABenchmarkeR_1.27.0.tgz(r-4.4-any)GSEABenchmarkeR_1.27.0.tgz(r-4.3-any)
GSEABenchmarkeR_1.27.0.tar.gz(r-4.5-noble)GSEABenchmarkeR_1.27.0.tar.gz(r-4.4-noble)
GSEABenchmarkeR_1.27.0.tgz(r-4.4-emscripten)GSEABenchmarkeR_1.27.0.tgz(r-4.3-emscripten)
GSEABenchmarkeR.pdf |GSEABenchmarkeR.html✨
GSEABenchmarkeR/json (API)
NEWS
| # Install 'GSEABenchmarkeR' in R: |
| install.packages('GSEABenchmarkeR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/waldronlab/gseabenchmarker/issues
On BioConductor:GSEABenchmarkeR-1.27.0(bioc 3.21)GSEABenchmarkeR-1.26.0(bioc 3.20)
immunooncologymicroarrayrnaseqgeneexpressiondifferentialexpressionpathwaysgraphandnetworknetworkgenesetenrichmentnetworkenrichmentvisualizationreportwritingbioconductor-packageu24ca289073
Last updated 4 months agofrom:7fc44fcb7f. Checks:1 OK, 6 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 28 2025 |
| R-4.5-win | NOTE | Jan 28 2025 |
| R-4.5-linux | NOTE | Jan 28 2025 |
| R-4.4-win | NOTE | Jan 28 2025 |
| R-4.4-mac | NOTE | Jan 28 2025 |
| R-4.3-win | NOTE | Jan 28 2025 |
| R-4.3-mac | NOTE | Jan 28 2025 |
Exports:bpPlotcacheResourcecompOptcompRandevalNrSetsevalNrSigSetsevalRandomGSevalRelevanceevalTypeIErrorloadEDatamaPreprocmetaFCplotDEDistributionplotNrSamplesreadDataId2diseaseCodeMapreadResultsrunDErunEAwriteDE
Dependencies:abindannotateAnnotationDbiAnnotationHubaskpassBHBiobaseBiocFileCacheBiocGenericsBiocManagerBiocParallelBiocVersionBiostringsbitbit64bitopsblobcachemclicodetoolscpp11crayoncurlDBIdbplyrDelayedArraydplyredgeREnrichmentBrowserExperimentHubfansifastmapfilelockformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesglueGO.dbgraphgraphiteGSEABasehttrhwriterIRangesjsonliteKEGGandMetacoreDzPathwaysGEOKEGGdzPathwaysGEOKEGGgraphKEGGRESTlambda.rlatticelifecyclelimmalocfitmagrittrMatrixMatrixGenericsmatrixStatsmemoisemimeopensslorg.Hs.eg.dbpathviewpillarpkgconfigplogrpngpurrrR6rappdirsRCurlRgraphvizrlangRSQLiteS4ArraysS4VectorssafesnowSparseArraySparseMSPIAstatmodstringistringrSummarizedExperimentsystibbletidyrtidyselectUCSC.utilsutf8vctrswithrXMLxtableXVectoryaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Customized boxplot visualization of benchmark results | bpPlot |
| Caching of a resource | cacheResource |
| Evaluating gene set rankings for the number of (significant) sets | evalNrSets evalNrSigSets |
| Evaluation of enrichment methods on random gene sets | evalRandomGS |
| Evaluating phenotype relevance of gene set rankings | compOpt compRand evalRelevance |
| Evaluation of the type I error rate of enrichment methods | evalTypeIError |
| Loading pre-defined and user-defined expression data | loadEData |
| Preprocessing of microarray expression data | maPreproc |
| Read a mapping between dataset ID and disease code | readDataId2diseaseCodeMap |
| Reading results of enrichment analysis | readResults |
| Differential expression analysis for datasets of a compendium | metaFC plotDEDistribution plotNrSamples runDE writeDE |
| Application of enrichment methods to multiple datasets | runEA |
