Package: GSABenchmark 1.0.0

Andrei-Florian Stoica

GSABenchmark: Tools for benchmarking single-cell gene set analysis methods

GSABenchmark is a package designed for benchmarking scRNA-seq gene set analysis (scGSA) methods. It provides both traditional and novel benchmark metrics, as well as visualization tools. Currently, GSABenchmark supports 17 scGSA methods.

Authors:Andrei-Florian Stoica [aut, cre]

GSABenchmark_1.0.0.tar.gz
GSABenchmark_1.0.0.zip(r-4.7)GSABenchmark_1.0.0.zip(r-4.6)GSABenchmark_1.0.0.zip(r-4.5)
GSABenchmark_1.0.0.tgz(r-4.6-any)GSABenchmark_1.0.0.tgz(r-4.5-any)
GSABenchmark_1.0.0.tar.gz(r-4.7-any)GSABenchmark_1.0.0.tar.gz(r-4.6-any)
GSABenchmark_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
GSABenchmark/json (API)
NEWS

# Install 'GSABenchmark' in R:

install.packages('GSABenchmark', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/andrei-stoica26/gsabenchmark/issues

On BioConductor:GSABenchmark-1.1.0(bioc 3.24)GSABenchmark-1.0.0(bioc 3.23)

software singlecell genesetenrichment geneexpression visualization

5.08 score 1 stars 216 downloads 36 exports 304 dependencies

Last updated from:e13f1cc684 (on RELEASE_3_23). Checks:1 NOTE, 9 OK. Indexed: no.

Target	Result	Time
bioc-checks	NOTE	383
linux-devel-x86_64	OK	846
source / vignettes	OK	478
linux-release-x86_64	OK	903
macos-release-arm64	OK	402
macos-oldrel-arm64	OK	344
windows-devel	OK	898
windows-release	OK	990
windows-oldrel	OK	1136
wasm-release	OK	307

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

Dependencies:abdiv abind annotate AnnotationDbi ape askpass assorthead backports base64enc beachmat BH Biobase BiocFileCache BiocGenerics biocmake BiocParallel BiocSingular Biostrings bit bit64 bitops blob bookdown brew broom bslib cachem caTools cellranger class cli clipr cluster codetools commonmark cowplot cpp11 crayon credentials crosstalk CSOA curl data.table DBI dbplyr decoupleR DelayedArray DelayedMatrixStats deldir dendsort desc digest dir.expiry distributional dotCall64 dplyr dqrng drat edgeR escape evaluate fabR farver fastcluster fastDummies fastmap filelock fitdistrplus float FNN fontawesome forcats formatR fs futile.logger futile.options future future.apply generics GenomicRanges gert ggalluvial ggdist ggeasy ggforce ggnewscale ggplot2 ggraph ggrepel ggridges gitcreds globals glue goftest gplots graph graphlayouts gridExtra GSEABase GSVA gtable gtools h5mread hammers haven HDF5Array henna here highr hms htmltools htmlwidgets httpuv httr httr2 ica igraph ini IRanges irlba isoband jaccard janitor jquerylib jsonlite KEGGREST kernlab KernSmooth kerntools knitr labeling lambda.r later lattice lazyeval lgr lifecycle limma listenv LISTO liver lmtest locfit lsa lubridate magick magrittr MASS Matrix MatrixExtra MatrixGenerics matrixStats memoise memuse mgcv mime miniUI mlapi MLmetrics mltools N2R nlme numDeriv omnibus openssl otel pagoda2 paletteer parallelly patchwork pbapply pbmcapply pillar pkgconfig plotly plyr png polyclip prettyunits primes prismatic pROC progress progressr promises purrr qs2 quadprog qvalue R.methodsS3 R.oo R.utils R6 RANN rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppParallel RcppProgress RcppSpdlog RcppTOML readr readxl rematch rematch2 reshape reshape2 reticulate rhdf5 rhdf5filters Rhdf5lib RhpcBLASctl rjson rlang rmarkdown RMTstat ROCR Rook rprojroot rsparse RSpectra RSQLite rstudioapi rsvd Rtsne S4Arrays S4Vectors S7 sass ScaledMatrix scales scattermore sccore scLang sctransform Seqinfo Seurat SeuratObject shiny SingleCellExperiment singscore SiPSiC sitmo slam snakecase snow SnowballC sourcetools sp spam SparseArray sparseMatrixStats SpatialExperiment spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils statisfactory statmod stringfish stringi stringr SummarizedExperiment survival sys systemfonts tensor text2vec textshape tibble tidygraph tidyr tidyselect timechange tinytex triebeard tweenr tzdb urltools usethis utf8 uwot VAM vctrs viridis viridisLite vroom whisker withr writexl xfun XML xtable XVector yaml zip zoo

Introduction to GSABenchmark

Andrei-Florian Stoica

Rendered fromGSABenchmark.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2026-03-04
Started: 2025-07-27

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Create an aggregate rank plot from a summary object	aggregateRankPlot
Plot the complete list of benchmark summaries	allBenchmarkPlots
Generate all benchmark results with some precomputed arguments. This function generates all benchmark results by using precomputed values for 'normSilDF', 'dimMat' and 'maxDist'.	allBenchmarkResults
Plot a list of summary data frames	benchmarkPlots
Create correlation plots for method results	corrPlots
Calculate correlation matrix for method results	corrSummary
Extract running times and peak memory usage for gene set analysis methods	efficiencyBenchmark
Create gene set rank plots for the method results	geneSetRankPlots
Create MDS plots for method results	mdsPlots
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.	memoryPlot
Create metric rank plots for the method results	metricRankPlots
Create Jaccard tile plots for method binary predictions	predJaccardPlots
Create a ratio rank plot for the method results	ratioPlot
Run AddModuleScore	runAddModuleScore
Run AUCell	runAUCell
Generate all benchmark results This function performs the entire 'GSABenchmark' pipeline.	runBenchmark
Generate all benchmark results for shuffled gene sets This function generates all benchmark results for shuffled gene sets.	runBenchmarkShuffle
Run gene set analysis methods	runGSAMethods
Run GSVA	runGSVA
Run JASMINE	runJASMINE
Run MDT using decoupleR	runMDT
Run gene set analysis method on shuffled gene sets	runMethodShuffle
Run MLM using decoupleR	runMLM
Run ORA using decoupleR	runORA
Run pagoda2	runPagoda2
Run PLAGE	runPLAGE
Run Singscore	runSingscore
Run SiPSiC	runSiPSiC
Run ssGSEA	runssGSEA
Run UCell	runUCell
Run UDT using decoupleR	runUDT
Run VAM	runVAM
Run Zscore	runZscore
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.	scorePlot
Show supported methods	supportedMethods
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.	timePlot