ArticleBriefings in bioinformatics2021
Toward a gold standard for benchmarking gene set enrichment analysis.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 2 of them syntheses that pooled it.
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Who cites it
88 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis.PLoS computational biology · 2022Pooled it
- SGLT-2 Inhibitors are Associated With a Lower Risk of Multiple Myeloma.European journal of haematology · 2026Article
- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- Improved chromosome-level genome assembly of Pleurotus ostreatus CCMSSC 00389.G3 (Bethesda, Md.) · 2026Article
- GeneInsight: Condensing gene set knowledge via language models.PLoS computational biology · 2026Article
- DAVID: a web server for functional annotation and functional enrichment analysis of gene lists (2025 update).Nucleic acids research · 2026Article
- Exploring molecular signatures of senescence with marker, an R Toolkit for evaluating gene sets as phenotypic markers.NAR genomics and bioinformatics · 2026Article
- Genetic Determinants of T-Cell Homeostasis in Critical Illness: An Exploratory Analysis of Immune Gene Variants and TREC Dynamics.Journal of personalized medicine · 2026Article
- CD36 enhances sensitivity of triple negative breast cancer cells to palmitate-induced ferroptosis.Cell death & disease · 2026Article
- A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026Article
- Genetic associations and candidate functional genes linking depression and obesity: a multi-omics integrative study.Frontiers in genetics · 2026Article
- Benchmarking multiple gene ontology enrichment tools reveals high biological significance, ranking, and stringency heterogeneity among datasets.Frontiers in bioinformatics · 2026Article
- GeDi: simplifying gene set distances for enhanced omics interpretation in R/Bioconductor.BMC bioinformatics · 2025Article
- Persistent and Dose-Dependent Neural and Metabolic Gene Expression Changes Induced by Transient Citalopram Exposure in Zebrafish Embryos.International journal of molecular sciences · 2025Article
- NeXus: An Automated Platform for Network Pharmacology and Multi-Method Enrichment Analysis.International journal of molecular sciences · 2025Article
- Constraint based modeling of drug induced metabolic changes in a cancer cell line.NPJ systems biology and applications · 2025Article
- Red-Wine Gene Networks Linked to Exceptional Longevity in Humans.Biomolecules · 2025Review
- The important role of standards for the uptake of transcriptomics and metabolomics based in vitro methods in regulatory toxicology.Archives of toxicology · 2025Review
- Weighted overlapping group lasso for integrating prior network knowledge into gene set analysis.BMC bioinformatics · 2025Article
28 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
motivationAlthough gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected datasets and biological reasoning on the relevance of resulting enriched gene sets.
resultsWe develop an extensible framework for reproducible benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization and detection of relevant processes. This framework incorporates a curated compendium of 75 expression datasets investigating 42 human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods, identifying significant differences in runtime and applicability to RNA-seq data, fraction of enriched gene sets depending on the null hypothesis tested and recovery of the predefined relevance rankings. We make practical recommendations on how methods originally developed for microarray data can efficiently be applied to RNA-seq data, how to interpret results depending on the type of gene set test conducted and which methods are best suited to effectively prioritize gene sets with high phenotype relevance. AVAILABILITY: http://bioconductor.org/packages/GSEABenchmarkeR. CONTACT: ludwig.geistlinger@sph.cuny.edu.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.