ArticleBMC bioinformatics2009
A general modular framework for gene set enrichment analysis.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 206 papers, 1 of them a synthesis that pooled it.
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Who cites it
206 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE).PLoS computational biology · 2019Pooled it
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- Statistical properties of functional connectivity MRI enrichment analysis in school-age autism research.Developmental cognitive neuroscience · 2025Article
- The E2F4 transcriptional repressor is a key mechanistic regulator of colon cancer resistance tobioRxiv : the preprint server for biology · 2025Article
- Gene Set Enrichment Analysis in Zebrafish Embryos Is Susceptible to False-Positive Results in the Absence of Differentially Expressed Genes.Bioinformatics and biology insights · 2025Article
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- Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics.NeuroImage · 2024Article
- Blood and urine multi-omics analysis of the impact of e-vaping, smoking, and cessation: from exposome to molecular responses.Scientific reports · 2024Article
- bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.Frontiers in bioinformatics · 2024Article
146 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
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Abstract
backgroundAnalysis of microarray and other high-throughput data on the basis of gene sets, rather than individual genes, is becoming more important in genomic studies. Correspondingly, a large number of statistical approaches for detecting gene set enrichment have been proposed, but both the interrelations and the relative performance of the various methods are still very much unclear.
resultsWe conduct an extensive survey of statistical approaches for gene set analysis and identify a common modular structure underlying most published methods. Based on this finding we propose a general framework for detecting gene set enrichment. This framework provides a meta-theory of gene set analysis that not only helps to gain a better understanding of the relative merits of each embedded approach but also facilitates a principled comparison and offers insights into the relative interplay of the methods.
conclusionWe use this framework to conduct a computer simulation comparing 261 different variants of gene set enrichment procedures and to analyze two experimental data sets. Based on the results we offer recommendations for best practices regarding the choice of effective procedures for gene set enrichment analysis.
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