Process / pipelineBioinformatics / omics
Pathway Enrichment Analysis — Biological Pathway Enrichment Analysis
Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.
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Sources
- Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550. DOI: 10.1073/pnas.0506580102 ↗
- Alexa, A., Rahnenführer, J., & Lengauer, T. (2006). Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics, 22(13), 1600–1607. DOI: 10.1093/bioinformatics/btl140 ↗
Related methods
Referenced by
Bayesian ChIP-seq peak callingBayesian eQTL analysisBayesian Gene Set Enrichment AnalysisBayesian GWASBayesian Metabolomics AnalysisBayesian Pathway Enrichment AnalysisBayesian Proteomics AnalysisBayesian RNA-seq differential expressionDifferential Epigenome-Wide Association StudyDifferential eQTL AnalysisDifferential Metabolomics AnalysisDifferential pathway enrichment analysisDifferential proteomics analysisEpigenome-wide association studyeQTL AnalysisGene Set Enrichment AnalysisGenome-wide association studyMachine learning-assisted expression quantitative trait loci analysisMachine learning-assisted gene set enrichment analysisMachine learning-assisted microbiome diversity analysisMachine learning-assisted RNA-seq differential expressionMachine learning-assisted single-cell RNA-seq analysisMetabolomics analysisMulti-omics epigenome-wide association studyMulti-omics gene set enrichment analysisMulti-omics metabolomics analysisMulti-omics microbiome diversity analysisMulti-omics proteomics analysisMulti-omics single-cell RNA-seq analysisNetwork-based copy number variation analysisNetwork-based epigenome-wide association studyNetwork-based eQTL analysisNetwork-based gene set enrichment analysisNetwork-based GWASNetwork-based metabolomics analysisNetwork-based microbiome diversity analysisNetwork-based RNA-seq differential expressionNetwork-based single-cell RNA-seq analysisProteomics AnalysisRNA-seq Differential ExpressionSingle-cell eQTL analysisSingle-cell Gene Set Enrichment AnalysisSingle-cell GWASSingle-cell metabolomics analysisSingle-cell RNA-seq analysisSingle-cell RNA-seq differential expressionTime-series gene set enrichment analysisTime-series metabolomics analysisTime-series microbiome diversity analysisTime-series pathway enrichment analysisTime-series RNA-seq differential expressionTime-series single-cell RNA-seq analysis