方法对比

	贝叶斯基因集富集分析	通路富集分析
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2004–2007	2003–2005
提出者	Michael A. Newton, Frank A. Quintana and colleagues; building on Subramanian et al. GSEA framework	Mootha et al. (2003); systematised by Subramanian et al. (2005)
类型	Probabilistic gene set enrichment method	Statistical functional annotation method
开创性文献	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., ... & 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.	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.
别名	Bayesian GSEA, BGSEA, Bayesian pathway scoring, probabilistic gene set testing	PEA, overrepresentation analysis, ORA, functional enrichment analysis
相关	6	6
摘要	Bayesian gene set enrichment analysis (Bayesian GSEA) applies a probabilistic framework to determine whether predefined sets of genes — representing biological pathways, cellular processes, or functional categories — are collectively more differentially expressed than expected by chance. Unlike classical frequentist GSEA, the Bayesian approach models uncertainty in expression estimates explicitly, incorporates prior biological knowledge, and produces posterior probabilities of enrichment rather than raw p-values, enabling more principled inference especially in small-sample settings.	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.