Порівняння методів

	Байєсівський аналіз збагачення генних наборів (Bayesian GSEA)	Аналіз збагачення шляхів
Галузь	Біоінформатика	Біоінформатика
Родина	Process / pipeline	Process / pipeline
Рік появи	2004–2007	2003–2005
Автор методу	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)
Тип	Probabilistic gene set enrichment method	Statistical functional annotation method
Основоположне джерело	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.
Інші назви	Bayesian GSEA, BGSEA, Bayesian pathway scoring, probabilistic gene set testing	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Пов'язані	6	6
Підсумок	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.