পদ্ধতির তুলনা করুন

	বেয়েশীয় পথ সমৃদ্ধি বিশ্লেষণ	পাথওয়ে এনরিচমেন্ট বিশ্লেষণ
ক্ষেত্র	জৈব তথ্যবিজ্ঞান	জৈব তথ্যবিজ্ঞান
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2001–2007	2003–2005
প্রবর্তক	Pierre Baldi, Anthony Long; Michael Newton et al. (foundational Bayesian gene-set frameworks)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
ধরন	Probabilistic gene-set testing	Statistical functional annotation method
মৌলিক উৎস	Baldi, P., & Long, A. D. (2001). A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17(6), 509–519.	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.
অপর নাম	Bayesian gene-set testing, Bayesian GSEA, Bayesian functional enrichment, BGSEA	PEA, overrepresentation analysis, ORA, functional enrichment analysis
সম্পর্কিত	6	6
সারসংক্ষেপ	Bayesian pathway enrichment analysis tests whether a predefined set of genes — a biological pathway — is systematically overrepresented among genes that show evidence of differential activity in an experiment. Unlike classical over-representation tests, it encodes prior biological knowledge as a prior distribution and updates it with the observed expression data, yielding posterior probabilities of enrichment rather than p-values. This probabilistic framing naturally handles small samples, multiple pathways, and uncertainty propagation in a coherent statistical framework.	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.