So sánh phương pháp

	Phân tích đa dạng hệ vi sinh vật bằng phương pháp Bayes	Phân tích làm giàu tập hợp gen (GSEA)
Lĩnh vực	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2010s (Dirichlet-Multinomial approach formalized ~2012; extensions ongoing)	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Người khởi xướng	Ian Holmes, Katie Harris, Christopher Quince (Dirichlet-Multinomial Mixture framework, 2012); broader Bayesian microbiome modeling community	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Loại	Probabilistic/Bayesian pipeline for compositional count data	Functional genomics / enrichment analysis
Công trình gốc	Holmes, I., Harris, K., & Quince, C. (2012). Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLOS ONE, 7(2), e30126.	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.
Tên gọi khác	Bayesian microbiome profiling, Dirichlet-Multinomial microbiome analysis, Bayesian alpha/beta diversity, probabilistic microbiome diversity	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Liên quan	5	5
Tóm tắt	Bayesian microbiome diversity analysis applies probabilistic models — chiefly Dirichlet-Multinomial and related hierarchical frameworks — to 16S rRNA or shotgun metagenomic count data to estimate alpha-diversity (within-sample richness and evenness) and beta-diversity (between-sample compositional differences) while propagating uncertainty through the entire inference chain. Unlike frequentist rarefaction-based approaches, Bayesian methods treat taxon counts as draws from a latent composition, enabling credible intervals on diversity metrics and principled comparison across groups with unequal sequencing depth.	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.