So sánh phương pháp

	Phân tích đa dạng hệ vi sinh vật bằng phương pháp Bayes	Phân tích đa dạng vi khuẩn chí dựa trên mạng lưới
Lĩnh vực	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2010s (Dirichlet-Multinomial approach formalized ~2012; extensions ongoing)	2012
Người khởi xướng	Ian Holmes, Katie Harris, Christopher Quince (Dirichlet-Multinomial Mixture framework, 2012); broader Bayesian microbiome modeling community	Faust, Raes, Friedman, Alm and colleagues
Loại	Probabilistic/Bayesian pipeline for compositional count data	Integrative bioinformatics pipeline
Công trình gốc	Holmes, I., Harris, K., & Quince, C. (2012). Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLOS ONE, 7(2), e30126.	Friedman, J., & Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Computational Biology, 8(9), e1002687.
Tên gọi khác	Bayesian microbiome profiling, Dirichlet-Multinomial microbiome analysis, Bayesian alpha/beta diversity, probabilistic microbiome diversity	microbial co-occurrence network analysis, microbiome network ecology, ecological network-based diversity, NBMDA
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.	Network-based microbiome diversity analysis integrates graph-theoretic co-occurrence network inference with classical alpha- and beta-diversity metrics to characterize the structural organization of microbial communities. Rather than treating taxa as independent entities, the method models pairwise microbial associations as edges in a network, enabling identification of keystone taxa, community modules, and ecological interaction patterns that simple diversity indices cannot detect.