Process / pipelineBioinformatics / omics

Björiešu mikrobiomu daudzveidības analīze — Varbūtības blakusgrupu struktūras novērtējums

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.

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  1. Holmes, I., Harris, K., & Quince, C. (2012). Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLOS ONE, 7(2), e30126. link
  2. La Rosa, P. S., Brooks, J. P., Deych, E., Boone, E. L., Edwards, D. J., Wang, Q., Sodergren, E., Weinstock, G., & Shannon, W. D. (2012). Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data. PLOS ONE, 7(12), e52078. link

Kā citēt šo lapu

ScholarGate. (2026, June 3). Bayesian Statistical Analysis of Microbiome Diversity. ScholarGate. https://scholargate.app/lv/bioinformatics/bayesian-microbiome-diversity-analysis

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ScholarGateBayesian Microbiome Diversity Analysis (Bayesian Statistical Analysis of Microbiome Diversity). Izgūts 2026-06-15 no https://scholargate.app/lv/bioinformatics/bayesian-microbiome-diversity-analysis · Datu kopa: https://doi.org/10.5281/zenodo.20539026