Bayesian Generalized Linear Models for Analyzing Compositional and Sub‐Compositional Microbiome Data via EM Algorithm

ABSTRACT The study of compositional microbiome data is critical for exploring the functional roles of microbial communities in human health and disease. Recent advances have shifted from traditional log‐ratio transformations of compositional covariates to zero constraint on the sum of the correspond...

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Veröffentlicht in:	Statistics in medicine Jg. 44; H. 7; S. e70084 - n/a
Hauptverfasser:	Zhang, Li, Ding, Zhenying, Cui, Jinhong, Zhou, Xiaoxiao, Yi, Nengjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 30.03.2025 Wiley Subscription Services, Inc
Schlagworte:	Algorithms Bayes Theorem Bayesian GLMs compositional data Computer Simulation EM algorithm Generalized linear models Humans Linear Models Markov Chains microbiome Microbiota Monte Carlo Method spike‐and‐slab priors sum‐to‐zero constraint compositional data Bayesian GLMs spike‐and‐slab priors EM algorithm microbiome sum‐to‐zero constraint
ISSN:	0277-6715, 1097-0258, 1097-0258
Online-Zugang:	Volltext
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