Association of body index with fecal microbiome in children cohorts with ethnic-geographic factor interaction: accurately using a Bayesian zero-inflated negative binomial regression model.

ABSTRACT: The exponential growth of high-throughput sequencing (HTS) data on the microbial communities presents researchers with an unparalleled opportunity to delve deeper into the association of microorganisms with host phenotype. However, this growth also poses a challenge, as microbial data are complex, sparse, discrete, and prone to zero inflation. Herein, by utilizing 10 distinct counting models for analyzing simulated data, we proposed an innovative Bayesian zero-inflated negative binomial (ZINB) regression model that is capable of identifying differentially abundant taxa associated with distinctive host phenotypes and quantifying the effects of covariates on these taxa. Our proposed model exhibits excellent accuracy compared with conventional Hurdle and INLA models, especially in scenarios c

SUBMITTER: Huang J 

PROVIDER: S-EPMC11651110 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature