18: Microbiome data analysis using a zero-inflated logistic normal multinomial model with covariates

Thomas M. Menino Convention & Exhibition Center 

Microbial abundance data, called microbiome data, are characterized by zero-inflated count structures. Zeng et al. (2022) proposed the zero-inflated logistic normal multinomial (ZILNM) model. A key characteristic of this model is the introduction of latent factors, representing unobserved variables that influence microbial compositions.

In this study, we extend the ZILNM model by incorporating observed subject-level covariates in addition to latent factors. While both latent factors and covariates affect model parameters, covariates are directly observable and can represent clinically meaningful information. By including covariates, the proposed model is expected to provide a more interpretable and practically relevant framework for medical microbiome data analysis.

Simulation studies were conducted under settings where latent factors were present and subjects were divided into control and treatment groups. Variational Bayesian inference was applied for parameter estimation, and we examined the behavior and estimation performance of regression coefficients associated with covariates, with particular emphasis on group comparison and interpretability.

zero-inflated models

count data

latent factors 

Biometrics Section