Bayesian Nonparametric Longitudinal Factor Model for Microbiome Counts
Abstract
Longitudinal microbiome studies provide important insights into the temporal evolution of microbial abundances and dynamic cross-feature dependence. Modeling longitudinal microbiome data is challenging because microbiome counts are highly heterogeneous and zero-inflated, and, moreover, repeated measurements induce within-subject temporal dependence and possibly time-varying cross-feature covariance. To address these challenges, we develop a flexible Bayesian longitudinal factor model using a rounded log-normal latent representation. The mean abundance process begins with a Dirichlet process mixture baseline, which accommodates sparse and multimodal abundance distributions, and subsequently evolves through an autoregressive process. Autoregressive coefficients differ across prespecified biological groups, which are defined by genotypes in our application. A separate autoregressive factor process captures shared cross-feature dependence. The model therefore distinguishes subject--feature mean evolution from shared dependence and yields a closed-form, time-indexed covariance. Simulation studies evaluate dynamic covariance recovery under matched and misspecified data-generating mechanisms. In the longitudinal mouse-gut microbiome application, LODI identifies highly persistent feature-specific abundance trajectories and shared latent factors that distinguish the three genotype groups over time. At week 15, the dominant factor follows the observed WT < ACK/G < HACK/G tumor-severity ordering, with its strongest contributions arising from OTUs classified within Clostridiaceae, Clostridium, and RF39.
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BibTeX
@misc{zhang2026bayesian,
title = {{Bayesian Nonparametric Longitudinal Factor Model for Microbiome Counts}},
author = {Shuangjie Zhang and Seoyeon Ok and Arul Jayaraman and Kirsten Landrock and Ivan Ivanov and Robert S. Chapkin and Yang Ni},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.21224},
eprint = {2610.21224},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.21224},
}
RIS
TY - UNPB
TI - Bayesian Nonparametric Longitudinal Factor Model for Microbiome Counts
AU - Shuangjie Zhang
AU - Seoyeon Ok
AU - Arul Jayaraman
AU - Kirsten Landrock
AU - Ivan Ivanov
AU - Robert S. Chapkin
AU - Yang Ni
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.21224
UR - https://arxiv.org/abs/2610.21224
AB - Longitudinal microbiome studies provide important insights into the temporal evolution of microbial abundances and dynamic cross-feature dependence. Modeling longitudinal microbiome data is challenging because microbiome counts are highly heterogeneous and zero-inflated, and, moreover, repeated measurements induce within-subject temporal dependence and possibly time-varying cross-feature covariance. To address these challenges, we develop a flexible Bayesian longitudinal factor model using a rounded log-normal latent representation. The mean abundance process begins with a Dirichlet process mixture baseline, which accommodates sparse and multimodal abundance distributions, and subsequently evolves through an autoregressive process. Autoregressive coefficients differ across prespecified biological groups, which are defined by genotypes in our application. A separate autoregressive factor process captures shared cross-feature dependence. The model therefore distinguishes subject--feature mean evolution from shared dependence and yields a closed-form, time-indexed covariance. Simulation studies evaluate dynamic covariance recovery under matched and misspecified data-generating mechanisms. In the longitudinal mouse-gut microbiome application, LODI identifies highly persistent feature-specific abundance trajectories and shared latent factors that distinguish the three genotype groups over time. At week 15, the dominant factor follows the observed WT < ACK/G < HACK/G tumor-severity ordering, with its strongest contributions arising from OTUs classified within Clostridiaceae, Clostridium, and RF39.
ER -