Sparse Bayesian Group Factor Model for Feature Interactions in Multiple Count Tables Data

• 成果类型: Article
• 署名作者: Zhang, Shuangjie; Shen, Yuning; Chen, Irene A.; Lee, Juhee
• 署名单位: University of California System; University of California Santa Cruz; University of California System; University of California Los Angeles
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2449721
• 发表日期: 2025
• 页码: 723-736
• 关键词: Covariance Estimation; actinomyces; microbiome; mixtures; rates

摘要

Group factor models have been developed to infer relationships between multiple co-occurring multivariate continuous responses. Motivated by complex count data from multi-domain microbiome studies using next-generation sequencing, we develop a sparse Bayesian group factor model (Sp-BGFM) for multiple count table data that captures the interaction between microorganisms in different domains. Sp-BGFM uses a rounded kernel mixture model using a Dirichlet process (DP) prior with log-normal mixture kernels for count vectors. A group factor model is used to model the covariance matrix of the mixing kernel that describes microorganism interaction. We construct a Dirichlet-Horseshoe (Dir-HS) shrinkage prior and use it as a joint prior for factor loading vectors. Joint sparsity induced by a Dir-HS prior greatly improves the performance in high-dimensional applications. We further model the effects of covariates on microbial abundances using regression. The semiparametric model flexibly accommodates large variability in observed counts and excess zero counts and provides a basis for robust estimation of the interaction and covariate effects. We evaluate Sp-BGFM using simulation studies and real data analysis, comparing it to popular alternatives. Our results highlight the necessity of joint sparsity induced by the Dir-HS prior, and the benefits of a flexible DP model for baseline abundances. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.