2020/11/12 by Yuan Fang, Fang, Yuan, Sanjeena Subedi +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #62H30 #Bayesian Methods and Mixture Models #Colorectal Cancer Screening and Detection #Computation (stat.CO) #FOS: Computer and information sciences #Gut microbiota and health #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2011.06682
openalex publication_date 2020/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typically high-dimensional, over-dispersed, and can only reveal relative abundance therefore being treated as compositional. Analyzing compositional data presents many challenges because they are restricted on a simplex. In a logistic normal multinomial model, the relative abundance is mapped from a simplex to a latent variable that exists on the real Euclidean space using the additive log-ratio transformation. While a logistic normal multinomial approach brings in flexibility for modeling the data, it comes with a heavy computational cost as the parameter estimation typically relies on Bayesian techniques. In this paper, we develop a novel mixture of logistic normal multinomial models for clustering microbiome data. Additionally, we utilize an efficient framework for parameter estimation using variational Gaussian approximations (VGA). Adopting a variational Gaussian approximation for the posterior of the latent variable reduces the computational overhead substantially. The proposed method is illustrated on simulated and real datasets.