Document Type

Article

Publication Date

2026

DOI

10.1007/s10260-026-00847-y

Publication Title

Statistical Methods & Applications

Volume

Advance online publication

Pages

33 pp.

Abstract

The logistic-normal multinomial distribution has been used for modelling microbiome data obtained from high-throughput sequencing technologies, which are compositional in nature. A logistic-normal multinomial distribution is a hierarchical multinomial distribution that assumes the latent variable which are the additive log-ratio (ALR) transformed proportions in a multinomial distribution follows a Gaussian distribution. Model-based clustering algorithms have also been developed for clustering microbiome data based on the logistic-normal models. However, the Gaussian assumption may violated when the ALR transformed variable exhibit heavy-tailed distributions or has outliers. Our study introduces a novel mixture of logistic-t multinomial models that effectively address these challenges. Utilizing a multivariate t distribution for ALR transformed latent variables, the proposed approach provides the flexibility to capture heavy tails and thus better accommodates the outliers when clustering microbiome compositional data. We incorporate a variational Expectation-Maximization (EM) algorithm to facilitate efficient parameter estimation for intractable posterior distributions. Our model demonstrates competitive performance in terms of clustering accuracy and parameter recovery as compared to existing approaches through simulation studies and real data analysis, hence offering a robust tool for exploring the complex structures and heterogeneity of microbiome compositional data.

Rights

© 2026 The Authors.

This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 

Original Publication Citation

Dai, W., Fang, Y., & Subedi, S. (2026). Logistic-t multinomial mixture model for clustering for microbiome data. Statistical Methods & Applications. Advance online publication. https://doi.org/10.1007/s10260-026-00847-y

ORCID

0000-0001-8430-1238 (Fang)

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