Microbiome subcommunity learning with logistic-tree normal latent Dirichlet allocation

TL;DR

This paper introduces a novel mixed-membership model combining logistic-tree normal and LDA to better capture heterogeneity in microbiome data, improving robustness and interpretability of microbial subcommunity identification.

Contribution

It develops a new Bayesian model integrating logistic-tree normal with LDA, addressing heterogeneity issues in microbiome subcommunity analysis.

Findings

Enhanced robustness in subcommunity detection

Improved handling of cross-sample heterogeneity

More meaningful microbial subcommunity identification

Abstract

Mixed-membership (MM) models such as Latent Dirichlet Allocation (LDA) have been applied to microbiome compositional data to identify latent subcommunities of microbial species. These subcommunities are informative for understanding the biological interplay of microbes and for predicting health outcomes. However, microbiome compositions typically display substantial cross-sample heterogeneities in subcommunity compositions -- that is, the variability in the proportions of microbes in shared subcommunities across samples -- which is not accounted for in prior analyses. As a result, LDA can produce inference which is highly sensitive to the specification of the number of subcommunities and often divides a single subcommunity into multiple artificial ones. To address this limitation, we incorporate the logistic-tree normal (LTN) model into LDA to form a new MM model. This model allows cross-sample variation in the composition of each subcommunity around some "centroid" composition that defines the subcommunity. Incorporation of auxiliary P\'olya-Gamma variables enables a computationally efficient collapsed blocked Gibbs sampler to carry out Bayesian inference under this model. By accounting for such heterogeneity, our new model restores the robustness of the inference in the specification of the number of subcommunities and allows meaningful subcommunities to be identified.