Microbiome subcommunity learning with logistic-tree normal latent Dirichlet allocation.

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 incorporat

SUBMITTER: LeBlanc P 

PROVIDER: S-EPMC10090221 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature