BMDD: A probabilistic framework for accurate imputation of zero-inflated microbiome sequencing data.

ABSTRACT: Microbiome sequencing data are inherently sparse and compositional, with excessive zeros arising from biological absence or insufficient sampling. These zeros pose significant challenges for downstream analyses, particularly those that require log-transformation. We introduce BMDD (BiModal Dirichlet Distribution), a novel probabilistic modeling framework for accurate imputation of microbiome sequencing data. Unlike existing imputation approaches that assume unimodal abundance, BMDD captures the bimodal abundance distribution of the taxa via a mixture of Dirichlet priors. It uses variational inference and a scalable expectation-maximization algorithm for efficient imputation. Through simulations and real microbiome datasets, we demonstrate that BMDD outperforms competing methods in reconstr

SUBMITTER: Zhou H 

PROVIDER: S-EPMC12582502 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature