Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization.

ABSTRACT: High-throughput sequencing technology provides unprecedented opportunities to quantitatively explore human gut microbiome and its relation to diseases. Microbiome data are compositional, sparse, noisy, and heterogeneous, which pose serious challenges for statistical modeling. We propose an identifiable Bayesian multinomial matrix factorization model to infer overlapping clusters on both microbes and hosts. The proposed method represents the observed over-dispersed zero-inflated count matrix as Dirichlet-multinomial mixtures on which latent cluster structures are built hierarchically. Under the Bayesian framework, the number of clusters is automatically determined and available information from a taxonomic rank tree of microbes is naturally incorporated, which greatly improves the interpret

SUBMITTER: Zhou F 

PROVIDER: S-EPMC9291645 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature