Latent environment allocation of microbial community data.

ABSTRACT: As data for microbial community structures found in various environments has increased, studies have examined the relationship between environmental labels given to retrieved microbial samples and their community structures. However, because environments continuously change over time and space, mixed states of some environments and its effects on community formation should be considered, instead of evaluating effects of discrete environmental categories. Here we applied a hierarchical Bayesian model to paired datasets containing more than 30,000 samples of microbial community structures and sample description documents. From the training results, we extracted latent environmental topics that associate co-occurring microbes with co-occurring word sets among samples. Topics are the core elem

SUBMITTER: Higashi K 

PROVIDER: S-EPMC6005635 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature