title: BioMiCo: a supervised Bayesian model for inference of microbial community structure.
abstract: BACKGROUND
  Microbiome samples often represent mixtures of communities, where each community is composed of overlapping assemblages of species. Such mixtures are complex, the number of species is huge and abundance information for many species is often sparse. Classical methods have a limited value for identifying complex features within such data.
  
  
  RESULTS
  Here, we describe a novel hierarchical model for Bayesian inference of microbial communities (BioMiCo). The model takes abundance data derived from environmental DNA, and models the composition of each sample by a two-level hierarchy of mixture distributions constrained by Dirichlet priors. BioMiCo is supervised, using known features for samples and appropriate prior constraints to overcome the challenges posed by many variables, sparse data, and large numbers of rare species. The model is trained on a portion of the data, where it learns how assemblages of species are mixed to form communities and how assemblages are related to the known features of each sample. Training yields a model that can predict the features of new samples. We used BioMiCo to build models for three serially sampled datasets and tested their predictive accuracy across different time points. The first model was trained to predict both body site (hand, mouth, and gut) and individual human host. It was able to reliably distinguish these features across different time points. The second was trained on vaginal microbiomes to predict both the Nugent score and individual human host. We found that women having normal and elevated Nugent scores had distinct microbiome structures that persisted over time, with additional structure within women having elevated scores. The third was trained for the purpose of assessing seasonal transitions in a coastal bacterial community. Application of this model to a high-resolution time series permitted us to track the rate and time of community succession and accurately predict known ecosystem-level events.
  
  
  CONCLUSIONS
  BioMiCo provides a framework for learning the structure of microbial communities and for making predictions based on microbial assemblages. By training on carefully chosen features (abiotic or biotic), BioMiCo can be used to understand and predict transitions between complex communities composed of hundreds of microbial species.
authors.affiliation: Department of Mathematics and Statistics, Dalhousie University, Halifax, NS Canada.
authors.forename: Mahdi
authors.initials: M
authors.lastname: Shafiei
authors.affiliation: Department of Biology, Dalhousie University, Halifax, NS Canada.
authors.forename: Katherine A
authors.initials: KA
authors.lastname: Dunn
authors.affiliation: Department of Biology, Dalhousie University, Halifax, NS Canada.
authors.forename: Eva
authors.initials: E
authors.lastname: Boon
authors.affiliation: Department of Biology, Dalhousie University, Halifax, NS Canada.
authors.forename: Shelley M
authors.initials: SM
authors.lastname: MacDonald
authors.affiliation: Department of Biology, Concordia University, Montreal, Quebec Canada.
authors.forename: David A
authors.initials: DA
authors.lastname: Walsh
authors.affiliation: Department of Mathematics and Statistics, Dalhousie University, Halifax, NS Canada.
authors.forename: Hong
authors.initials: H
authors.lastname: Gu
authors.affiliation: Department of Mathematics and Statistics, Dalhousie University, Halifax, NS Canada ; Department of Biology, Dalhousie University, Halifax, NS Canada.
authors.forename: Joseph P
authors.initials: JP
authors.lastname: Bielawski
keywords: Admixture model; Bayesian model; Hierarchical mixed-membership model; Human; Microbial community structure; Microbiome; OTU abundance data; Supervised learning; Temperate coastal ocean