BioMiCo: a supervised Bayesian model for inference of microbial community structure

Microbiome
Mahdi ShafieiJoseph P Bielawski

Abstract

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. 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 ...Continue Reading

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Citations

Apr 10, 2016·Current Opinion in Microbiology·Cesar CardonaJack A Gilbert
Nov 21, 2014·PLoS Computational Biology·Mahdi ShafieiJoseph P Bielawski
Dec 1, 2017·Genome Biology·Himel MallickCurtis Huttenhower
Jun 7, 2018·PLoS Computational Biology·Koichi HigashiKen Kurokawa
Jun 6, 2018·Biostatistics·Kris Sankaran, Susan P Holmes
Feb 6, 2016·Inflammatory Bowel Diseases·Jessica M Moore-ConnorsJohan Van Limbergen
Sep 15, 2018·Frontiers in Public Health·Pauline TrinhPeter M Rabinowitz

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Methods Mentioned

BETA
biopsies

Software Mentioned

BioMiCo

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