Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis

@article{Ren2017BayesianME,
  title={Bayesian mixed effects models for zero-inflated compositions in microbiome data analysis},
  author={B. Ren and Sergio Bacallado and S. Favaro and T. Vatanen and C. Huttenhower and L. Trippa},
  journal={The Annals of Applied Statistics},
  year={2017},
  volume={14},
  pages={494-517}
}
Detecting associations between microbial compositions and sample characteristics is one of the most important tasks in microbiome studies. Most of the existing methods apply univariate models to single microbial species separately, with adjustments for multiple hypothesis testing. We propose a Bayesian analysis for a generalized mixed effects linear model tailored to this application. The marginal prior on each microbial composition is a Dirichlet Process, and dependence across compositions is… Expand

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