Selected Publications

Here we develop efficient and accurate inference for a wide range of multinomial logistic-normal models. Our approach is often 4-5 orders of magnitude faster than MCMC and 1-2 orders of magnitude faster than Variational Bayes.
On arXiv

Here we demonstrate a simple paired experimental and modeling approach to measuring and correcting PCR bias in high-throughput sequencing data
On bioRxiv

In this work we provide a systematic description of different processes that can give rise to zero values as well as the types of methods for addressing zeros in sequence count studies. We demonstrate that zero-inflated models can have substantial biases in both simulated and real data settings. Additionally, we find that zeros due to biological absences can, for many applications, be approximated as originating from under sampling.
On bioRxiv

Here we introduce Multinomial Logistic-Normal Dynamic Linear Models (MALLARDs) for the analysis of longitudinal microbiome studies. We apply this framework for the analysis of a longitudinal study of 4 artificial gut models.
In Microbiome

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