Selected Publications

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

In this review we takle statistical considerations starting from the data generation process, discussing technical and biological variation, experimental design, and various modeling approaches currently available.
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

Surveys of microbial communities (microbiota), typically measured as relative abundance of species, have illustrated the importance of these communities in human health and disease. Yet, statistical artifacts commonly plague the analysis of relative abundance data. Here, we introduce the PhILR transform, which incorporates microbial evolutionary models with the isometric log-ratio transform to allow off-the-shelf statistical tools to be safely applied to microbiota surveys.
In eLife

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