@article {silverman2018,
	author = {Silverman, Justin D and Durand, Heather and Bloom, Rachael J and Mukherjee, Sayan and David, Lawrence A},
	title = {Dynamic linear models guide design and analysis of microbiota studies within artificial human guts},
	year = {2018},
	doi = {10.1101/306597},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Artificial gut models provide unique opportunities to study human-associated microbiota. Outstanding questions for these models{\textquoteright} fundamental biology include the timescales on which microbiota vary and the factors that drive such change. Answering these questions though requires overcoming analytical obstacles like estimating the effects of technical variation on observed microbiota dynamics, as well as the lack of appropriate benchmark datasets. To address these obstacles, we created a modeling framework based on multinomial logistic-normal dynamic linear models (MALLARDs) and performed dense longitudinal sampling of replicate artificial human guts over the course of 1 month. The resulting analyses revealed that when observed on an hourly basis, 76\% of community variation could be ascribed to technical noise from sample processing, which could also skew the observed covariation between taxa. Our analyses also supported hypotheses that human gut microbiota fluctuate on sub-daily timescales in the absence of a host and that microbiota can follow replicable trajectories in the presence of environmental driving forces. Finally, multiple aspects of our approach are generalizable and could ultimately be used to facilitate the design and analysis of longitudinal microbiota studies in vivo.},
	URL = {https://www.biorxiv.org/content/early/2018/04/24/306597},
	eprint = {https://www.biorxiv.org/content/early/2018/04/24/306597.full.pdf},
	journal = {bioRxiv}
}

@ARTICLE{silverman2019,
       author = {{Silverman}, Justin D. and {Roche}, Kimberly and {Holmes}, Zachary C. and
         {David}, Lawrence A. and {Mukherjee}, Sayan},
        title = "{Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes}",
      journal = {arXiv e-prints},
     keywords = {Statistics - Methodology},
         year = "2019",
        month = "Mar",
          eid = {arXiv:1903.11695},
        pages = {arXiv:1903.11695},
archivePrefix = {arXiv},
       eprint = {1903.11695},
 primaryClass = {stat.ME},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190311695S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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  publisher={Elsevier}
}

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}