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DESCRIPTION:Siyuan Ma\, PhD\n\nAssistant Professor of Biostatistics\n\nDepa
 rtment of Biostatistics | Vanderbilt University Medical Center\n\nWhere: H
 ybrid | 2001 51³Ô¹ÏÍøCollege Avenue\, room 1140\; Zoom\n\nAbstract\n\nMicro
 biome epidemiology demands generative models of community profiles for stu
 dy design considerations such as power analysis. We developed SparseDOSSA\
 , a statistical model that parameterizes microbial communities and can be 
 used to simulate new\, realistic profiles to inform study designs. Our mod
 el connects zero-inflated marginals with a Gaussian copula\, and has an ad
 ditional renormalization component. As such\, it uniquely satisfies common
  compositional\, zero-inflation\, and interaction properties of microbiome
  data. We demonstrate that SparseDOSSA accurately models human-associated 
 microbiomes\, and can generate realistic synthetic communities with prescr
 ibed population and ecological structures. We provide an open-source imple
 mentation for SparseDOSSA\, which can be used in practice for power analys
 is and method benchmarking to inform microbiome study designs.\n\nSpeaker 
 Bio\n\nSiyuan is an Assistant Professor at the Department of Biostatistics
  at the Vanderbilt University Medical Center. His work focuses on statisti
 cal methods for modern molecular epidemiology applications. His methods re
 search includes batch correction and meta-analysis\, dimension reduction\,
  and high-dimensional modeling and conditional testing. His application ar
 eas include the healthy and dysbiotic microbiome\, cancer transcriptomics\
 , and spatially resolved imaging proteomics. He obtained his Ph.D. in bios
 tatistics from Harvard T.H. Chan School of Public Health and had postdocto
 ral training at the University of Pennsylvania.\n\nWebsite: https://syma-r
 esearch.github.io\n
DTSTART:20230322T193000Z
DTEND:20230322T203000Z
SUMMARY:Modeling the Joint Distribution of Compositional Microbiome Data
URL:/epi-biostat-occh/channels/event/modeling-joint-di
 stribution-compositional-microbiome-data-345999
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