Microbiome epidemiology demands generative models of community profiles for study 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 model connects zero-inflated marginals with a Gaussian copula, and has an additional 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 prescribed population and ecological structures. We provide an open-source implementation for SparseDOSSA, which can be used in practice for power analysis and method benchmarking to inform microbiome study designs.

Siyuan is an Assistant Professor at the Department of Biostatistics at the Vanderbilt University Medical Center. His work focuses on statistical methods for modern molecular epidemiology applications. His methods research includes batch correction and meta-analysis, dimension reduction, and high-dimensional modeling and conditional testing. His application areas include the healthy and dysbiotic microbiome, cancer transcriptomics, and spatially resolved imaging proteomics. He obtained his Ph.D. in biostatistics from Harvard T.H. Chan School of Public Health and had postdoctoral training at the University of Pennsylvania. Website: https://syma-research.github.io