Statistical Bioinformatics Seminar: Ma

Title: Modelling the Joint Distribution of Compositional Microbiome Data 

Speaker: Dr Siyuan Ma (Vanderbilt University) 

Abstract: 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.  

About the speaker: Siyuan’s work focuses on statistical methods for modern molecular
epidemiology applications.  His methods research includes batch correction and
meta-analysis, dimension reduction, high-dimensional conditional testing, and simulation
models for power analysis.  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.