EVENT DETAILS
Title: Modeling microbiome data in the presence of complex measurement error
Abstract: Estimating and modeling the relative abundances of bacterial species in a microbiome is a common task in modern microbiome studies. Motivated by our work showing that high-throughput sequencing distorts the true composition of microbial communities, we propose a statistical model and algorithm for estimating microbial relative abundances. Notably, our procedure permits estimated relative abundances to lie on the boundary of the simplex. We conclude with examples of the utility of the method, and recommendations for the design and analysis of microbiome studies. Our approach can be leveraged to select experimental protocols, design experiments with appropriate control data, and remove sample-specific contamination. This is joint work with David Clausen.
Bio: Amy Willis is the Principal Investigator of the Statistical Diversity Lab and a tenure-track Assistant Professor in the Department of Biostatistics at the University of Washington. Amy and StatDivLab develop tools for the analysis of microbiome and biodiversity data. Amy is passionate about reproducible science, meaningful data analysis, ecosystem and host health, and collaborating with scientists who share these values. Amy is the recipient of an NIH Outstanding Investigator Award, a University of Washington Outstanding Faculty Teaching Award, and a University of Washington Outstanding Faculty Mentor Award.
TIME Friday October 28, 2022 at 2:00 PM - 3:00 PM
LOCATION A230, Technological Institute map it
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CONTACT Tierney Acott tierney.acott@northwestern.edu
CALENDAR McCormick - Civil and Environmental Engineering (CEE)