Join us for the next instalment of the STAGE International Speaker Seminar Series (ISSS) with
Dr. Michael Wu
Fred Hutchinson Cancer Center
Affiliate Professor, Biostatistics
University of Washington
Free Hybrid (In person/Online) Event | Registration Required
Statistics for Keeping your Microbiome Analyses out of the Toilet
Microbiome profiling studies of hundreds to thousands of individuals are being conducted within existing epidemiologic cohorts. Analysis of data from these studies offers comprehensive identification of bacterial taxa related to a plethora of health outcomes. However, key characteristics of these studies (e.g. large sample size) also induce serious statistical challenges, particularly in combination with the difficulties inherent to microbiome data (e.g. high-dimensionality, sparsity, compositionality). Some challenges include accommodating batch effects and robustly identifying taxa related to outcomes. To address these problems, we propose novel batch correction and individual-taxon differential abundance testing frameworks. Our work is based on using two-part zero-inflated quantile regression which makes minimal distributional assumptions while accommodating the zero-inflated nature of the data. We illustrate our work through simulations and application to data from a number of large-scale microbiome studies including the CARDIA cohort and HIV Reanalysis Consortium.
Mike Wu is a Professor in the Biostatistics Program at the Fred Hutchinson Cancer Center and an Affiliate Professor of Biostatistics at the University of Washington.
Mike’s group focuses on developing and applying statistical methods for complex omics data, with a recent focus on the human microbiome. The group has developed the MiRKAT method for conducting community-level microbiome analysis and the SKAT method for analysis of rare genetic variants, which now represent integral components of many analytic pipelines. Recently, the group has focused heavily on the analysis of large-scale microbiome profiling studies and multi-omics analysis, while also pursuing efforts in translational medicine.
Mike is a recipient of the W.J. Youden Award in interlaboratory testing from the American Statistical Association (ASA). He also received the inaugural Robert H. Riffenburgh award from the ASA for his contributions and impact from transferring machine learning approaches to microbiome and human genetics. Mike is an elected Fellow of the ASA and previously chaired the ASA Section on Statistics in Genomics and Genetics.
Mike completed a B.S. in Mathematical and Computational Science at Stanford University and a Ph.D. in Biostatistics from Harvard University. He then started his faculty career in the Department of Biostatistics at UNC Chapel Hill before being recruited to the Fred Hutchinson Cancer Center.
CANSSI Ontario STAGE (STAGE) is a training program in genetic epidemiology and statistical genetics housed at the University of Toronto Dalla Lana School of Public Health. It operates with financial and in-kind support from CANSSI Ontario, an extra-departmental unit in the Faculty of Arts & Science at U of T.
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