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Bio: Dr. Gen Li is an associate professor in the Department of Biostatistics at the University of Michigan. Before joining Michigan, he was an Assistant Professor and Sanford Bolton Faculty Scholar in the Department of Biostatistics at Columbia University. Dr. Li’s research interests lie in the development of new statistical methods for complex biomedical data, including multi-way tensor array data, multi-view data, and compositional data, with applications to omics studies.
Abstract: The presence-absence (P/A) based analysis has gained popularity in microbiome research due to its robustness against measurement errors and sensitivity to rare taxa. In this work, we develop a new logic regression framework to associate binary P/A data with clinical outcomes. The method can effectively leverage the tree structure among taxa to adaptively select important features at different taxonomic levels. The proposed method outperforms existing methods in synthetic examples and provides new insightful findings in an oral microbiome study
