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Activity Number: 555 - New Statistical Methods for Microbiome Data Analysis
Type: Invited
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: ENAR
Abstract #308041
Title: A Taxonomy-Regularized Regression Paradigm for Microbiome Compositional Data
Author(s): Gen Li*
Companies: Columbia University
Keywords: compositional data; taxonomy; microbiome; equi-sparsity; relative shift; tree structure

The human microbiome plays a critical role in human health and disease. Microbiome data are usually represented as compositions, residing in a simplex that does not admit the standard Euclidean geometry. Furthermore, there exists a tree-structured taxonomic relationship among the microbes, which can be examined through a phylogenetic tree. Existing regression methods are inadequate in modeling compositional data or accounting for the taxonomic structure. To address the issues, we develop a novel taxonomy-regularized relative-shift regression paradigm for compositional data with tree structure. We will directly use compositions as predictors without any transformation, and exploit a tree-guided regularization method to encourage feature aggregation. The new paradigm adaptively determines which organisms and which taxonomic ranks contribute to the outcome. Extensive numerical studies demonstrate the efficacy of the proposed method.

Authors who are presenting talks have a * after their name.

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