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Activity Number: 442 - Disease Prediction, Statistical Methods for Genetic Epidemiology and Mis
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #318407
Title: Microbial Trend Analysis for Common Dynamic Trend, Group Comparison, and Classification in Longitudinal Microbiome Study
Author(s): Chan Wang* and Jiyuan Hu and Martin J. Blaser and Huilin Li
Companies: NYU Langone Health and New York University Grossman School of Medicine and Rutgers University, Center for Advanced Biotechnology and Medicine and New York University Grossman School of Medicine
Keywords: Classification; Dynamic; High dimensionality; Longitudinal microbiome; Phylogenetic tree

The human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving the disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time. In this paper, we propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects at the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different between groups; 3) classify an individual subject based on its longitudinal microbial profiling. Both simulations and real data analyses demonstrate the superb performance of the proposed MTA framework.

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

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