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Activity Number: 442 - Methods for Single-Cell and Microbiome Sequencing Data
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
Sponsor: Biometrics Section
Abstract #309666
Title: Statistical Methods for Clustered Microbiome Data
Author(s): Zheng-Zheng Tang* and Guanhua Chen
Companies: University of Wisconsin-Madison and University of Wisconsin-Madison
Keywords: Microbiome; Compositional Data; Clustered Data

Clustered microbiome data have become prevalent in recent years from designs such as longitudinal studies, family studies, and matched case-control studies. The within-cluster dependence compounds the challenge of the microbiome data analysis. Methods that properly accommodate intra-cluster correlation and features of the microbiome data are needed. We develop robust and powerful differential composition tests for clustered microbiome data. We perform extensive simulation studies under commonly-adopted clustered data designs to evaluate the methods. The usefulness of the proposed methods is further demonstrated with a real data application.

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

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