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