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Activity Number: 328 - Statistical Methods for Multi-Omics Data Integration
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #313782
Title: Distance-Based Analysis for Longitudinal Multi-Omic Data
Author(s): Anna Plantinga*
Companies: Williams College
Keywords: microbiome; distance-based; longitudinal; host genomics

Microbiome association studies are growing increasingly complex, often combining several omics data sources (host gene expression, metabolomics, and others) and following subjects across time. These study design characteristics attempt to provide some understanding of the mechanism of association between the microbiome and host phenotypes, but few statistical methods are available for testing associations between the microbiome and other structured high-dimensional data types longitudinally. We evaluate and compare several approaches to testing longitudinal associations between the microbiota and genomic features, such as host genetic variants or gene expression, by leveraging existing methodology. We then propose a combined kernel RV approach. The method is tested on both simulated and real microbiome data.

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

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