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Activity Number: 48 - Longitudinal Modeling and Experimental Design for InvestigatingĀ Host Associated Microbiota
Type: Invited
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract #326811
Title: Quantifying and Controlling for Sources of Technical Variation and Bias in Longitudinal Microbiome Surveys
Author(s): Justin D Silverman* and Heather Durand and Sayan Mukherjee and Lawrence A David
Companies: Duke University and Duke University and Duke University and Duke University
Keywords: Microbiome; Longitudinal; Bayesian; Time Series; Experimental Design; State Space

Microbial communities can play important roles in both the health and disease of their hosts. However, measurements of these communities are often confounded by technical variation and bias introduced at a number of stages of sample processing and measurement. Here we develop a flexible class of Bayesian Multinomial-Logistic Normal state space models which explicitly controls for technical variation and bias. Paired with this modeling framework we discuss best practices for experimental design; in particular, the use of technical replicates for quantifying technical variation and calibration curves for measuring bias. We demonstrate our approach through both simulation studies and application to real data.

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

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