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