Quantifying and Controlling for Sources of Technical Variation and Bias in Longitudinal Microbiome Surveys


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.

Vancouver, British Columbia