B0461
Title: Measurement error modeling on zero-inflated and overdispersed microbiome sequence count data
Authors: Tianying Wang - Colorado State University (United States) [presenting]
Abstract: In microbiome studies, it is of interest to use a sample from a population of microbes, such as the gut microbiota community, to estimate the population proportion of these taxa. However, due to biases introduced in sampling and preprocessing steps, these observed taxa abundances may not reflect true taxa abundance patterns in the ecosystem. Repeated measures, including longitudinal study designs, may be potential solutions to mitigate the discrepancy between observed abundances and true underlying abundances. Yet, widely observed zero-inflation and over-dispersion issues can distort downstream statistical analyses aiming to associate taxa abundances with covariates of interest. A zero-inflated Poisson Gamma (ZIPG) model framework is presented to address these aforementioned challenges. From a perspective of measurement errors, the discrepancy is accommodated between observations and truths by decomposing the mean parameter in Poisson regression into a true abundance level and a multiplicative measurement of sampling variability from the microbial ecosystem. Then, a flexible ZIPG model framework is provided by connecting both the mean abundance and the variability of abundances to different covariates, and valid statistical inference procedures are built for both parameter estimation and hypothesis testing.
