A Flexible Zero-Inflated Poisson-Gamma model with application to microbiome read counts

TL;DR

This paper introduces a flexible Zero-Inflated Poisson-Gamma model tailored for microbiome read counts, effectively addressing zero-inflation and over-dispersion issues to improve analysis of microbial abundance data.

Contribution

It develops a novel ZIPG framework that models both mean abundance and variability with covariates, incorporating measurement error considerations for microbiome data analysis.

Findings

The ZIPG model accurately captures zero-inflation and over-dispersion in microbiome data.

Simulation studies demonstrate improved parameter estimation and hypothesis testing.

Application to real data reveals new biological insights into microbial variability.

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. To this end, we propose a Zero-Inflated Poisson Gamma (ZIPG) framework to address the aforementioned challenges. From a perspective of measurement errors, we accommodate the discrepancy 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, we provide a flexible model by connecting both mean abundance and the variability to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. Through comprehensive simulation studies and real data applications, the proposed ZIPG method provides significant insights into distinguished differential variability and abundance.