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) model framework to address these 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 ZIPG model framework by connecting both the mean abundance and the variability of abundances 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 mean abundance. Supplementary materials for this article are available online.

摘要

在微生物组研究中，使用微生物群体（例如肠道微生物群落）的样本来估计这些分类单元的群体比例是很有意义的。然而，由于采样和预处理步骤中引入的偏差，这些观察到的类群丰度可能无法反映生态系统中真实的类群丰度模式。重复措施，包括纵向研究设计，可能是减轻观察到的丰度与真实的潜在丰度之间差异的潜在解决方案。然而，广泛观察到的零通货膨胀和过度分散问题可能会扭曲旨在将类群丰度与感兴趣的协变量联系起来的下游统计分析。为此，我们提出了零膨胀泊松伽玛（ZIPG）模型框架来解决上述挑战。从测量误差的角度来看，我们通过将泊松回归中的平均参数分解为真实的丰度水平以及对微生物生态系统采样变异性的乘法测量来适应观察结果与事实之间的差异。然后，我们通过将平均丰度和丰度变异性连接到不同的协变量来提供灵活的 ZIPG 模型框架，并为参数估计和假设检验构建有效的统计推断程序。通过全面的模拟研究和实际数据应用，所提出的 ZIPG 方法为区分差异变异性和平均丰度提供了重要的见解。本文的补充材料可在线获取。