High-throughput sequencing technology has enabled researchers to profile microbial communities from a variety of environments, but analysis of multivariate taxon count data remains challenging. We develop a Bayesian nonparametric (BNP) regression model with zero inflation to analyse multivariate count data from microbiome studies. A BNP approach flexibly models microbial associations with covariates, such as environmental factors and clinical characteristics. The model produces estimates for probability distributions which relate microbial diversity and differential abundance to covariates, and facilitates community comparisons beyond those provided by simple statistical tests. We compare the model to simpler models and popular alternatives in simulation studies, showing, in addition to these additional community-level insights, it yields superior parameter estimates and model fit in various settings. The model's utility is demonstrated by applying it to a chronic wound microbiome data set and a Human Microbiome Project data set, where it is used to compare microbial communities present in different environments.

中文翻译：

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零膨胀多元计数数据的贝叶斯非参数分析及其在微生物组研究中的应用

高通量测序技术使研究人员能够分析各种环境中的微生物群落，但多变量分类单元计数数据的分析仍然具有挑战性。我们开发了零通胀的贝叶斯非参数 (BNP) 回归模型来分析微生物组研究的多变量计数数据。BNP 方法灵活地模拟微生物与协变量的关联，例如环境因素和临床特征。该模型产生概率分布的估计，将微生物多样性和差异丰度与协变量联系起来，并促进除简单统计测试之外的群落比较。我们将该模型与模拟研究中更简单的模型和流行的替代方案进行比较，结果表明，除了这些额外的社区层面的见解之外，它在各种设置下都能产生出色的参数估计和模型拟合。该模型的实用性通过将其应用于慢性伤口微生物组数据集和人类微生物组项目数据集来证明，用于比较不同环境中存在的微生物群落。