The human gut microbiome is one of the fundamental components of our physiology, and exploring the relationship between biological and environmental covariates and the resulting taxonomic composition of a given microbial community is an active area of research. Previously, a Dirichlet–multinomial regression framework has been suggested to model this relationship, but it did not account for any underlying latent group structure. An underlying group structure of guts (such as enterotypes) has been observed across gut microbiome samples in which guts in the same group share similar biota compositions. In the paper, a finite mixture of Dirichlet–multinomial regression models is proposed that accounts for this underlying group structure and to allow for a probabilistic investigation of the relationship between bacterial abundance and biological and/or environmental covariates within each inferred group. Furthermore, finite mixtures of regression models which incorporate the concomitant effect of the covariates on the resulting mixing proportions are also proposed and examined within the Dirichlet–multinomial framework. We utilize the proposed mixture model to gain insight on underlying subgroups in a microbiome data set comprising tumour and healthy samples and the relationships between covariates and microbial abundance in those subgroups.

中文翻译：

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混合使用Dirichlet-多项式回归模型对微生物组数据进行聚类分析

人类肠道微生物组是我们生理学的基本组成部分，探索生物学和环境协变量之间的关系以及给定微生物群落的分类分类组成是一个活跃的研究领域。以前，曾有人建议使用Dirichlet-多项式回归框架对该关系进行建模，但它并未考虑任何潜在的潜在群体结构。在肠道微生物组样本中已观察到肠道的潜在群体结构（例如肠型），其中同一组的肠道共享相似的生物群组成。在纸上 提出了Dirichlet-多项式回归模型的有限混合，该模型考虑了这一潜在的群体结构，并允许对每个推断群体中细菌丰度与生物学和/或环境协变量之间的关系进行概率研究。此外，在Dirichlet-多项式框架内，还提出并检验了回归模型的有限混合，这些模型结合了协变量对混合比例的伴随影响。我们利用提出的混合模型来了解包括肿瘤和健康样本的微生物组数据集中的基础亚组，以及这些亚组中协变量与微生物丰度之间的关系。在Dirichlet-多项式框架内，还提出并检验了回归模型的有限混合，这些模型结合了协变量对最终混合比例的伴随影响。我们利用提出的混合模型来了解包括肿瘤和健康样本的微生物组数据集中的基础亚组，以及这些亚组中协变量与微生物丰度之间的关系。在Dirichlet-多项式框架内，还提出并检验了回归模型的有限混合，这些模型结合了协变量对最终混合比例的伴随影响。我们利用提出的混合模型来了解包括肿瘤和健康样本的微生物组数据集中的基础亚组，以及这些亚组中协变量与微生物丰度之间的关系。