The absolute abundance of bacterial taxa in human host-associated environments plays a critical role in reproductive and gastrointestinal health. However, obtaining the absolute abundance of many bacterial species is typically prohibitively expensive. In contrast, relative abundance data for many species are comparatively cheap and easy to collect (e.g., with universal primers for the 16S rRNA gene). In this paper, we propose a method to jointly model relative abundance data for many taxa and absolute abundance data for a subset of taxa. Our method provides point and interval estimates for the absolute abundance of all taxa. Crucially, our proposal accounts for differences in the efficiency of taxon detection in the relative and absolute abundance data. We show that modeling taxon-specific efficiencies substantially reduces the estimation error for absolute abundance, and controls the coverage of interval estimators. We demonstrate the performance of our proposed method via a simulation study, a study of the effect of HIV acquisition on microbial abundances, and a sensitivity study where we jackknife the taxa with observed absolute abundances.

中文翻译：

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相对和绝对微生物丰度的多视图模型


人类宿主相关环境中细菌类群的绝对丰度对生殖和胃肠道健康起着至关重要的作用。然而，获得许多细菌物种的绝对丰度通常非常昂贵。相比之下，许多物种的相对丰度数据相对便宜且易于收集（例如，使用 16S rRNA 基因的通用引物）。在本文中，我们提出了一种对许多类群的相对丰度数据和类群子集的绝对丰度数据进行联合建模的方法。我们的方法为所有类群的绝对丰度提供点和区间估计。至关重要的是，我们的建议考虑了相对丰度数据和绝对丰度数据中分类单元检测效率的差异。我们表明，对特定分类单元的效率进行建模可以大大减少绝对丰度的估计误差，并控制区间估计器的覆盖范围。我们通过模拟研究、HIV 获取对微生物丰度影响的研究以及敏感性研究（其中我们用观察到的绝对丰度对类群进行折切）展示了我们提出的方法的性能。