Evaluating the reproducibility or agreement of microbiome measurements is often a crucial step to ensure rigorous downstream analyses in microbiome studies. In this paper, we address this need by developing adaptations of Lin’s concordance correlation coefficient (CCC) tailored to microbiome studies. We introduce a general formulation of the new CCC measures upon the use of a distance function appropriately characterizing the discrepancy between microbiome compositional measurements. We thoroughly study the special cases that adopt the Euclidean distance and Aitchison distance. Our proposals appropriately account for the unique features of microbiome compositional data, including high-dimensionality, dependency among individual relative abundances and the presence of many zeros. We further investigate a practical compound approach to help better understand the sources of data inconsistency. Extensive simulation studies are conducted to evaluate the utility of the proposed methods in realistic scenarios. We also apply the proposed methods to a microbiome validation data set from the Feeding Infants Right.. from the STart (FIRST) study. Our analyses offer useful insight about the extent of data variations resulted from two different experiment procedures as well as their heterogeneous patterns across genera.

中文翻译：

---

基于一致性相关系数评估微生物组测量的可重复性

评估微生物组测量的可重复性或一致性通常是确保微生物组研究中严格的下游分析的关键步骤。在本文中，我们通过开发适应微生物组研究的 Lin 一致性相关系数 (CCC) 来满足这一需求。我们介绍了新的 CCC 措施的一般公式，使用距离函数适当地描述了微生物组组成测量之间的差异。我们深入研究了采用欧氏距离和艾奇逊距离的特殊情况。我们的建议适当地解释了微生物组组成数据的独特特征，包括高维性、个体相对丰度之间的依赖性和许多零的存在。我们进一步研究了一种实用的复合方法，以帮助更好地理解数据不一致的来源。进行了广泛的模拟研究，以评估所提出的方法在现实场景中的效用。我们还将所提出的方法应用于微生物组验证数据集Feeding Infants Right.. 来自 STart (FIRST) 研究。我们的分析提供了关于两个不同实验程序以及它们跨属的异质模式导致的数据变化程度的有用见解。