Program for Human Microbiome Research, Medical University of South Carolina, 135 Cannon Street MSC 200, Charleston, SC 29425, USA.
Biomedical Informatics Center, Medical University of South Carolina, 135 Cannon Street MSC 200, Charleston, SC 29425, USA.
Genes (Basel). 2019 Jul 11;10(7):524. doi: 10.3390/genes10070524.
Many important exposure-response relationships, such as diet and weight, can be influenced by intermediates, such as the gut microbiome. Understanding the role of these intermediates, the mediators, is important in refining cause-effect theories and discovering additional medical interventions (e.g., probiotics, prebiotics). Mediation analysis has been at the heart of behavioral health research, rapidly gaining popularity with the biomedical sciences in the last decade. A specific analytic challenge is being able to incorporate an entire 'omics assay as a mediator. To address this challenge, we propose a hypothesis testing framework for multivariate omnibus distance mediation analysis (MODIMA). We use the power of energy statistics, such as partial distance correlation, to allow for analysis of multivariate exposure-mediator-response triples. Our simulation results demonstrate the favorable statistical properties of our approach relative to the available alternatives. Finally, we demonstrate the application of the proposed methods in two previously published microbiome datasets. Our framework adds a new tool to the toolbox of approaches to the integration of 'omics big data.
许多重要的暴露-反应关系，如饮食和体重，可能受到中间因素的影响，如肠道微生物组。了解这些中间因素（调解者）的作用对于完善因果理论和发现额外的医学干预措施（如益生菌、益生元）非常重要。调解分析一直是行为健康研究的核心，在过去十年中，它在生物医学科学中迅速流行起来。一个特定的分析挑战是能够将整个“组学”分析作为一个调解者纳入其中。为了解决这个挑战，我们提出了一种用于多元整体距离调解分析（MODIMA）的假设检验框架。我们利用能量统计的强大功能，如偏距相关，来允许对多元暴露-调解者-反应三重奏进行分析。我们的模拟结果表明，与现有替代方法相比，我们的方法具有良好的统计特性。最后，我们在两个以前发表的微生物组数据集上展示了所提出方法的应用。我们的框架为整合“组学”大数据的方法工具箱添加了一个新工具。
