Xia Yin, Li Lexin, Lockhart Samuel N, Jagust William J
Department of Statistics, School of Management, Fudan University, Shanghai, China.
Department of Biostatistics and Epidemiology, Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA.
J Am Stat Assoc. 2020;115(531):1279-1291. doi: 10.1080/01621459.2019.1623040. Epub 2019 Jun 28.
Multimodal integrative analysis fuses different types of data collected on the same set of experimental subjects. It is becoming a norm in many branches of scientific research, such as multi-omics and multimodal neuroimaging analysis. In this article, we address the problem of simultaneous covariance inference of associations between multiple modalities, which is of a vital interest in multimodal integrative analysis. Recognizing that there are few readily available solutions in the literature for this type of problem, we develop a new simultaneous testing procedure. It provides an explicit quantification of statistical significance, a much improved detection power, as well as a rigid false discovery control. Our proposal makes novel and useful contributions from both the scientific perspective and the statistical methodological perspective. We demonstrate the efficacy of the new method through both simulations and a multimodal positron emission tomography study of associations between two hallmark pathological proteins of Alzheimer's disease.
多模态综合分析融合了在同一组实验对象上收集的不同类型数据。它正在成为许多科研分支中的一种常态，比如多组学和多模态神经影像分析。在本文中，我们解决多模态之间关联的同时协方差推断问题，这在多模态综合分析中至关重要。认识到文献中针对这类问题几乎没有现成的解决方案，我们开发了一种新的同时检验程序。它提供了统计显著性的明确量化、显著提高的检测能力以及严格的错误发现控制。我们的提议从科学视角和统计方法视角都做出了新颖且有用的贡献。我们通过模拟以及一项关于阿尔茨海默病两种标志性病理蛋白之间关联的多模态正电子发射断层扫描研究，证明了新方法的有效性。
