Simultaneous Covariance Inference for Multimodal Integrative Analysis. Issue 531 (2nd July 2020)

• Record Type: Journal Article
• Authors: Xia, Yin; Li, Lexin; Lockhart, Samuel N.; Jagust, William J.
• Is Part Of: Journal of the American Statistical Association. Volume 115:Issue 531(2020)
• Page Start: 1279
• Page End: 1291
• Publication Date: 2020-07-02
• Subjects: Extreme value distribution -- False discovery control -- Minimax rate optimality -- Multimodal integrative analysis -- Multiple testing -- Positron emission tomography; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2019.1623040
• Languages: English
• ISSNs: 0162-1459

Abstract

Abstract: 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.