Simultaneous Covariance Inference for Multimodal Integrative Analysis

Yin Xia, Lexin Li, Samuel N. Lockhart and William J. Jagust

Journal of the American Statistical Association, 2020, vol. 115, issue 531, 1279-1291

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.

Date: 2020
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DOI: 10.1080/01621459.2019.1623040

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