Simultaneous Covariance Inference for Multimodal Integrative Analysis.

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 perspe

SUBMITTER: Xia Y 

PROVIDER: S-EPMC8048125 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature