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Simultaneous Covariance Inference for Multimodal Integrative Analysis

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.

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Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION [JASA]; Vol.115 No.531 September 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 1279 - 1291

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

MULTIPLE TESTING
EXTREME VALUE DISTRIBUTION
POSITRON EMISSION TOMOGRAPHY
FALSE DISCOVERY CONTROL
MINIMAX RATE OPTIMALITY
MULTIMODAL INTEGRATIVE ANALYSIS

Info Detail Spesifik

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Pernyataan Tanggungjawab

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

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