Joint Principal Trend Analysis for Longitudinal High-dimensional Data

Biometrics - Tập 74 Số 2 - Trang 430-438 - 2018
Yuping Zhang1,2,3, Zhengqing Ouyang4,5,6
1Center for Quantitative Medicine, University of Connecticut Health Center , Farmington, Connecticut , U.S.A.
2Department of Statistics, University of Connecticut , Storrs, Connecticut , U.S.A.
3Institute for Systems Genomics, Institute for Collaboration on Health, Intervention, and Policy, CT Institute of the Brain and Cognitive Sciences, University of Connecticut , Storrs, Connecticut , U.S.A.
4Department of Biomedical Engineering, Institute for Systems Genomics, University of Connecticut , Storrs, Connecticut , U.S.A.
5Department of Genetics and Genome Sciences, University of Connecticut Health Center , Farmington, Connecticut , U.S.A.
6The Jackson Laboratory for Genomic Medicine , Farmington, Connecticut , U.S.A.

Tóm tắt

Summary We consider a research scenario motivated by integrating multiple sources of information for better knowledge discovery in diverse dynamic biological processes. Given two longitudinal high-dimensional datasets for a group of subjects, we want to extract shared latent trends and identify relevant features. To solve this problem, we present a new statistical method named as joint principal trend analysis (JPTA). We demonstrate the utility of JPTA through simulations and applications to gene expression data of the mammalian cell cycle and longitudinal transcriptional profiling data in response to influenza viral infections.

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Biometrics

Tập 74 Số 2

430-438

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