Open Access
December 2013 Principal trend analysis for time-course data with applications in genomic medicine
Yuping Zhang, Ronald Davis
Ann. Appl. Stat. 7(4): 2205-2228 (December 2013). DOI: 10.1214/13-AOAS659


Time-course high-throughput gene expression data are emerging in genomic and translational medicine. Extracting interesting time-course patterns from a patient cohort can provide biological insights for further clinical research and patient treatment. We propose principal trend analysis (PTA) to extract principal trends of time-course gene expression data from a group of patients, and identify genes that make dominant contributions to the principal trends. Through simulations, we demonstrate the utility of PTA for dimension reduction, time-course signal recovery and feature selection with high-dimensional data. Moreover, PTA derives new insights in real biological and clinical research. We demonstrate the usefulness of PTA by applying it to longitudinal gene expression data of a circadian regulation system and burn patients. These applications show that PTA can extract interesting time-course trends with biological significance, which helps the understanding of biological mechanisms of circadian regulation systems as well as the recovery of burn patients. Overall, the proposed PTA approach will benefit the genomic medicine research. Our method is implemented into an R-package: PTA (Principal Trend Analysis).


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Yuping Zhang. Ronald Davis. "Principal trend analysis for time-course data with applications in genomic medicine." Ann. Appl. Stat. 7 (4) 2205 - 2228, December 2013.


Published: December 2013
First available in Project Euclid: 23 December 2013

zbMATH: 1283.62126
MathSciNet: MR3161719
Digital Object Identifier: 10.1214/13-AOAS659

Keywords: high dimensional , longitudinal , Principal Component Analysis , principal trend analysis , smooth , sparse , Time-course

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.7 • No. 4 • December 2013
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