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Biostatistics Advance Access originally published online on July 14, 2009
Biostatistics 2009 10(4):667-679; doi:10.1093/biostatistics/kxp022
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Identifying temporally differentially expressed genes through functional principal components analysis

Xueli Liu*

Division of Biostatistics, City of Hope, Duarte, CA 91010-3000, USA xuliu{at}coh.org

Mark C. K. Yang

Department of Statistics, University of Florida, Gainesville, Florida 32611-8545, USA

* To whom correspondence should be addressed.

Time course gene microarray is an important tool to identify genes with differential expressions over time. Traditional analysis of variance (ANOVA) type of longitudinal investigation may not be applicable because of irregular time intervals and possible missingness due to contamination in microarray experiments. Functional principal components analysis is proposed to test hypotheses in the change of the mean curves. A permutation test under a mild assumption is used to make the method more robust. The proposed method outperforms the recently developed extraction of differential gene expression and a 2-way mixed effects ANOVA under reasonable gene expression models in simulation. Real data on transcriptional profiles of blood cells microarray from treated and untreated individuals were used to illustrate this method.

Keywords: False discovery rate; Functional hypothesis testing; Functional principal components analysis; Permutation test; Time course gene expression profiles

Received June 6, 2008; revised May 27, 2009; accepted for publication June 22, 2009.


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