Bioinformatics Vol. 19 no. 6 2003
Pages 694-703
© 2003 Oxford University Press
Statistical tests for identifying differentially expressed genes in time-course microarray experiments
1 Department of Statistics,
Seoul National University, Seoul, Korea
2 Department of Applied Mathematics, Sejong
University, Seoul, Korea
3 Department of Biochemistry, Hanyang University College of
Medicine, Seoul, Korea
Received on July 17, 2002
; revised on September 7, 2002
; accepted on December 11, 2002
Motivation: Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course experiments in which gene expression is monitored over time, we are interested in testing gene expression profiles for different experimental groups. However, no sophisticated analytic methods have yet been proposed to handle time-course experiment data.
Results: We propose a statistical test procedure based on the ANOVA model to identify genes that have different gene expression profiles among experimental groups in time-course experiments. Especially, we propose a permutation test which does not require the normality assumption. For this test, we use residuals from the ANOVA model only with time-effects. Using this test, we detect genes that have different gene expression profiles among experimental groups. The proposed model is illustrated using cDNA microarrays of 3840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells.
Availability: A set of programs written by R will be electronically sent upon request.
Contact: tspark{at}stats.snu.ac.kr
* To whom correspondence should be addressed.
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