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Bioinformatics Advance Access originally published online on August 23, 2006
Bioinformatics 2006 22(21):2650-2657; doi:10.1093/bioinformatics/btl451
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

MARD: a new method to detect differential gene expression in treatment-control time courses

Chao Cheng 1,2,*, Xiaotu Ma 1, Xiting Yan 1, Fengzhu Sun 1 and Lei M. Li 1,2,*

1 Molecular and Computational Biology Program, Department of Biological Sciences, Computational Biology, University of Southern California Los Angeles CA, USA
2 Department of Mathematics, University of Southern California Los Angeles CA, USA

*To whom correspondence should be addressed.

Motivation: Characterizing the dynamic regulation of gene expression by time course experiments is becoming more and more important. A common problem is to identify differentially expressed genes between the treatment and control time course. It is often difficult to compare expression patterns of a gene between two time courses for the following reasons: (1) the number of sampling time points may be different or hard to be aligned between the treatment and the control time courses; (2) estimation of the function that describes the expression of a gene in a time course is difficult and error-prone due to the limited number of time points. We propose a novel method to identify the differentially expressed genes between two time courses, which avoids direct comparison of gene expression patterns between the two time courses.

Results: Instead of attempting to ‘align’ and compare the two time courses directly, we first convert the treatment and control time courses into neighborhood systems that reflect the underlying relationships between genes. We then identify the differentially expressed genes by comparing the two gene relationship networks. To verify our method, we apply it to two treatment-control time course datasets. The results are consistent with the previous results and also give some new biologically meaningful findings.

Availability: The algorithm in this paper is coded in C++ and is available from http://leili-lab.cmb.usc.edu/yeastaging/projects/MARD/

Contact: lilei{at}usc.edu; chaochen{at}usc.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Received on April 25, 2006; revised on July 27, 2006; accepted on August 17, 2006

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