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Bioinformatics Advance Access published online on August 23, 2006
Bioinformatics, 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
Received April 25, 2006
Revised July 27, 2006
Accepted August 17, 2006

Article

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

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

1 Computational Biology, University of Southern California Los Angeles, CA, USA; Mathematics, University of Southern California Los Angeles, CA, USA
2 Computational Biology, University of Southern California Los Angeles, CA, USA

* To whom correspondence should be addressed.
Lei Li, E-mail: lilei{at}usc.edu


  Abstract

Motivation: Characterizing the dynamic regulation of gene expression by time course experiments is becoming more and more widely used. This makes it a frequently met problem 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 data sets. 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 available from http://leili-lab.cmb.usc.edu/yeastaging/projects/MARD/.

Associate Editor: John Quackenbush
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