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


BACKGROUND
The algorithm in this paper is coded in C++ and is available from http://leili-lab.cmb.usc.edu/yeastaging/projects/MARD/
Cheng
Chao
C
Molecular and Computational Biology Program, Department of Biological Sciences, Computational Biology, University of Southern California Los Angeles, CA, USA. chaochen@usc.edu
Ma
Xiaotu
X
Yan
Xiting
X
Sun
Fengzhu
F
Li
Lei M
LM
D000465:Algorithms; D002118:Calcium / Q000008:administration & dosage; D002453:Cell Cycle / Q000187:drug effects / Q000502:physiology; D018797:Cell Cycle Proteins / Q000378:metabolism*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000166:cytology* / Q000187:drug effects / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D012984:Software; D016559:Tacrolimus / Q000008:administration & dosage; D013997:Time Factors
D018797:Cell Cycle Proteins; D029701:Saccharomyces cerevisiae Proteins; D002118:Calcium; D016559:Tacrolimus