Human Molecular Genetics, 2002, Vol. 11, No. 17 1977-1985
© 2002 Oxford University Press
A regression-based method to identify differentially expressed genes in microarray time course studies and its application in an inducible Huntington's disease transgenic model
1Division of Public Health Sciences and 2Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
Received March 12, 2002; Accepted June 7, 2002
Time-course studies with microarray technologies provide enormous potential for exploring underlying mechanisms of biological phenomena in many areas of biomedical research, but the large amount of gene expression data generated by such studies also presents great challenges to data analysis. Here we introduce a regression-based statistical modeling approach that identifies differentially expressed genes in microarray time-course studies. To illustrate this method, we applied it to data generated from an inducible Huntington's disease transgenic model. The regression method accounts for the induction process, incorporates relevant experimental information, and includes parameters that specifically address the research interest: the temporal differences in gene expression profiles between the mutant and control mice over the time course, in addition to heterogeneities that commonly exist in microarray data. Least-squares and estimating equation techniques were used to estimate parameters and variances, and inferences were made based on efficient and robust Z-statistics under a set of well-defined assumptions. A permutation test was also used to estimate the number of false-positives, providing an alternative measurement of statistical significance useful for investigators to make decisions on follow-up studies.
* To whom correspondence should be addressed at: Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, MW-805, Seattle, WA 98109, USA. Tel: +1 2066676927; Fax: +1 2066672437; Email: lzhao{at}fhcrc.org
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  E. Mendez, J. R. Houck, D. R. Doody, W. Fan, P. Lohavanichbutr, T. C. Rue, B. Yueh, N. D. Futran, M. P. Upton, D. G. Farwell, et al. A Genetic Expression Profile Associated with Oral Cancer Identifies a Group of Patients at High Risk of Poor Survival Clin. Cancer Res., February 15, 2009; 15(4): 1353 - 1361. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. Lohavanichbutr, J. Houck, W. Fan, B. Yueh, E. Mendez, N. Futran, D. R. Doody, M. P. Upton, D. G. Farwell, S. M. Schwartz, et al. Genomewide Gene Expression Profiles of HPV-Positive and HPV-Negative Oropharyngeal Cancer: Potential Implications for Treatment Choices Arch Otolaryngol Head Neck Surg, February 1, 2009; 135(2): 180 - 188. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Chen, E. Mendez, J. Houck, W. Fan, P. Lohavanichbutr, D. Doody, B. Yueh, N. D. Futran, M. Upton, D. G. Farwell, et al. Gene Expression Profiling Identifies Genes Predictive of Oral Squamous Cell Carcinoma Cancer Epidemiol. Biomarkers Prev., August 1, 2008; 17(8): 2152 - 2162. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Steinhoff and M. Vingron Normalization and quantification of differential expression in gene expression microarrays Brief Bioinform, June 1, 2006; 7(2): 166 - 177. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Conesa, M. J. Nueda, A. Ferrer, and M. Talon maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments Bioinformatics, May 1, 2006; 22(9): 1096 - 1102. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Hodges, A. D. Strand, A. K. Aragaki, A. Kuhn, T. Sengstag, G. Hughes, L. A. Elliston, C. Hartog, D. R. Goldstein, D. Thu, et al. Regional and cellular gene expression changes in human Huntington's disease brain Hum. Mol. Genet., March 15, 2006; 15(6): 965 - 977. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Xie, W. Pan, and A. B. Khodursky A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data Bioinformatics, December 1, 2005; 21(23): 4280 - 4288. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Z. Bar-Joseph, G. Gerber, I. Simon, D. K. Gifford, and T. S. Jaakkola Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes PNAS, September 2, 2003; 100(18): 10146 - 10151. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K. A. Serikawa, X. L. Xu, V. L. MacKay, G. L. Law, Q. Zong, L. P. Zhao, R. Bumgarner, and D. R. Morris The Transcriptome and Its Translation during Recovery from Cell Cycle Arrest in Saccharomyces cerevisiae Mol. Cell. Proteomics, March 1, 2003; 2(3): 191 - 204. [Abstract] [Full Text] [PDF]
|
|