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Open Access Highly Accessed Methodology article

Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments

Hua Liu1*, Sergey Tarima1, Aaron S Borders2, Thomas V Getchell24, Marilyn L Getchell34 and Arnold J Stromberg1

Author Affiliations

1 Department of Statistics, University of Kentucky, Lexington, KY 40506, USA

2 Department of Physiology, University of Kentucky, Lexington, KY 40536, USA

3 Department of Anatomy and Neurobiology, University of Kentucky, Lexington, KY 40536, USA

4 Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA

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BMC Bioinformatics 2005, 6:106  doi:10.1186/1471-2105-6-106

Published: 25 April 2005

Abstract

Background

Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage of the fact that time is a continuous variable, and existing clustering methods often group biologically unrelated genes together.

Results

We propose a quadratic regression method for identification of differentially expressed genes and classification of genes based on their temporal expression profiles for non-cyclic short time-course microarray data. This method treats time as a continuous variable, therefore preserves actual time information. We applied this method to a microarray time-course study of gene expression at short time intervals following deafferentation of olfactory receptor neurons. Nine regression patterns have been identified and shown to fit gene expression profiles better than k-means clusters. EASE analysis identified over-represented functional groups in each regression pattern and each k-means cluster, which further demonstrated that the regression method provided more biologically meaningful classifications of gene expression profiles than the k-means clustering method. Comparison with Peddada et al.'s order-restricted inference method showed that our method provides a different perspective on the temporal gene profiles. Reliability study indicates that regression patterns have the highest reliabilities.

Conclusion

Our results demonstrate that the proposed quadratic regression method improves gene discovery and pattern recognition for non-cyclic short time-course microarray data. With a freely accessible Excel macro, investigators can readily apply this method to their microarray data.