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BMCBI
2005

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

9 years 7 months ago
Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments
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...
Hua Liu, Sergey Tarima, Aaron S. Borders, Thomas V
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2005
Where BMCBI
Authors Hua Liu, Sergey Tarima, Aaron S. Borders, Thomas V. Getchell, Marilyn L. Getchell, Arnold J. Stromberg
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