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.
Document Type
Article
Publication Date
4-25-2005
Digital Object Identifier (DOI)
http://dx.doi.org/10.1186/1471-2105-6-106
Repository Citation
Liu, Hua; Tarima, Sergey; Borders, Aaron S.; Getchell, Thomas V.; Getchell, Marilyn L.; and Stromberg, Arnold J., "Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments" (2005). Statistics Faculty Publications. 10.
https://uknowledge.uky.edu/statistics_facpub/10
Additional file 1
Notes/Citation Information
Published in BMC Bioinformatics, v. 6, 106.
© 2005 Liu et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.