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

• Published in: BMC bioinformatics Vol. 6; no. 1; p. 106
• Main Authors: Liu, Hua, Tarima, Sergey, Borders, Aaron S, Getchell, Thomas V, Getchell, Marilyn L, Stromberg, Arnold J
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 25.04.2005; BioMed Central; BMC
• Subjects: Algorithms; Analysis of Variance; Animals; Artificial Intelligence; Cluster Analysis; Computational Biology - methods; Computer Graphics; Computer Simulation; Data Interpretation, Statistical; Databases, Genetic; Gene Expression Profiling - methods; Gene Expression Regulation; Gene Library; Genomics - methods; Humans; Methodology; Models, Theoretical; Olfactory Receptor Neurons - metabolism; Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated; Probability; Regression Analysis; Reproducibility of Results; Sequence Alignment; Sequence Analysis, DNA; Software; Time Factors
• ISSN: 1471-2105; 1471-2164; 1471-2105; 1471-2164
• DOI: 10.1186/1471-2105-6-106

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. 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. 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.