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Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments. BMC Bioinformatics 2005 Apr 25;6:106



Pubmed ID


Pubmed Central ID




Scopus ID

2-s2.0-25444533041   40 Citations


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.

Author List

Liu H, Tarima S, Borders AS, Getchell TV, Getchell ML, Stromberg AJ


Sergey S. Tarima PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Analysis of Variance
Artificial Intelligence
Cluster Analysis
Computational Biology
Computer Graphics
Computer Simulation
Data Interpretation, Statistical
Databases, Genetic
Gene Expression Profiling
Gene Expression Regulation
Gene Library
Models, Theoretical
Olfactory Receptor Neurons
Oligonucleotide Array Sequence Analysis
Pattern Recognition, Automated
Regression Analysis
Reproducibility of Results
Sequence Alignment
Sequence Analysis, DNA
Time Factors
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