The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data
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A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.
KeywordsGene Expression Cluster Analysis Growth Condition Expression Data Single Gene
- 5.Southern EM: DNA microarrays: history and overview. Methods in Molecular Biology 2001, 170: 1-15.Google Scholar
- 17.Qian J, Dolled-Filhart M, Lin J, Yu H, Gerstein M: Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions. Journal of Molecular Biology 2001, 314(5):1053-1066. 10.1006/jmbi.2000.5219CrossRefGoogle Scholar
- 18.Bar-Joseph Z, Gerber G, Simon I, Gifford DK, Jaakkola TS: Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes. Proceedings of the National Academy of Sciences of the United States of America 2003, 100(18):10146-10151. 10.1073/pnas.1732547100CrossRefMathSciNetMATHGoogle Scholar
- 29.Sokal RR, Michener CD: A statistical method for evaluating systematic relationships. University of Kansaa Science Bulletin 1958, 38: 1409-1438.Google Scholar
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