The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

Open Access
Research Article


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.


Gene Expression Cluster Analysis Growth Condition Expression Data Single Gene 
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Copyright information

© J. Z. Song et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  1. 1.Department of Animal and Avian Science, 2413 Animal Science CenterUniversity of MarylandCollege ParkUSA
  2. 2.Department of Microbiology and Infectious Diseases, and Department of Biochemistry and Molecular Biology, Health Sciences CentreUniversity of CalgaryCalgaryCanada
  3. 3.Department of MathematicsUniversity of CalgaryCalgaryCanada

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