Wavelet-based functional clustering for patterns of high-dimensional dynamic gene expression

Bong Rae Kim, Timothy McMurry, Wei Zhao, Rongling Wu, Arthur Berg

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Functional gene clustering is a statistical approach for identifying the temporal patterns of gene expression measured at a series of time points. By integrating wavelet transformations, a power dimension-reduction technique, noisy gene expression data is smoothed and clustered allowing for new patterns of functional gene expression profiles to be identified. We implement the idea of wavelet dimension reduction into the mixture model for gene clustering, aimed to de-noise the data by transforming an inherently high-dimensional biological problem to its tractable low-dimensional representation. As a first attempt of its kind, we capitalize on the simplest Haar wavelet shrinkage technique to break an original signal down into its spectrum by taking its averages and differences and, subsequently, detect gene expression patterns that differ in the smooth coefficients extracted from noisy time series gene expression data. The method is shown to be effective on simulated data and and on recent time course gene expression data. Supplementary Material is available at www.liebertonline.com.

Original languageEnglish (US)
Pages (from-to)1067-1080
Number of pages14
JournalJournal of Computational Biology
Volume17
Issue number8
DOIs
StatePublished - Aug 1 2010

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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