title: Wavelet-based functional clustering for patterns of high-dimensional dynamic gene expression.
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 .
authors: Department of Dentistry, Seoul National University, Seoul, Republic of Korea.
authors: Bong-Rae
authors: BR
authors: Kim
authors: Timothy
authors: T
authors: McMurry
authors: Wei
authors: W
authors: Zhao
authors: Rongling
authors: R
authors: Wu
authors: Arthur
authors: A
authors: Berg
mesh_terms: D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D013997:Time Factors