Dataset Information

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 .

SUBMITTER: Kim BR

PROVIDER: S-EPMC3133835 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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

Kim Bong-Rae BR McMurry Timothy T Zhao Wei W Wu Rongling R Berg Arthur A

Journal of computational biology : a journal of computational molecular cell biology 20100801 8

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 transformin ...[more]

PMID: 20726793

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Project description:BACKGROUND: In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits consistent pattern over a subset of conditions. Conventional clustering algorithms that deal with the entire row or column in an expression matrix would therefore fail to detect these useful patterns in the data. Recently, biclustering has been proposed to detect a subset of genes exhibiting consistent pattern over a subset of conditions. However, most existing biclustering algorithms are based on searching for sub-matrices within a data matrix by optimizing certain heuristically defined merit functions. Moreover, most of these algorithms can only detect a restricted set of bicluster patterns. RESULTS: In this paper, we present a novel geometric perspective for the biclustering problem. The biclustering process is interpreted as the detection of linear geometries in a high dimensional data space. Such a new perspective views biclusters with different patterns as hyperplanes in a high dimensional space, and allows us to handle different types of linear patterns simultaneously by matching a specific set of linear geometries. This geometric viewpoint also inspires us to propose a generic bicluster pattern, i.e. the linear coherent model that unifies the seemingly incompatible additive and multiplicative bicluster models. As a particular realization of our framework, we have implemented a Hough transform-based hyperplane detection algorithm. The experimental results on human lymphoma gene expression dataset show that our algorithm can find biologically significant subsets of genes. CONCLUSION: We have proposed a novel geometric interpretation of the biclustering problem. We have shown that many common types of bicluster are just different spatial arrangements of hyperplanes in a high dimensional data space. An implementation of the geometric framework using the Fast Hough transform for hyperplane detection can be used to discover biologically significant subsets of genes under subsets of conditions for microarray data analysis.

Dataset Information

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

Publications

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

Similar Datasets

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