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Biostatistics 2005 6(2):211-226; doi:10.1093/biostatistics/kxi004
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© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org.

Denoising array-based comparative genomic hybridization data using wavelets

Li Hsu*

Biostatistics Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, M2-B500, Seattle, WA 98109, USA

Steven G. Self and Douglas Grove

Biostatistics Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, PO Box 19024, Seattle, WA 98109, USA

Tim Randolph

Department of Biostatistics, University of Washington, Box 357232, Seattle, WA 98195, USA

Kai Wang

Department of Biomedical Informatics, Columbia University, 622 West 168th Street, New York, NY 10032, USA

Jeffrey J. Delrow

Genomics, Shared Resources, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, DE-740, Seattle, WA 98109, USA

Lenora Loo and Peggy Porter

Human Biology Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, C1-015, Seattle, WA 98109, USA

* To whom correspondence should be addressed. lih{at}fhcrc.org

Array-based comparative genomic hybridization (array-CGH) provides a high-throughput, high-resolution method to measure relative changes in DNA copy number simultaneously at thousands of genomic loci. Typically, these measurements are reported and displayed linearly on chromosome maps, and gains and losses are detected as deviations from normal diploid cells. We propose that one may consider denoising the data to uncover the true copy number changes before drawing inferences on the patterns of aberrations in the samples. Nonparametric techniques are particularly suitable for data denoising as they do not impose a parametric model in finding structures in the data. In this paper, we employ wavelets to denoise the data as wavelets have sound theoretical properties and a fast computational algorithm, and are particularly well suited for handling the abrupt changes seen in array-CGH data. A simulation study shows that denoising data prior to testing can achieve greater power in detecting the aberrant spot than using the raw data without denoising. Finally, we illustrate the method on two array-CGH data sets.

Keywords: Array-CGH; Denoising; Hidden Markov models; Nonparametric modeling; Threshold; Wavelet analysis


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