Nucleic Acids Research Advance Access published online on February 1, 2007
Nucleic Acids Research, doi:10.1093/nar/gkl730
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Methods Online |
Exploiting noise in array CGH data to improve detection of DNA copy number change
Department of Electrical and Computer Engineering, University of Florida Gainesville, FL 32611, 1Biosieve 1026 Springfield Drive, Campbell, CA 95008 and 2Department of Medicine, Mount Sinai School of Medicine One Gustave L. Levy Place, New York, NY 10029, USA
*To whom correspondence should be addressed. Tel: +1 2122412883; Fax: +1 2128492643; Email: weijia.zhang{at}mssm.edu *Correspondence may also be addressed to Jian-Bo Gao. Tel: +1 3523920918; Fax: +1 3523920044; Email: gao{at}ece.ufl.edu
Received April 14, 2006. Revised September 20, 2006. Accepted September 21, 2006.
Developing effective methods for analyzing array-CGH data to detect chromosomal aberrations is very important for the diagnosis of pathogenesis of cancer and other diseases. Current analysis methods, being largely based on smoothing and/or segmentation, are not quite capable of detecting both the aberration regions and the boundary break points very accurately. Furthermore, when evaluating the accuracy of an algorithm for analyzing array-CGH data, it is commonly assumed that noise in the data follows normal distribution. A fundamental question is whether noise in array-CGH is indeed Gaussian, and if not, can one exploit the characteristics of noise to develop novel analysis methods that are capable of detecting accurately the aberration regions as well as the boundary break points simultaneously? By analyzing bacterial artificial chromosomes (BACs) arrays with an average 1 Mb resolution, 19 k oligo arrays with the average probe spacing <100 kb and 385 k oligo arrays with the average probe spacing of about 6 kb, we show that when there are aberrations, noise in all three types of arrays is highly non-Gaussian and possesses long-range spatial correlations, and that such noise leads to worse performance of existing methods for detecting aberrations in array-CGH than the Gaussian noise case. We further develop a novel method, which has optimally exploited the character of the noise, and is capable of identifying both aberration regions as well as the boundary break points very accurately. Finally, we propose a new concept, posteriori signal-to-noise ratio (p – SNR), to assign certain confidence level to an aberration region and boundaries detected.