Bioinformatics Advance Access published online on July 27, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm359
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Robust smooth segmentation approach for array CGH data analysis
1Statistical Laboratory, Department of Statistics, University College Cork, Ireland; 2MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 2SR, United Kingdom; 3Department of Oncology, University Hospital, SE-221 85 Lund, Sweden; 4Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
*To whom correspondence should be addressed. Prof. Yudi Pawitan, E-mail: yudi.pawitan{at}ki.se
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Abstract |
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Motivation: Array comparative genomic hybridization (aCGH) provides a genome-wide technique to screen for copy number alteration. The existing segmentation approaches for analyzing aCGH data are based on modeling data as a series of discrete segments with unknown boundaries and unknown heights. Although the biological process of copy number alteration is discrete, in reality a variety of biological and experimental factors can cause the signal to deviate from a stepwise function. To take this into account, we propose a smooth segmentation (smoothseg) approach.
Methods: To achieve a robust segmentation procedure, we use a doubly-heavy-tailed random-effect model. The first heavy-tailed structure on the errors deals with outliers in the observations, and the second deals with possible jumps in the underlying pattern associated with different segments. We develop a fast and reliable computational procedure based on the iterative weighted least-squares algorithm with band-limited matrix inversion.
Results: Using simulated and real data sets, we demonstrate how smoothseg can aid in identification of regions with genomic alteration and in classification of samples. For the real data sets smoothseg leads to smaller false discovery rate and classification error rate than the circular binary segmentation (CBS) algorithm. In a realistic simulation setting, smoothseg is better than wavelet smoothing and CBS in identification of regions with genomic alterations and better than CBS in classification of samples. For comparative analyses, we demonstrate that segmenting the t statistics performs better than segmenting the data.
Availability: The R package smoothseg to perform smooth segmentation is available from http://www.meb.ki.se/~yudpaw.
Contact: yudi.pawitan{at}ki.se
Associate Editor: Prof. Thomas Lengauer
Received on January 23, 2007; revised on June 12, 2007; accepted on July 9, 2007
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