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Åke Borg

Åke Borg

Principal investigator

Åke Borg

Robust smooth segmentation approach for array CGH data analysis

Author

  • J Huang
  • Arief Gusnanto
  • Kathleen O'Sullivan
  • Johan Staaf
  • Åke Borg
  • Yudi Pawitan

Summary, in English

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, 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.

Department/s

  • Breastcancer-genetics

Publishing year

2007

Language

English

Pages

2463-2469

Publication/Series

Bioinformatics

Volume

23

Issue

18

Document type

Journal article

Publisher

Oxford University Press

Topic

  • Bioinformatics and Systems Biology

Status

Published

ISBN/ISSN/Other

  • ISSN: 1367-4803