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Robust smooth segmentation approach for array CGH data analysis

  • J Huang
  • Arief Gusnanto
  • Kathleen O'Sullivan
  • Johan Staaf
  • Åke Borg
  • Yudi Pawitan
Publishing year: 2007
Language: English
Pages: 2463-2469
Publication/Series: Bioinformatics
Volume: 23
Issue: 18
Document type: Journal article
Publisher: Oxford University Press

Abstract 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.


  • Bioinformatics and Systems Biology


  • ISSN: 1367-4803
Åke Borg
Åke Borg
E-mail: ake [dot] borg [at] med [dot] lu [dot] se

Principal investigator

Oncology and Pathology, MV

+46 46 275 25 52

MV 404 C21B2


Project manager

Familial Breast Cancer



Oncology and Pathology, MV

MV 404 C21C2