A model-based circular binary segmentation algorithm for the analysis of array CGH data

Fang Han Hsu, Hung I H Chen, Mong Hsun Tsai, Liang Chuan Lai, Chi Cheng Huang, Shih Hsin Tu, Eric Y. Chuang, Yidong Chen

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Background: Circular Binary Segmentation (CBS) is a permutation-based algorithm for array Comparative Genomic Hybridization (aCGH) data analysis. CBS accurately segments data by detecting change-points using a maximal-t test; but extensive computational burden is involved for evaluating the significance of change-points using permutations. A recent implementation utilizing a hybrid method and early stopping rules (hybrid CBS) to improve the performance in speed was subsequently proposed. However, a time analysis revealed that a major portion of computation time of the hybrid CBS was still spent on permutation. In addition, what the hybrid method provides is an approximation of the significance upper bound or lower bound, not an approximation of the significance of change-points itself. Results: We developed a novel model-based algorithm, extreme-value based CBS (eCBS), which limits permutations and provides robust results without loss of accuracy. Thousands of aCGH data under null hypothesis were simulated in advance based on a variety of non-normal assumptions, and the corresponding maximal-t distribution was modeled by the Generalized Extreme Value (GEV) distribution. The modeling results, which associate characteristics of aCGH data to the GEV parameters, constitute lookup tables (eXtreme model). Using the eXtreme model, the significance of change-points could be evaluated in a constant time complexity through a table lookup process. Conclusions: A novel algorithm, eCBS, was developed in this study. The current implementation of eCBS consistently outperforms the hybrid CBS 4× to 20× in computation time without loss of accuracy. Source codes, supplementary materials, supplementary figures, and supplementary tables can be found at http://ntumaps.cgm.ntu.edu.tw/eCBSsupplementary.

Original languageEnglish
Article number394
JournalBMC Research Notes
Volume4
DOIs
Publication statusPublished - 2011
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

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    Hsu, F. H., Chen, H. I. H., Tsai, M. H., Lai, L. C., Huang, C. C., Tu, S. H., Chuang, E. Y., & Chen, Y. (2011). A model-based circular binary segmentation algorithm for the analysis of array CGH data. BMC Research Notes, 4, [394]. https://doi.org/10.1186/1756-0500-4-394