BMC Research Notes: 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
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 an 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 the 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.
