title: Piecewise-constant and low-rank approximation for identification of recurrent copy number variations.
abstract: BACKGROUND
  The post-genome era sees urgent need for more novel approaches to extracting useful information from the huge amount of genetic data. The identification of recurrent copy number variations (CNVs) from array-based comparative genomic hybridization (aCGH) data can help understand complex diseases, such as cancer. Most of the previous computational methods focused on single-sample analysis or statistical testing based on the results of single-sample analysis. Finding recurrent CNVs from multi-sample data remains a challenging topic worth further study.
  
  
  RESULTS
  We present a general and robust method to identify recurrent CNVs from multi-sample aCGH profiles. We express the raw dataset as a matrix and demonstrate that recurrent CNVs will form a low-rank matrix. Hence, we formulate the problem as a matrix recovering problem, where we aim to find a piecewise-constant and low-rank approximation (PLA) to the input matrix. We propose a convex formulation for matrix recovery and an efficient algorithm to globally solve the problem. We demonstrate the advantages of PLA compared with alternative methods using synthesized datasets and two breast cancer datasets. The experimental results show that PLA can successfully reconstruct the recurrent CNV patterns from raw data and achieve better performance compared with alternative methods under a wide range of scenarios.
  
  
  METHODS
  The MATLAB code is available at http://bioinformatics.ust.hk/pla.zip.
authors.affiliation: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon and Department of Computer Science and Institute of Theoretical and Computational Study, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Zhou
authors.affiliation: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon and Department of Computer Science and Institute of Theoretical and Computational Study, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China.
authors.forename: Jiming
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon and Department of Computer Science and Institute of Theoretical and Computational Study, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Wan
authors.affiliation: Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon and Department of Computer Science and Institute of Theoretical and Computational Study, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China.
authors.forename: Weichuan
authors.initials: W
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D055028:Comparative Genomic Hybridization / Q000379:methods*; D056915:DNA Copy Number Variations; D005260:Female; D006801:Humans