Multisample aCGH data analysis via total variation and spectral regularization

Xiaowei Zhou, Can YANG, Xiang WAN, Hongyu Zhao, Weichuan Yu

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

DNA copy number variation (CNV) accounts for a large proportion of genetic variation. One commonly used approach to detecting CNVs is array-based comparative genomic hybridization (aCGH). Although many methods have been proposed to analyze aCGH data, it is not clear how to combine information from multiple samples to improve CNV detection. In this paper, we propose to use a matrix to approximate the multisample aCGH data and minimize the total variation of each sample as well as the nuclear norm of the whole matrix. In this way, we can make use of the smoothness property of each sample and the correlation among multiple samples simultaneously in a convex optimization framework. We also developed an efficient and scalable algorithm to handle large-scale data. Experiments demonstrate that the proposed method outperforms the state-of-the-art techniques under a wide range of scenarios and it is capable of processing large data sets with millions of probes.

Original languageEnglish
Article number6517420
Pages (from-to)230-235
Number of pages6
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume10
Issue number1
DOIs
Publication statusPublished - 2013

Scopus Subject Areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

User-Defined Keywords

  • aCGH
  • CNV
  • convex optimization
  • spectral regularization
  • total variation

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