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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 9

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Multisample aCGH Data Analysis via Total Variation and Spectral Regularization

Content Provider	ACM Digital Library
Author	Yu, Weichuan Yang, Can Zhao, Hongyu Wan, Xiang Zhou, Xiaowei
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.
Starting Page	230
Ending Page	235
Page Count	6
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2012.166
Volume Number	10
Issue Number	1
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-01-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Convex optimization,CNV,aCGH,total variation,spectral regularization
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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