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Multisample aCGH Data Analysis via Total Variation and Spectral Regularization
Jan.-Feb. 2013 (vol. 10 no. 1)
pp. 230-235
Xiaowei Zhou, Dept. of Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Hong Kong, China
Can Yang, Dept. of Biostat., Yale Univ., New Haven, CT, USA
Xiang Wan, Dept. of Comput. Sci., Hong Kong Baptist Univ., Hong Kong, China
Hongyu Zhao, Dept. of Biostat., Yale Univ., New Haven, CT, USA
Weichuan Yu, Dept. of Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Hong Kong, China
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.
Index Terms:
optimisation,bioinformatics,DNA,genetics,genomics,molecular biophysics,data set processing,multisample aCGH data analysis,total variation,spectral regularization,DNA copy number variation,genetic variation,array-based comparative genomic hybridization,bioinformatics,nuclear norm,convex optimization framework,state-of-the-art techniques,Spectral analysis,Optimization,Convex optimization,convex optimization,CNV,aCGH,total variation,spectral regularization
Citation:
Xiaowei Zhou, Can Yang, Xiang Wan, Hongyu Zhao, Weichuan Yu, "Multisample aCGH Data Analysis via Total Variation and Spectral Regularization," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 1, pp. 230-235, Jan.-Feb. 2013, doi:10.1109/TCBB.2012.166
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