Wavelet footprints and Sparse Bayesian Learning for DNA copy number change analysis

被引:0
作者
Pique-Regi, Roger [1 ,2 ]
Tsau, En-Shuo [1 ]
Ortega, Antonio [1 ]
Seeger, Robert [2 ]
Asgharzadeh, Shahab [2 ]
机构
[1] Univ Southern Calif, Inst Signal & Image Proc, Dept Elect Engn, Viterbi Sch Engn, Los Angeles, CA 90089 USA
[2] Univ Southern Calif, Childrens Hosp Los Angles, KECK School Med, Dept Pediat,Div Hematol Oncol, Los Angeles, CA USA
来源
2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL I, PTS 1-3, PROCEEDINGS | 2007年
关键词
DNA copy number; piece-wise constant; detection; denoising; sparse Bayesian learning;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Alterations in the number of DNA copies are very common in tumor cells and may have a very important role in cancer development and progression. New array platforms provide means to analyze the copy number by comparing the hybridization intensities of thousands of DNA sections along the genome. However, detecting and locating the copy number changes from this data is a very challenging task due to the large amount of biological processes that affect hybridization and cannot be controlled. This paper proposes a new technique that exploits the key characteristic that the DNA copy number is piecewise-constant along the genome. First, wavelet footprints are used to obtain a basis for representing the DNA copy number that is maximally sparse in the number of copy number change points. Second, Sparse Bayesian Learning is applied to infer the copy number changes from noisy array probe intensities. Results demonstrate that Sparse Bayesian Learning has better performance than matching pursuits methods for this high coherence dictionary. Finally, our results are also shown to be very competitive in performance as compared to state-of-the-art methods for copy number detection.
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页码:353 / +
页数:2
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