Method
An optimization framework for unsupervised identification of rare copy number variation from SNP array data
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* Corresponding author: Thomas LaFramboise thomas.laframboise@case.edu
1 Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
2 Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
3 Department of Genetics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
4 Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH, 44195, USA
Genome Biology 2009, 10:R119 doi:10.1186/gb-2009-10-10-r119
Published: 23 October 2009Abstract
Copy number variants (CNVs) have roles in human disease, and DNA microarrays are important tools for identifying them. In this paper, we frame CNV identification as an objective function optimization problem. We apply our method to data from hundreds of samples, and demonstrate its ability to detect CNVs at a high level of sensitivity without sacrificing specificity. Its performance compares favorably with currently available methods and it reveals previously unreported gains and losses.