A robust penalized method for the analysis of noisy DNA copy number data

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Xiaoli Gao, Associate Professor (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: BackgroundDeletions and amplifications of the human genomic DNA copy number are the causes ofnumerous diseases, such as, various forms of cancer. Therefore, the detection of DNAcopy number variations (CNV) is important in understanding the genetic basis of manydiseases. Various techniques and platforms have been developed for genome-wideanalysis of DNA copy number, such as, array-based comparative genomic hybridization(aCGH) and high-resolution mapping with high-density tiling oligonucleotide arrays.Since complicated biological and experimental processes are often associated with theseplatforms, data can be potentially contaminated by outliers.ResultsWe propose a penalized LAD regression model with the adaptive fused lasso penalty fordetecting CNV. This method contains robust properties and incorporates both the spatialdependence and sparsity of CNV into the analysis. Our simulation studies and real dataanalysis indicate that the proposed method can correctly detect the numbers and locationsof the true breakpoints while appropriately controlling the false positives.ConclusionsThe proposed method has three advantages for detecting CNV change points: it containsrobustness properties; incorporates both spatial dependence and sparsity; and estimatesthe true values at each marker accurately.

Additional Information

BMC Genomics
Language: English
Date: 2010
DNA, Human genome, Comparative genomic hybridization , Oligonucleotides, Regression analysis

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