Detection of DNA Copy Number Variations Using Penalized Least Absolute Deviations Regression

نویسندگان

  • Xiaoli Gao
  • Jian Huang
چکیده

Deletions and amplifications of the human genomic DNA copy number are the cause of numerous diseases such as various forms of cancer. Therefore, the detection of DNA copy number variations (CNV) is important in understanding the genetic basis of disease. Various techniques and platforms have been developed for genome-wide analysis of DNA copy number, such as array-based comparative genomic hybridization (aCGH) and high-resolution mapping using high-density tiling oligonucleotide arrays. Since complicated biological and experimental processes are involved in these platforms, data can be contaminated by outliers. Inspired by the robustness property of the LAD regression, we propose a penalized LAD regression with the fused lasso penalty for detecting CNV. This method incorporates the spatial dependence and sparsity of CNV into the analysis and is computationally feasible for high-dimensional array-based data. We evaluate the proposed method using simulation studies, which indicate that it can correctly detect the numbers and locations of the true breakpoints while controlling the false positives appropriately. We demonstrate the proposed method on two real data examples.

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تاریخ انتشار 2007