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

Xiaoli Gao, Jian Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


Background: Deletions and amplifications of the human genomic DNA copy number are the causes 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 many diseases. 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 with high-density tiling oligonucleotide arrays. Since complicated biological and experimental processes are often associated with these platforms, data can be potentially contaminated by outliers.Results: We propose a penalized LAD regression model with the adaptive fused lasso penalty for detecting CNV. This method contains robust properties and incorporates both the spatial dependence and sparsity of CNV into the analysis. Our simulation studies and real data analysis indicate that the proposed method can correctly detect the numbers and locations of the true breakpoints while appropriately controlling the false positives.Conclusions: The proposed method has three advantages for detecting CNV change points: it contains robustness properties; incorporates both spatial dependence and sparsity; and estimates the true values at each marker accurately.
Original languageEnglish
Article number517
JournalBMC Genomics
Issue number1
Publication statusPublished - 25 Sept 2010
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Genetics


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