Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Research article

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

Xiaoli Gao1* and Jian Huang23

Author affiliations

1 Department of Mathematics and Statistics, Oakland University, Rochester, MI 48309, USA

2 Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA 52246, USA

3 Department of Biostatistics, University of Iowa, Iowa City, IA 52246, USA

For all author emails, please log on.

Citation and License

BMC Genomics 2010, 11:517  doi:10.1186/1471-2164-11-517

Published: 25 September 2010

Abstract

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.