Identification of Differential Aberrations in Multiple-Sample Array CGH Studies
Article first published online: 9 JUL 2010
© 2010, The International Biometric Society
Volume 67, Issue 2, pages 353–362, June 2011
How to Cite
Wang, H. J. and Hu, J. (2011), Identification of Differential Aberrations in Multiple-Sample Array CGH Studies. Biometrics, 67: 353–362. doi: 10.1111/j.1541-0420.2010.01457.x
- Issue published online: 20 JUN 2011
- Article first published online: 9 JUL 2010
- Received August 2009. Revised April 2010. Accepted April 2010.
- Array CGH;
- Change point;
- Common aberration;
- Copy number aberration;
- Fused lasso;
- Median regression;
Summary Most existing methods for identifying aberrant regions with array CGH data are confined to a single target sample. Focusing on the comparison of multiple samples from two different groups, we develop a new penalized regression approach with a fused adaptive lasso penalty to accommodate the spatial dependence of the clones. The nonrandom aberrant genomic segments are determined by assessing the significance of the differences between neighboring clones and neighboring segments. The algorithm proposed in this article is a first attempt to simultaneously detect the common aberrant regions within each group, and the regions where the two groups differ in copy number changes. The simulation study suggests that the proposed procedure outperforms the commonly used single-sample aberration detection methods for segmentation in terms of both false positives and false negatives. To further assess the value of the proposed method, we analyze a data set from a study that identified the aberrant genomic regions associated with grade subgroups of breast cancer tumors.