Abstract:
: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.
journal_name
Biometricsjournal_title
Biometricsauthors
Wang HJ,Hu Jdoi
10.1111/j.1541-0420.2010.01457.xsubject
Has Abstractpub_date
2011-06-01 00:00:00pages
353-62issue
2eissn
0006-341Xissn
1541-0420pii
BIOM1457journal_volume
67pub_type
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