NDLI: Sparse Group Selection on Fused Lasso Components for Identifying Group-Specific DNA Copy Number Variations

Content Provider	IEEE Xplore Digital Library
Author	Ze Tian Huanan Zhang Rui Kuang
Copyright Year	2012
Abstract	Detecting DNA copy number variations (CNVs) from arrayCGH or genotyping-array data to correlate with cancer outcomes is crucial for understanding the molecular mechanisms underlying cancer. Previous methods either focus on detecting CNVs in each individual patient sample or common CNVs across all the patient samples. These methods ignore the discrepancies introduced by the heterogeneity in the patient samples, which implies that common CNVs might only be shared within some groups of samples instead of all samples. In this paper, we propose a latent feature model that couples sparse sample group selection with fused lasso on CNV components to identify group-specific CNVs. Assuming a given group structure on patient samples by clinical information, sparse group selection on fused lasso (SGS-FL) identifies the optimal latent CNV components, each of which is specific to the samples in one or several groups. The group selection for each CNV component is determined dynamically by an adaptive algorithm to achieve a desired sparsity. Simulation results show that SGS-FL can more accurately identify the latent CNV components when there is a reliable underlying group structure in the samples. In the experiments on arrayCGH breast cancer and bladder cancer datasets, SGS-FL detected CNV regions that are more relevant to cancer, and provided latent feature weights that can be used for better sample classification.
Starting Page	665
Ending Page	674
File Size	391190
Page Count	10
File Format	PDF
ISBN	9781467346498
ISSN	15504786
DOI	10.1109/ICDM.2012.35
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-10
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	DNA Optimization Probes Cancer Arrays Matrix decomposition Feature extraction DNA copy number variations sparse group learning fused lasso group lasso
Content Type	Text
Resource Type	Article

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