期刊
STATISTICS IN BIOSCIENCES
卷 4, 期 1, 页码 3-26出版社
SPRINGER
DOI: 10.1007/s12561-011-9048-z
关键词
Sparse CCA; Structured sparsity; Group structure; Network structure; Genome-wide association study; eQTL mapping; Optimization algorithm
资金
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1116730, 1332109] Funding Source: National Science Foundation
In this paper we develop an efficient optimization algorithm for solving canonical correlation analysis (CCA) with complex structured-sparsity-inducing penalties, including overlapping-group-lasso penalty and network-based fusion penalty. We apply the proposed algorithm to an important genome-wide association study problem, eQTL mapping. We show that, with the efficient optimization algorithm, one can easily incorporate rich structural information among genes into the sparse CCA framework, which improves the interpretability of the results obtained. Our optimization algorithm is based on a general excessive gap optimization framework and can scale up to millions of variables. We demonstrate the effectiveness of our algorithm on both simulated and real eQTL datasets.
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