An Efficient Optimization Algorithm for Structured Sparse CCA, with Applications to eQTL Mapping
An Efficient Optimization Algorithm for Structured Sparse CCA, with Applications to eQTL Mapping
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DOI：
10.1007/s12561-011-9048-z
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发表时间：
2012-05-01
影响因子：
1
通讯作者：
Liu, Han
中科院分区：
文献类型：
--
作者：
Chen, Xi;Liu, Han
在本文中，我们开发了一个有效的优化算法来解决典型相关分析（CCA）与复杂的结构稀疏诱导惩罚，包括双组套索惩罚和基于网络的融合惩罚。我们将该算法应用于一个重要的全基因组关联研究问题，eQTL定位。我们表明，与高效的优化算法，可以很容易地将丰富的基因之间的结构信息到稀疏CCA框架，这提高了所获得的结果的可解释性。我们的优化算法是基于一个通用的过度间隙优化框架，可以扩展到数百万个变量。我们证明了我们的算法在模拟和真实的eQTL数据集上的有效性。
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.