An Efficient Optimization Algorithm for Structured Sparse CCA, with Applications to eQTL Mapping

Xi Chen*, Han Liu

*Corresponding author for this work

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)3-26
Number of pages24
JournalStatistics in Biosciences
Volume4
Issue number1
DOIs
StatePublished - May 1 2012

Keywords

  • eQTL mapping
  • Genome-wide association study
  • Group structure
  • Network structure
  • Optimization algorithm
  • Sparse CCA
  • Structured sparsity

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)

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