Sparse generalized eigenvalue problem with application to canonical correlation analysis for integrative analysis of methylation and gene expression data

Sandra E. Safo, Jeongyoun Ahn, Yongho Jeon, Sungkyu Jung

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We present a method for individual and integrative analysis of high dimension, low sample size data that capitalizes on the recurring theme in multivariate analysis of projecting higher dimensional data onto a few meaningful directions that are solutions to a generalized eigenvalue problem. We propose a general framework, called SELP (Sparse Estimation with Linear Programming), with which one can obtain a sparse estimate for a solution vector of a generalized eigenvalue problem. We demonstrate the utility of SELP on canonical correlation analysis for an integrative analysis of methylation and gene expression profiles from a breast cancer study, and we identify some genes known to be associated with breast carcinogenesis, which indicates that the proposed method is capable of generating biologically meaningful insights. Simulation studies suggest that the proposed method performs competitive in comparison with some existing methods in identifying true signals in various underlying covariance structures.

Original languageEnglish
Pages (from-to)1362-1371
Number of pages10
JournalBiometrics
Volume74
Issue number4
DOIs
Publication statusPublished - 2018 Dec

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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