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

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.

journal_name

Biometrics

journal_title

Biometrics

authors

Safo SE,Ahn J,Jeon Y,Jung S

doi

10.1111/biom.12886

subject

Has Abstract

pub_date

2018-12-01 00:00:00

pages

1362-1371

issue

4

eissn

0006-341X

issn

1541-0420

journal_volume

74

pub_type

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