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
Biometricsjournal_title
Biometricsauthors
Safo SE,Ahn J,Jeon Y,Jung Sdoi
10.1111/biom.12886subject
Has Abstractpub_date
2018-12-01 00:00:00pages
1362-1371issue
4eissn
0006-341Xissn
1541-0420journal_volume
74pub_type
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