Adaptive elastic-net sparse principal component analysis for pathway association testing.

ABSTRACT: Pathway or gene set analysis has become an increasingly popular approach for analyzing high-throughput biological experiments such as microarray gene expression studies. The purpose of pathway analysis is to identify differentially expressed pathways associated with outcomes. Important challenges in pathway analysis are selecting a subset of genes contributing most to association with clinical phenotypes and conducting statistical tests of association for the pathways efficiently. We propose a two-stage analysis strategy: (1) extract latent variables representing activities within each pathway using a dimension reduction approach based on adaptive elastic-net sparse principal component analysis; (2) integrate the latent variables with the regression modeling framework to analyze studies wi

SUBMITTER: Chen X 

PROVIDER: S-EPMC3215429 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature