Simultaneous analysis of multiple data types in pharmacogenomic studies using weighted sparse canonical correlation analysis.

ABSTRACT: Variation in drug response results from a combination of factors that include differences in gender, ethnicity, and environment, as well as genetic variation that may result in differences in mRNA and protein expression. This article presents two integrative analytic approaches that make use of both genome-wide SNP and mRNA expression data available on the same set of subjects: a step-wise integrative approach and a comprehensive analysis using sparse canonical correlation analysis (SCCA). In addition to applying standard SCCA, we present a novel modification of SCCA which allows different weighting for the various pair-wise relationships in the SCCA. These integrative approaches are illustrated with both simulated data and data from a pharmacogenomic study of the drug gemcitabine. Results

SUBMITTER: Chalise P 

PROVIDER: S-EPMC3394856 | biostudies-literature | 2012 Jul-Aug

REPOSITORIES: biostudies-literature