Statistical Methods for Genome-Wide Evaluation of Gene Expression Regulation Mechanisms Using TCGA Data

• Activity Number: 558
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Peng Wei*+
• Companies: The University of Texas Health Science Center
• Keywords: gene expression ; copy number variation ; methylation ; microRNA ; mixture model

Abstract

Gene expression data have been widely used for disease diagnosis and prognosis, for example, gene expression signatures for different cancers. Research has shown that the observed expression changes can be driven by many possible mechanisms, such as copy number alternations, point mutations, DNA methylation, and microRNAs. However, it remains unclear the relative contributions of the different mechanisms to genome-wide gene expression regulation. To approach this fundamental problem in biology we propose a statistical framework hinged on novel integration of mixture models and penalized regression methods. We will illustrate the utility of the proposed method using the diverse types of genomic data generated in The Cancer Genome Atlas (TCGA) project for ovarian cancer.