Differential Expression Analysis for Paired RNA-Seq Data

• Activity Number: 72
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Lisa M. Chung*+ and John Ferguson and Hongyu Zhao
• Companies: Yale University and Yale University and Yale University
• Keywords: RNA-seq ; differential expression ; paired data ; paired count data ; bayesian modeling

Abstract

RNA-Seq technology measures the absolute abundance of transcripts by generating sequence reads and counting their frequencies across different biological states. To identify differentially expressed genes between two classes, it is important to consider experimental design structure as well as the distributional property of the data. The current approaches for detecting DE assume independence of the samples in the treatment and control group. However, in many real RNA sequencing studies, the expression data is obtained as multiple pairs - i.e samples pre- vs. post-treatment for the same individual. Here, we propose a novel Bayesian hierarchical approach to modeling count data that separately accounts for the within and between individual variability from a paired data structure. We apply our model using both simulated data and two real RNA Seq data sets.