Tail-Based Robust Test to Detect Gene Differential Expression in RNA-Seq Data

• Activity Number: 353
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Jiong Chen* and Jianhua Hu
• Companies: MD Anderson Cancer Center and MD Anderson Cancer Center
• Keywords: Quantile ; Expected shortfall ; Biomarker discovery ; RNA sequencing

Abstract

RNA-sequencing data has been massively produced in biomedical research. Such data at the exon-level are usually heavily tailed and correlated. Conventional statistical tests on mean or median difference for differential expression detection could suffer from low power when a between-group difference (e.g., disease or not) shows only in the upper or lower tail of the distribution of gene expression measurements. We propose a tail-based test derived from quantile regression adjusting for covariates, which makes comparisons between groups in terms of a specific distribution area rather than a single location. The new test also accounts for within-sample dependence among the exons through a specified correlation structure. Through extensive simulation studies, we show the new test is more powerful and robust in terms of decision making compared to several popular mean or median effect tests. An application of lung adenocarcinoma demonstrates the promise of the proposed method in terms of biomarker discovery.