Normalization and Differential Expression in RNA-Seq

• Activity Number: 574
• Type: Invited
• Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
• Sponsor: WNAR
• Author(s): Sandrine Dudoit*+
• Companies: University of California, Berkeley
• Keywords: RNA-Seq ; Normalization ; Differential expression ; GC-content

Abstract

This talk concerns statistical methods and software for the analysis of RNA abundance by sequencing (RNA-Seq). We first present exploratory data analysis (EDA) approaches for quality assessment/control (QA/QC) of RNA-Seq reads. Next, we propose within-lane normalization methods to adjust for sample-specific gene-level effects such as length and GC-content. We also provide between-lane normalization procedures to account for distributional differences such as sequencing depth. Finally, we consider the quantitation of (differential) gene expression levels using generalized linear models (GLM). This work was motivated by a collaboration with the Sherlock Lab on transcriptome analysis in Saccharomyces. Our exploratory data analysis and normalization methods are implemented in the open-source Bioconductor R package EDASeq (http://www.bioconductor.org).