Normalization and Differential Expression in RNA-Seq

Sandrine Dudoit
University of California, Berkeley (UC Berkeley)
Biostatistics and Statistics

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 (

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