Statistical Issues in the Analysis of Data from RNA-Seq Experiments

• Activity Number: 320
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): David Rocke* and Sharon Aviran and Blythe Durbin-Johnson and Luyao Ruan
• Companies: UC Davis and UC Davis and UC Davis and UC Davis
• Keywords: RNA-Seq ; Gene Expression ; DESeq ; edgeR ; limma voom

Abstract

RNA-Seq data are increasingly used for whole-genome differential mRNA expression analysis in lieu of gene expression arrays such as those from Affymetrix and Illumina. We review commonly used methods for this type of analysis, including DESeq, edgeR, and limma-voom, by placing them within a common framework that allows comparisons of components of the methods as well as of the overall results. We show that many of the commonly used methods are not satisfactory, with most identifying large numbers of genes as differentially expressed even when there are none; these include edgeR, edgeR-robust, DESeq and DESeq. On the other hand, limma-voom maintains p-value control in almost all cases, as does a simple two-sample t-test and also limma with a prior variance stabilizing transformation. This area is still early in its intellectual development and is changing rapidly, so there are substantial contributions that can be made.