Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Open Badges Research article

A comparison of methods for differential expression analysis of RNA-seq data

Charlotte Soneson1* and Mauro Delorenzi12

Author Affiliations

1 Bioinformatics Core Facility, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland

2 Département de formation et recherche, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland

For all author emails, please log on.

BMC Bioinformatics 2013, 14:91  doi:10.1186/1471-2105-14-91

Published: 9 March 2013

Additional files

Additional file 1:

Contains supplementary figures referred to in the text. Here, we also evaluate the effect of selecting different values for the parameters of edgeR and DESeq and evaluate two additional transformation-based methods, and we evaluate the effect of simulating data with different dispersion parameter in the two compared conditions. We also present some comparisons based on data sets with 3 samples per condition. The file also contains information regarding the estimation of the mean and dispersion parameters from real data, and an additional analysis of two real RNA-seq data sets. Finally, it contains sample R code to run the differential expression analysis and estimates of the computational time requirements for the different methods.

Format: PDF Size: 3.4MB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data