From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline

Yunshun Chen; Aaron T L Lun; Gordon K Smyth

doi:10.12688/f1000research.8987.2

From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline

F1000Res. 2016 Jun 20:5:1438. doi: 10.12688/f1000research.8987.2. eCollection 2016.

Authors

Yunshun Chen¹, Aaron T L Lun², Gordon K Smyth³

Affiliations

¹ The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia; Department of Medical Biology, The University of Melbourne, Victoria, 3010, Australia.
² Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.
³ The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia; Department of Mathematics and Statistics, The University of Melbourne, Victoria, 3010, Australia.

Abstract

In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for profiling gene expression. One of the most common aims of RNA-seq profiling is to identify genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This article demonstrates a computational workflow for the detection of DE genes and pathways from RNA-seq data by providing a complete analysis of an RNA-seq experiment profiling epithelial cell subsets in the mouse mammary gland. The workflow uses R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, including alignment of read sequences, data exploration, differential expression analysis, visualization and pathway analysis. Read alignment and count quantification is conducted using the Rsubread package and the statistical analyses are performed using the edgeR package. The differential expression analysis uses the quasi-likelihood functionality of edgeR.

Keywords: R software; RNA sequencing; gene expression; molecular pathways.

Grants and funding

This work was supported by the National Health and Medical Research Council (Fellowship 1058892 and Program 1054618 to G.K.S, Independent Research Institutes Infrastructure Support to the Walter and Eliza Hall Institute) and by a Victorian State Government Operational Infrastructure Support Grant.