RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

• DOI: 10.12688/f1000research.9005.1
• Publication Date: 2016-06-17T10:40:09Z
• Authors: Charity W. Law; Monther Alhamdoosh; Shian Su; Gordon K. Smyth; Matthew E. Ritchie
• Keywords: Bioconductor; R package

The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at gene-level, typical analysis involves pre-processing, exploratory analysis, differential expression testing pathway results obtained informing future experiments validation studies. In this workflow article, we from mouse mammary gland, demonstrating use popular edgeR package import, organise, filter normalise data, followed by limma its voom method, linear modelling empirical Bayes moderation assess perform gene set testing. This pipeline further enhanced Glimma which enables interactive exploration so that individual samples genes can be examined user. complete offered these three packages highlights ease researchers turn raw an experiment into biological insights using Bioconductor.