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

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

Barcode plot of LIM_MAMMARY_LUMINAL_MATURE_UP (red bars, top of plot) and LIM_MAMMARY_LUMINAL_MATURE_DN (blue bars, bottom of plot) gene sets in the LP versus ML contrast with an enrichment line for each set that shows the relative enrichment of the vertical bars in each part of the plot.

Availability – Code to perform this analysis is available from the Supplementary Materials website at http://bioinf.wehi.edu.au/folders/limmaWorkflow/ and as a Bioconductor workflow from http://www.bioconductor.org/help/workflows/.