RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
Journal Title
F1000Res
Publication Type
Journal Article
Abstract
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, we 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.
Publisher
F1000
WEHI Research Division(s)
Molecular Medicine; Bioinformatics
PubMed ID
27441086
Open Access at Publisher's Site
http://f1000research.com/articles/5-1408/v1
Rights Notice
Refer to copyright notice on published article.


Creation Date: 2016-08-10 04:12:54
Last Modified: 2016-08-11 10:04:39
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