2015) and consists of three cell populations (basal, luminal progenitor (LP) and mature luminal (ML)) sorted from the mammary glands of female virgin mice, each profiled in triplicate. The experiment analysed in this workflow is from Sheridan et al. This analysis is enhanced through the use of interactive graphics from the Glimma package (Su and Ritchie 2016), that allows for a more detailed exploration of the data at both the sample and gene-level than is possible using static R plots. In this article, we describe an edgeR - limma workflow for analysing RNA-seq data that takes gene-level counts as its input, and moves through pre-processing and exploratory data analysis before obtaining lists of differentially expressed (DE) genes and gene signatures. 2015) offer a well-developed suite of statistical methods for dealing with this question for RNA-seq data. 2015) available from the Bioconductor project (Huber et al. The edgeR (Robinson, McCarthy, and Smyth 2010) and limma packages (Ritchie et al. RNA-sequencing (RNA-seq) has become the primary technology used for gene expression profiling, with the genome-wide detection of differentially expressed genes between two or more conditions of interest one of the most commonly asked questions by researchers. 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. 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. 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. 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. The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project.
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