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.

Introduction. Microarray analysis is a complex procedure, and using R and the associated BioConductor packages can be a daunting task. In this tutorial we are going to show you how to install R and BioConductor, download a dataset from GEO, normalise it, do some quality control checks and finally analyse it to find differentially expressed genes. Before we can test for differential expression, we have to specify which groups of samples we want to compare. We could just point at a useful grouping variable among the phenotypic data, but Limma requires a slightly more precise formulation that will limma powers differential expression analyses for RNA-sequencing and microarray studies The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Ritchie, Matthew E., Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, and Gordon K. Smyth. 2015. "limma powers Hi, Instead of using limma on cel files downloaded from GEO, I now have gene expression data for differential expression of genes under various condition (8 replicates) in txt file in following format: The data for this tutorial comes from a Nature Cell Biology paper, EGF-mediated induction of Mcl-1 at the switch to lactation is essential for alveolar cell survival (Fu et al. 2015). Both the raw data (sequence reads) and processed data (counts) can be downloaded from Gene Expression Omnibus database (GEO) under accession number GSE60450. Investigates data from gene expression experiments. limma contains features for handling complex experimental designs and for information borrowing