Differential Gene Expression Using R

Who: Meeta Mistry

When: April 27, 2016

Times: 9:15am-12:15pm PST

UC Davis Location: DSI Space, Shields Library, UC Davis Campus

Contact: Please contact Jessica Mizzi with any questions


For RNA-seq data, the strategy taken is to count the number of reads that fall into annotated genes and to perform statistical analysis on the table of counts to discover quantitative changes in expression levels between experimental groups. Easy, right? Not exactly.

1. We have integer counts and not continuous measurements. Data are not normally distributed, so statistical methods we applied to microarray data don’t work here.

2. Replication levels in designed RNAseq experiments tend to be modest, often not much more than two or three. As a result, there is a need for statistical methods that perform well in small sample situations.

3. There is a dependence of variance on the mean (which changes with increasing number of replicates).

Solution: Appropriate modeling of the mean-variance relationship in DGE data is important for making inferences about differential expression. Employing methods which assess the mean-variance relationship to help with the problem of estimating biological variability for experiments with a small number of replicates.

In this module, learners will use R Statistical Software to walk-through activities designed to compare the performance of different tools (edgeR, DESeq2, limma-voom) for differential expression analysis of RNA-Seq data, and how the mean-variance relationship is addressed in datasets with increasing number of replicates.

This workshop will be taught remotely and broadcast to our classroom via Google Hangouts on Air. We will have helpers in our local room to facilitate the lesson. The lesson will also be streamed to YouTube and saved there for viewing at a later time. You only need to register to this event if you plan on coming to a classroom.

Computer and workshop requirements

Attendees will need to bring a computer with an Internet connection.

Please install R Statistical Software before the workshop by following the instructions below.

Installation instructions

Windows and OS X:

Download R Studio (select the desktop, open source addition, which is free).

The R packages required for this workshop can be installed from Bioconductor. There are three packages required: edgeR, limma, and DESeq2. To install, please follow the instructions below:

In the R console you will need to first source the BioC installation script:


If this is your first time using Bioconductor you will first need to get the latest install of Bioconductor using:


To install a package:


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