Introduction to RNA-seq analysis in R

  • Sheffield - 13th February 2020
  • 09:30am - 5pm
  • Bartolome House, Seminar Room EG03

Course Materials

Course Data

Please download and un-zip this file containing the data for the course

Instructor Markdown

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In this workshop, you will be learning how to analyse RNA-seq count data, using R. This will include reading the data into R, quality control and performing differential expression analysis and gene set testing, with a focus on the DESEq2 analysis workflow. You will learn how to generate common plots for analysis and visualisation of gene expression data, such as boxplots and heatmaps. You will also be learning how alignment and counting of raw RNA-seq data can be performed in R. This workshop is aimed at biologists interested in learning how to perform differential expression analysis of RNA-seq data when reference genomes are available.


***Some basic R knowledge is assumed (and is essential). Without it, you will struggle on this course. If you are not familiar with the R statistical programming language we strongly encourage you to work through an introductory R course before attempting these materials. If you have done and R course before we recommend reading our R crash course before attending to refresh your knowledge. You can also attend our Data Analysis Essentials for Researchers if you have not used R previously.

Please also make sure you have experience of using the Unix command line. For example by attending our Introduction to the Command Line for Bioformatics course


This course is based on the course RNAseq analysis in R originally prepared by Combine Australia and delivered on May 11/12th 2016 in Carlton. The course was then modified by Cancer Research Uk Cambridge Institute and delivered as part of the CRUK Functional Genomics Autumn School

Who should attend this course?

Researchers in life sciences who want to get an appreciation for the computational steps involved in RNA-seq analysis, and how to execute best-practice RNA-seq workflows in R.

Objectives:- After this course you should be able to:

  • Design properly your RNA-Seq experiments considering advantages and limitations of RNA-seq assays
  • Assess the quality of your datasets
  • Know what tools are available in Bioconductor for RNA-seq data analysis and understand the basic object-types that are utilised
  • Produce a list of differentially expressed genes from an RNA-seq experiment

Aims:- During this course you will learn about:

  • RNA sequencing technology and considerations on experiment design
  • Sources of variation in RNA-seq data
  • Differential expression analysis using edgeR and DEseq2
  • Annotation resources in Bioconductor
  • Identifying over-represented gene sets among a list of differentially expressed genes

Software installation

You will need to bring an internet-enabled laptop to the course and install the latest versions of both R and RStudio before coming to the course


Install R by downloading and running this .exe file from CRAN. Also, please download and run the RStudio installer for Windows. Note that if you have separate user and admin accounts, you should run the installers as administrator (right-click on .exe file and select “Run as administrator” instead of double-clicking). Otherwise problems may occur later, for example when installing R packages.


Install R by downloading and running this .pkg file from CRAN. Also, please download and run the RStudio installer for Mac


You can download the binary files for your distribution from CRAN. Or you can use your package manager (e.g. for Debian/Ubuntu run sudo apt-get install r-base and for Fedora run sudo yum install R). Also, please download and run the RStudio installer.


  • Dr. Mark Dunning, Bioinformatics Core Director
  • Katjusa Koler, PhD Student
  • Niamh Errington, PhD Student


We also have a docker container that has all the software packages and data pre-installed. First install docker and then launch the container with:-

docker run -p 8787:8787 markdunning/rna-seq-r

In your web browser http://localhost:8787 should show an RStudio session. The username and password are both rstduio.


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