Introduction to RNA-seq analysis in R

This course will take place in-person in the afternoons of June 13th, June 15th and June 17th at the following venues and time. You will need to attend all three sessions

• Monday 13th June, The Diamond Workroom 1, 13:00 - 17:00
• Wednesday 15th June, Bartolome House, Seminar Room EG03, 13:00 - 17:00
• Friday 17th June, Bartolome House, Seminar Room EG03, 13:00 - 17:00

Overview

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. We recommend reading our R crash course before attending, which should take around 1 hour

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 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

Windows

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.

Mac

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

Linux

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.

Instructors

• Dr. Mark Dunning, Bioinformatics Core Director
• to be confirmed

Timetable (provisional)

• Session 1 Monday 13th June 13:00 - 17:00
  • Importing RNA-seq counts into R
  • Quality assessment of raw counts
  • Checking sources of variation using PCA and clustering
• Session 2 Wednesday 15th June 13:00 - 17:00
  • Differential expression using DESeq2 to generate gene-lists
  • Manipulating and filtering gene-lists
  • Basic plotting of differential-expression results
  • More-advanced designs using DESeq2
• Session 3 Friday 17th June 13:00 - 17:00
  • Advanced plotting with heatmaps
  • Using Bioconductor resources to interrogate Gene Ontologies
  • Identifying over-represented and enriched gene sets

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