BOOKED OUT: RNA-seq Differential Gene Expression analysis in R – 5 June
This workshop introduces the analysis of RNA-seq count data using R. This will include reading the data into R, quality control and preprocessing, and performing differential expression analysis and gene set testing, with a focus on the limma-voom analysis workflow.
This workshop does not cover the alignment of RNA-seq data to a reference genome in order to generate count data. For an introduction to that topic, refer to our Galaxy-based RNA-seq workshops.
At the end of this workshop you will be able to:
- Read RNA-seq count data into R
- Produce diagnostic plots of the data, carry out quality checks, and recognise common issues
- Perform differential gene expression analysis between two groups to obtain a set of differentially expressed genes
- Annotate differentially expressed genes
- Perform simple gene-set testing analysis
This workshop assumes some knowledge of the R programming language. If you are not familiar with R, it is compulsory that you work through an introductory R course before you attend this workshop. Introductory R courses are offered periodically by Research Platforms here. Self-guided introductory R courses are also made available by Software Carpentry. We suggest following the course materials at http://swcarpentry.github.io/r-novice-gapminder/ up to and including Topic 9 (Vectorisation).
NB: is workshop has been developed from existing workshop materials produced by COMBINE. Attendance is not recommended if you have already participated in a COMBINE workshop on this same topic.
Participants must bring their own laptops, and the required software packages must be pre-installed:
- Download and install R from https://cran.r-project.org/. The latest version is recommended.
- Download and install RStudio from https://www.rstudio.com/products/rstudio/download/. The free version is all you need.
- Install R packages by opening RStudio and copying the following commands into your console:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(c("tidyverse", "edgeR", "Glimma", "gplots", "RColorBrewer", "org.Mm.eg.db", "BiasedUrn"))
- Check you can load the following libraries without issue: