This hands-on workshop will introduce users of the R software environment to the specific skills and applications used in the analysis of microarray and next-generation sequencing (NGS) data.

Practical exercises will include quality control and normalisation of data for differential gene expression, and linking genomic information to external public datasets.

Recommended participants

Biologists and bioinformaticians wishing to use R for RNA expression analysis. Prior expertise with R and the command line interface is required, to a level equivalent of that provided by the QFAB workshop “Introduction to R”.

What will I learn?

During this course you will learn about:

Pre-processing and quality control of microarray and RNA-Seq data
The use of R packages for the identification of differentially-expressed genes from expression data
Systems biology interpretation of gene lists using pathway analysis
Integration of expression and genome data with Ensembl databases
After this course you should be able to:

Import Affymetrix CEL files to R as data objects
Carry out standard QC tests on microarray and RNA-Seq datasets
Use the limma-voom R package to produce lists of differentially expressed genes between pairs of samples
Identify over-represented gene ontology categories in gene lists using the GOStats package