Differential Gene Expression analysis with R (microarray and NGS data)
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.
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”.
- 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
- 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