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

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”.

Syllabus

  • 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

Learning Objectives: 

  • 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