Differentially expressed genes don't tell the whole story. In this hands-on workshop, you will learn how to move beyond individual gene lists and uncover coordinated biological pathways and processes using Gene Set Enrichment Analysis (GSEA) with the clusterProfiler R/Bioconductor package.

By the end of the session, participants will have a reusable, well-structured and annotated R script they can adapt to their own datasets and will learn to:

1. Explain the principles of GSEA and distinguish it from over-representation analysis (ORA), including how ranked gene lists and enrichment scores are computed.

2. Prepare input data for GSE including ranking genes by log₂ fold change, and handling duplicate gene symbols.

3. Select and load appropriate gene sets from MSigDB using the msigdbr package.

4. Run GSEA using the GSEA() function with appropriate parameter choices

5. Interpret GSEA output, including Normalized Enrichment Score (NES), FDR q-values, and leading-edge genes.

6. Generate and customize visualizations including dot plots and enrichment score plots using enrichplot.

Prerequisites:

Basic familiarity with R and RStudio.

Having a laptop with the latest version of RStudio installed.