Pathway enrichment with gProfiler, clusterProfiler and fgsea

Learn to perform pathway enrichment analysis using overrepresentation analysis, functional class scoring, and topology-based methods, including defining gene lists, backgrounds, and gene sets, with practical R guidance.

Analyze differential expression from rna-seq data comparing lesional and healthy psoriatic skin with CPM-normalized counts; perform gene set enrichment with gProfiler, clusterProfiler, and fgsea using HGNC symbols.

Learn to perform overrepresentation analysis with gProfiler and clusterProfiler, annotate differential expression using logFC and FDR thresholds, visualize with volcano plots, and prepare up/down gene lists and background for ORA.

Gather gene sets for pathway enrichment using MCdb R package, focusing on Homo sapiens and C2 pathways and C5 gene ontology processes. Filter by size and prepare for overrepresentation analysis.

Run an overrepresentation analysis with gProfiler using up or down gene lists, a background, chosen sources, with FDR correction and custom annotated domain scope, highlighting immune response pathways.

Apply clusterProfiler for overrepresentation analysis, formatting gene sets, running enrichment on upregulated and downregulated genes, using 0.05 p value and BH FDR, and compare with gProfiler results.

Compare ora results from cluster profiler and g profiler with a venn diagram and term reformats to reveal 35% shared pathways and 426 common terms.

This lecture shows building a ranked gene list from degs using log fold change and FDR, removing duplicates, and applying clusterProfiler for functional class scoring on the ranked vector.

Prepare pathway gene lists by splitting gene sets, run fgsea with parallel processing and a ranked vector, and interpret leading-edge genes and significant results (p-adjusted < 0.05).

Compare functional class coding analysis results with cluster profiler functional class scoring using a Venn diagram, showing about 93% overlap between GFC and cluster profiler results, then proceed to plotting.

Combine upregulated and downregulated gProfiler results into a ggplot bubble plot, filter term size, compute gene ratio, and visualize top pathways with facet-wrapped, colored bubbles.

Create an enrichment bubble plot for clusterProfiler's ora, visualize upregulated and downregulated gene enrichment, derive the source column with gsub, and export the plot to pdf.

Create an enrichment bubble plot for clusterProfiler's functional class scoring, using normalized enrichment score to label upregulated and downregulated pathways, clean names, and select top 30 by NES.

Plot fgsea results, including a dot plot of top enriched terms and a running enrichment score for pathways such as interferon gamma, plus a heat map of leading-edge genes.

Plot leading edge genes tied to the interferon gamma response by converting Ensembl IDs to HGNC symbols, z-score normalizing, and visualizing an annotated heatmap to distinguish lesional skin from controls.

Learn how to ensure reproducibility in overrepresentation analysis by setting seeds, saving workspace and session info, and exporting results as heat maps and PDFs.