scientific-pathway-enrichment

Cross-database pathway enrichment is performed by applying ORA and GSEA analyses across KEGG, Reactome, GO, WikiPathways, and Pathway Commons. You provide a ranked gene list, and the pipeline integrates results into downloadable CSVs and visualizations.

ORA (Over-Representation Analysis) and GSEA (Gene Set Enrichment Analysis) are both applied to your gene list to identify enriched pathways. The pipeline runs both methods across five major pathway resources to reconcile inconsistent annotations and produce unified functional signals.

Yes, calculating pathway enrichment requires Python with SciPy, Statsmodels, Pandas, and NumPy. Optionally, gseapy can be used to execute the GSEA and ORA analyses within the cross-database pipeline.

Yes, you can compare pathway activity across KEGG, Reactome, GO, WikiPathways, and Pathway Commons. The workflow unifies ORA and GSEA analyses to reconcile inconsistent annotations and prioritize targets.

Cross-database enrichment results are exported as downloadable CSV files and visualizations. The pipeline integrates the outputs from ORA and GSEA to generate figures for quick interpretation and reporting.

Running both ORA and GSEA across five major pathway resources helps reconcile inconsistent database annotations. This integrated approach validates functional signals in your ranked gene list more robustly than using a single method.