scfgsea

What problem does it solve?

Quickly identifying enriched biological pathways in single-cell datasets by analyzing ranked genes derived from differential expression, enabling interpretable pathway-level insights.

Core Features & Use Cases

• Fast GSEA on single-cell data using the fgsea R package.
• Rank genes by differential expression between cell groups and compute enrichment scores, p-values, and NES.
• Generate publication-ready visualizations and exportable results for downstream reporting.
• Use cases include cluster interpretation, pathway-driven hypotheses in disease vs control studies, and subgroup analyses with custom gene sets.

Quick Start

Run scfgsea on a ranked gene list derived from Seurat clusters to identify enriched pathways.