scfgsea
Performs fast Gene Set Enrichment Analysis (GSEA) on single-cell data using fgsea R package. Identifies enriched biological pathways by ranking genes based on differential expression between cell groups. Generates enrichment scores, significance metrics, and publication-ready visualizations.
npx skills add majiayu000/claude-skill-registry --skill scfgsea-pwwang-immunopipe-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ScFGSEA Process Configuration ## Purpose Performs fast Gene Set Enrichment Analysis (GSEA) on single-cell data using fgsea R package. Identifies enriched biological pathways by ranking genes based on differential expression between cell groups. Generates enrichment scores, significance metrics, and publication-ready visualizations. ## When to Use - After clustering: Functional interpretation of cluster differences - Pathway analysis: Identify biological processes driving cell type differentiation - Comparative analysis: Compare gene expression patterns between groups (e.g., disease vs control) - Subgroup analysis: Run GSEA on metadata subsets (diagnosis, treatment, etc.) - TCR integration: Analyze pathway enrichment in TCR-selected clones/clusters ## Configuration Structure ### Process Enablement ```toml [ScFGSEA] cache = true ``` ### Input Specification ```toml [ScFGSEA.in] srtobj = ["SeuratClustering"] # or "ScRepCombiningExpression" ``` ### Environment Variables ```toml [ScFGSEA.envs] # Core parameters ncores = 1 # Parallel cores assay = "RNA" # Assay to use subset = "seurat_clusters %in% c('c1', 'c2')" # Subset cells # Grouping parameters group_by = "seurat_clusters" # Column
- Purpose
- When to Use
- Configuration Structure
- Process Enablement
- Input Specification
- Environment Variables
- Gene Set Databases
- MSigDB Collections
- Custom GMT Files
- Ranking Methods
- Configuration Examples
- Minimal Configuration
- Standard Hallmark Analysis
- KEGG Pathways with Custom Thresholds
What does the scfgsea skill do?
Performs fast Gene Set Enrichment Analysis (GSEA) on single-cell data using fgsea R package. Identifies enriched biological pathways by ranking genes based on differential expression between cell groups. Generates enrichment scores, significance metrics, and publication-ready visualizations.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scfgsea-pwwang-immunopipe-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.