Scfgsea

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

[ScFGSEA]
cache = true

Input Specification

[ScFGSEA.in]
srtobj = ["SeuratClustering"]  # or "ScRepCombiningExpression"

Environment Variables

[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 to compare
ident_1 = "c1"  # First group
ident_2 = "c2"  # Second group (optional: uses all others)
each = "seurat_clusters"  # Split into multiple cases

# Gene set database
gmtfile = "KEGG_2021_Human"  # Default

# Ranking method
method = "s2n"  # signal-to-noise (default)

# fgsea parameters
minsize = 10  # Min gene set size
maxsize = 100  # Max gene set size
top = 20  # Top pathways to plot (< 1 for padj threshold)
eps = 0.0  # P-value boundary

# Visualization
[ScFGSEA.envs.alleach_plots.Heatmap]
plot_type = "heatmap"
group_by = "Diagnosis"

Gene Set Databases

MSigDB Collections

• H (Hallmark): 50 curated, non-redundant gene sets → "MSigDB_Hallmark_2020"
• C2 (Curated): 7,411 gene sets from pathway databases
  • CP:KEGG → "KEGG_2021_Human"
  • CP:REACTOME → "Reactome_Pathways_2024"
  • CP:BIOCARTA → "BioCarta_2016"
  • CP:WIKIPATHWAYS → "WikiPathways_2024_Human"
• C5 (GO): 18,807 Gene Ontology terms
  • BP → "GO_Biological_Process_2025"
  • CC → "GO_Cellular_Component_2025"
  • MF → "GO_Molecular_Function_2025"
• C7 (Immunologic): 2,497 immune-specific signatures (use custom GMT)

Custom GMT Files

gmtfile = "/path/to/custom.gmt"

Format: name<tab>description<tab>gene1,gene2,...

Ranking Methods

• "s2n"/"signal_to_noise": Signal-to-noise ratio (default)
• "abs_s2n"/"abs_signal_to_noise": Absolute signal-to-noise
• "t_test": Student's t-test
• "ratio_of_classes": Fold change (natural scale)
• "diff_of_classes": Difference of means
• "log2_ratio_of_classes": Log2 fold change (recommended for log-scale RNA-seq)

Configuration Examples

Minimal Configuration

[ScFGSEA]
[ScFGSEA.in]
srtobj = ["SeuratClustering"]

[ScFGSEA.envs]
group_by = "seurat_clusters"
ident_1 = "c1"
ident_2 = "c2"

Standard Hallmark Analysis

[ScFGSEA.envs]
gmtfile = "MSigDB_Hallmark_2020"
group_by = "Diagnosis"
ident_1 = "Disease"
ident_2 = "Control"
each = "seurat_clusters"
method = "s2n"
top = 20

KEGG Pathways with Custom Thresholds

[ScFGSEA.envs]
gmtfile = "KEGG_2021_Human"
group_by = "Treatment"
ident_1 = "Treated"
ident_2 = "Untreated"
minsize = 15
maxsize = 200
method = "log2_ratio_of_classes"

GO Biological Process

[ScFGSEA.envs]
gmtfile = "GO_Biological_Process_2025"
group_by = "Diagnosis"
ident_1 = "Colitis"
ident_2 = "Control"
minsize = 10
maxsize = 500
top = 0.05  # padj < 0.05

Immunologic Signatures (Custom GMT)

[ScFGSEA.envs]
gmtfile = "/data/gmt/MSigDB_C7_Immunologic_Signatures.gmt"
group_by = "tissue_type"
ident_1 = "Inflamed"
ident_2 = "Normal"
minsize = 5
maxsize = 150

Multiple Database Comparison

[ScFGSEA.envs.cases.Hallmark]
gmtfile = "MSigDB_Hallmark_2020"
ident_1 = "Disease"
ident_2 = "Control"

[ScFGSEA.envs.cases.KEGG]
gmtfile = "KEGG_2021_Human"
ident_1 = "Disease"
ident_2 = "Control"

TCR Clonotype Analysis

[ScFGSEA.in]
srtobj = ["ScRepCombiningExpression"]

[ScFGSEA.envs]
group_by = "cdr3_clonotype_cluster"
ident_1 = "expanded_clone"
ident_2 = "rest"
gmtfile = "MSigDB_Hallmark_2020"
subset = "CD4"

Common Patterns

Pattern 1: Standard Cluster Comparison

[ScFGSEA.envs]
gmtfile = "MSigDB_Hallmark_2020"
group_by = "seurat_clusters"
ident_1 = "c1"
ident_2 = "c2"

Pattern 2: Disease vs Control with Multiple Clusters

[ScFGSEA.envs]
group_by = "Diagnosis"
ident_1 = "Disease"
ident_2 = "Control"
each = "seurat_clusters"
gmtfile = "KEGG_2021_Human"

Pattern 3: Log2 Fold Change Ranking

[ScFGSEA.envs]
method = "log2_ratio_of_classes"
gmtfile = "MSigDB_Hallmark_2020"

Pattern 4: Stringent Pathway Size Filter

[ScFGSEA.envs]
minsize = 20
maxsize = 150
gmtfile = "Reactome_Pathways_2024"

Pattern 5: P-Value Threshold for Plots

[ScFGSEA.envs]
top = 0.01  # padj < 0.01 only
gmtfile = "MSigDB_Hallmark_2020"

Pattern 6: Custom Metabolic Pathways

[ScFGSEA.envs]
gmtfile = "/data/gmt/KEGG_Metabolism.gmt"
group_by = "Metabolic_State"
ident_1 = "High"
ident_2 = "Low"

Dependencies

• Upstream: SeuratClustering or ScRepCombiningExpression
• Downstream: CellTypeAnnotation, pathway visualization

Validation Rules

• gmtfile: Valid enrichit name or GMT path
• group_by: Valid metadata column
• ident_1/ident_2: Values must exist in group_by
• minsize: ≥ 1, maxsize: > minsize
• top: > 0 or < 1 (padj threshold)
• method: Valid fgsea ranking method

Troubleshooting

Too Few Pathways Enriched

[ScFGSEA.envs]
minsize = 5  # Smaller pathways
maxsize = 500  # Larger pathways
top = 0.1  # Looser threshold
gmtfile = "GO_Biological_Process_2025"  # More gene sets

No Enrichment Results

Causes: Insufficient cells, gene name mismatch, restrictive thresholds Solutions:

[ScFGSEA.envs]
minsize = 10
maxsize = 200
subset = "group_by_count > 10"

Long Computation Time

[ScFGSEA.envs]
minsize = 20
maxsize = 100
gmtfile = "MSigDB_Hallmark_2020"
ncores = 8
subset = "seurat_clusters %in% c('c1', 'c2')"

Gene Name Mismatch

Cause: Human (GENE) vs mouse (Gene), different ID types Solutions:

• Download species-specific GMT from MSigDB
• Check rownames(seurat_object)
• Ensure consistent formatting (uppercase for human)

Best Practices

1. Start with Hallmark for quick, interpretable results
2. Use log2_ratio_of_classes for log-scale RNA-seq data
3. Adjust minsize/maxsize based on database and research question
4. Use multiple databases for comprehensive coverage
5. Verify gene names match between Seurat and GMT files
6. Use each parameter for multiple subgroup comparisons
7. Set top < 1 for p-value-based filtering
8. Validate cell counts before running GSEA
9. Parallelize with ncores for large datasets
10. Cache results when testing visualization parameters

External References

• fgsea: https://bioconductor.org/packages/release/bioc/html/fgsea.html
• MSigDB: https://www.gsea-msigdb.org/gsea/msigdb/
• enrichit: https://pwwang.github.io/enrichit/reference/FetchGMT.html
• GSEA paper: Subramanian et al. 2005, PNAS

Related Processes

• ClusterMarkers: Differential expression (provides ranked genes)
• MarkersFinder: Flexible marker finding with GSEA
• PseudoBulkDEG: Bulk-like DE with GSEA
• ModuleScoreCalculator: Score pathway genes across cells