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Enrichment Analysis Quick Reference

Quick reference guide for trajectory-based enrichment analysis.

Prerequisites

# Install PyDESeq2
pip install pydeseq2

# Download GSEA CLI tool
# From: https://www.gsea-msigdb.org/gsea/downloads.jsp
# Extract to project root

Quick Start

1. Run Complete Pipeline

python scripts/pipeline_steps/6_enrichment_analysis.py \
    --trajectory_dir data/processed/20251216_synthetic_data/kirc/early_to_late \
    --output_dir data/processed/20251219_enrichment \
    --cancer_type kirc \
    --n_threads 8 \
    --gsea_path ./GSEA_4.3.2/gsea-cli.sh \
    --pathways_file data/external/ReactomePathways.gmt

2. Generate Pathway Heatmaps

python scripts/pipeline_steps/6b_generate_pathway_heatmap.py \
    --enrichment_file data/processed/20251219_enrichment/trajectory_enrichment.csv \
    --output_dir data/processed/20251219_enrichment \
    --fdr_threshold 0.05

Python API

Basic Usage

from renalprog.enrichment import EnrichmentPipeline

pipeline = EnrichmentPipeline(
    trajectory_dir='data/processed/trajectories',
    output_dir='data/processed/enrichment',
    cancer_type='kirc',
    n_threads=8
)

results = pipeline.run()

Generate Heatmaps

from renalprog.enrichment import generate_pathway_heatmap

heatmap_data, figures = generate_pathway_heatmap(
    enrichment_file='data/processed/enrichment/trajectory_enrichment.csv',
    output_dir='data/processed/enrichment',
    fdr_threshold=0.05
)

Pipeline Steps

  1. DESeq2 Analysis: Differential expression using PyDESeq2
  2. Reverse log-transform RSEM data
  3. Compare trajectory vs healthy controls

  4. Generate ranked gene lists

  5. GSEA Execution: Pathway enrichment analysis

  6. Run GSEAPreranked in parallel
  7. Test against Reactome pathways

  8. Calculate enrichment scores and FDR

  9. Results Combination: Aggregate all results

  10. Combine all timepoints and trajectories
  11. Filter by FDR threshold

  12. Save to CSV

  13. Visualization: Generate heatmaps

  14. Top varying pathways
  15. Top upregulated/downregulated
  16. Selected high-level and literature pathways

Output Files

output_dir/
├── deseq/                              # DESeq2 results
│   └── early_to_late/
│       └── patient_id/
│           ├── *.rnk                   # Ranked gene lists
│           └── gsea_*/                 # GSEA results
├── trajectory_enrichment.csv           # Combined results
└── pathway_heatmaps/                   # Heatmap visualizations
    ├── top_varying_pathways.pdf
    ├── top_upregulated_pathways.pdf
    ├── top_downregulated_pathways.pdf
    ├── high_level_pathways.pdf
    └── literature_pathways.pdf

Key Parameters

Parameter Description Default
n_threads Parallel processing threads 4
fdr_threshold FDR q-value cutoff 0.05
gsea_nperm GSEA permutations 1000
set_min Min pathway size 15
set_max Max pathway size 500

Common Issues

Memory errors

  • Reduce n_threads
  • Process in smaller batches

GSEA not found

  • Check gsea_path points to correct location
  • Ensure execute permissions on gsea-cli.sh

No significant pathways

  • Lower fdr_threshold
  • Check input data quality
  • Verify pathway database matches gene IDs

See Also