Functional Enrichment Analysis (GSEA + ORA)

Functional Enrichment Analysis

Translate differential expression results into biological insights using GSEA and ORA.

When to Use This Skill

Use this skill after completing differential expression analysis to identify enriched pathways and biological processes.

Use when:

• ✅ You have DE results with fold changes and p-values
• ✅ Want to answer: "What pathways or processes are affected?"
• ✅ Need to interpret gene lists in biological context
• ✅ Preparing results for publication or validation

Two complementary methods: GSEA (primary, uses all ranked genes, detects coordinated changes) and ORA (secondary, uses significant gene list, validates GSEA). Default recommendation: Run GSEA unless user specifically requests ORA or has only a gene list (no fold changes).

See references/gsea_ora_comparison.md for detailed method comparison.

Quick Start (Example Data)

Test this skill with real DE results in ~2 minutes:

# Load example DE results from airway dataset (dexamethasone treatment)
source("scripts/load_example_data.R")
de_results <- load_airway_de_results()  # Auto-installs packages (~1-2 min, ~40MB)

# Load required packages and scripts
library(clusterProfiler)
library(msigdbr)
source("scripts/prepare_gene_lists.R")
source("scripts/get_msigdb_genesets.R")
source("scripts/run_gsea.R")
source("scripts/generate_plots.R")
source("scripts/export_results.R")

# Run GSEA workflow
ranked_genes <- create_ranked_list(de_results)
term2gene <- get_msigdb_genesets("human", c("H"))  # Hallmark pathways only for speed
gsea_result <- run_gsea(ranked_genes, term2gene, n_perm = 1000)
generate_all_plots(gsea_result)
export_all(gsea_result, ranked_genes, output_prefix = "quick_test")

What you get:

• Dataset: Human airway smooth muscle cells, dexamethasone treatment vs untreated
• Expected results: ~5-10 significant Hallmark pathways (Inflammatory Response, TNF-alpha signaling, Interferon response)
• Outputs: CSV results, SVG/PNG plots, RDS objects, markdown summary

For your own data: Replace example data loading with your DE results file (see Inputs section).

Installation

# Install Bioconductor packages
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c("clusterProfiler", "enrichplot", "org.Hs.eg.db"))

# Install CRAN packages
install.packages(c("msigdbr", "ggplot2", "ggrepel", "dplyr"))

Software Requirements

Database Licenses

⚠️ KEGG requires commercial license for commercial use. MSigDB and GO use CC-BY 4.0 (commercial use permitted with attribution). For commercial applications, use Hallmark, Reactome, or GO databases, or obtain appropriate KEGG license.

Inputs

Required:

• Differential expression results with:
  • Gene identifiers (HGNC symbols preferred)
  • Log2 fold change values (for GSEA ranking)
  • Adjusted p-values (for ORA filtering)
  • Optional: Test statistics for optimal GSEA ranking

Supported DE methods:

• DESeq2 results (padj, log2FoldChange, stat)
• edgeR results (FDR, logFC, logCPM)
• limma results (adj.P.Val, logFC, t)

File formats:

• CSV/TSV files with DE results
• R data frames or result objects

Species support:

• Human (default)
• Mouse
• Other species via custom gene sets

Outputs

Primary results (CSV):

• enrichment_gsea_results.csv - All GSEA results (NES, p-value, FDR, leading edge genes)
• enrichment_ora_up_results.csv - Enriched pathways in upregulated genes (if ORA run)
• enrichment_ora_down_results.csv - Enriched pathways in downregulated genes (if ORA run)

Visualizations (PNG + SVG):

• gsea_dotplot.png / .svg - Activated/suppressed pathways visualization
• gsea_running_score.png / .svg - Running enrichment score plots for top pathways
• ora_up_barplot.png / .svg - Bar plot for upregulated pathways (if ORA run)
• ora_down_barplot.png / .svg - Bar plot for downregulated pathways (if ORA run)

Analysis objects (RDS):

• enrichment_gsea_result.rds - Complete GSEA enrichResult object for downstream use
  • Load with: gsea_result <- readRDS('enrichment_gsea_result.rds')
  • Required for: pathway visualization tools, network analysis, downstream enrichment
• enrichment_ora_up_result.rds - ORA enrichResult for upregulated genes (if run)
• enrichment_ora_down_result.rds - ORA enrichResult for downregulated genes (if run)
• enrichment_ranked_genes.rds - Ranked gene list used for GSEA
  • Load with: ranked_genes <- readRDS('enrichment_ranked_genes.rds')

Summary:

• enrichment_summary.md - Text summary of top findings with interpretation

Clarification Questions

Default settings (use unless user specifies otherwise):

• Method: GSEA
• Databases: Hallmark + KEGG
• Species: Human
• ORA thresholds: padj ≤ 0.05, |log2FC| ≥ 1 (only if running ORA)

⚠️ CRITICAL: Always ask question #1 first to check if user has provided input files before proceeding with analysis.

Questions to ask only if ambiguous:

1. Input Files (ASK THIS FIRST):

   • Do you have specific differential expression results file(s) to analyze?
     • If uploaded: Is this the DE results file (with log2 fold changes and p-values) you'd like to use?
     • Expected formats: CSV/TSV with gene, log2FoldChange, padj/FDR columns
   • Or use example data for testing?
     • Use source("scripts/load_example_data.R"); de_results <- load_airway_de_results()
     • Human airway dataset: dexamethasone treatment (~60k genes, ~1-2 min to generate)

2. Method: GSEA (default with full DE results), ORA (gene list only), or Both (validation)

3. Databases: Hallmark + KEGG (default exploratory), GO:BP (detailed processes), Reactome (alternative to KEGG), C6/C7 (cancer/immunology)

4. Species: Human (default) or Mouse

See references/decision-guide.md for detailed decision guidance.

Standard Workflow

Note: Run from the OmicsClaw root directory and add the workflow scripts to sys.path:

import sys; import os; sys.path.insert(0, os.path.abspath('knowledge_base/scripts/functional-enrichment-from-degs'))

🚨 MANDATORY: USE SCRIPTS EXACTLY AS SHOWN - DO NOT WRITE INLINE CODE 🚨

CRITICAL: Use relative paths (knowledge_base/scripts/functional-enrichment-from-degs/). DO NOT construct absolute paths.

These scripts provide robust, validated enrichment analysis. Use them as-is for standard GSEA workflows.

Step 1 - Load data:

# Load all required scripts
source("scripts/load_de_results.R")
source("scripts/prepare_gene_lists.R")
source("scripts/get_msigdb_genesets.R")
source("scripts/run_gsea.R")
source("scripts/generate_plots.R")
source("scripts/export_results.R")

# Load your DE results
de_results <- load_de_results("your_de_results.csv")  # Replace with your file path

# Prepare inputs
ranked_genes <- create_ranked_list(de_results)
term2gene <- get_msigdb_genesets("human", c("H", "C2:CP:KEGG"))

DO NOT write inline data loading code. Just use the scripts.

Step 2 - Run analysis:

gsea_result <- run_gsea(ranked_genes, term2gene)

DO NOT write inline GSEA code. Just use run_gsea().

Step 3 - Generate visualizations:

generate_all_plots(gsea_result)

🚨 DO NOT write inline plotting code (ggsave, dotplot, etc.). Just use generate_all_plots(). 🚨

The script handles PNG + SVG export with graceful fallback for SVG dependencies.

Step 4 - Export results:

export_all(gsea_result, ranked_genes, output_prefix = "enrichment")

DO NOT write custom export code. Use export_all().

✅ VERIFICATION - You should see:

• After Step 1: "✓ Data loaded successfully"
• After Step 2: "✓ Analysis completed successfully!"
• After Step 3: "✓ All plots generated successfully!"
• After Step 4: "=== Export Complete ==="

❌ IF YOU DON'T SEE THESE: You wrote inline code. Stop and use the scripts.

⚠️ CRITICAL - DO NOT:

• ❌ Write inline enrichment code → STOP: Use run_gsea()
• ❌ Write inline plotting code (ggsave, dotplot, gseaplot2, etc.) → STOP: Use generate_all_plots()
• ❌ Write custom export code → STOP: Use export_all()
• ❌ Use absolute paths like /mnt/knowhow/ → use relative paths scripts/
• ❌ Skip the background parameter in ORA → causes inflated p-values
• ❌ Try to install svglite → script handles SVG fallback automatically

⚠️ IF SCRIPTS FAIL - Script Failure Hierarchy:

1. Fix and Retry (90%) - Install missing package, re-run script
2. Modify Script (5%) - Edit the script file itself, document changes
3. Use as Reference (4%) - Read script, adapt approach, cite source
4. Write from Scratch (1%) - Only if genuinely impossible, explain why

NEVER skip directly to writing inline code without trying the script first.

When customization is needed:

• Databases, ranking metrics, thresholds: Read references/decision-guide.md to understand options and adapt to your specific analysis goals
• Complete custom workflow: See references/method-reference.md for step-by-step inline examples (only if user explicitly requests full customization)
• Adding ORA validation: See Pattern 2 in Common Patterns section below

What the scripts provide:

• scripts/load_de_results.R - Standardizes DE results from DESeq2/edgeR/limma
• scripts/load_example_data.R - Loads example datasets for testing
• scripts/prepare_gene_lists.R - Creates ranked lists (GSEA) and filtered lists (ORA)
• scripts/get_msigdb_genesets.R - Retrieves MSigDB gene sets (Hallmark, KEGG, Reactome, GO)
• scripts/run_gsea.R - Runs GSEA with sensible defaults
• scripts/run_ora.R - Runs ORA for up/downregulated genes
• scripts/generate_plots.R - Creates publication-quality PNG + SVG plots with automatic SVG fallback handling
• scripts/export_results.R - Exports CSV results, RDS objects, and markdown summaries

Decision Guide

Key decisions during analysis:

1. Method selection (before analysis): GSEA only (default, full DE results), ORA only (gene list without fold changes), or Both (validation)

2. Database selection (Step 3): Hallmark + KEGG (default exploratory), GO:BP (detailed processes), Reactome (KEGG alternative, commercial-friendly), or C6/C7 (cancer/immunology)

3. Ranking metric (Step 2): Test statistic (best, balances effect + significance), signed -log10(pvalue) (good alternative), or log2FC only (not recommended)

For detailed decision guidance with options and recommendations, see references/decision-guide.md.

Common Patterns

For all patterns and advanced examples, see references/method-reference.md.

Pattern 1: Quick GSEA with Default Settings

# Load required scripts
source("scripts/load_de_results.R")
source("scripts/prepare_gene_lists.R")
source("scripts/get_msigdb_genesets.R")
source("scripts/run_gsea.R")
source("scripts/generate_plots.R")
source("scripts/export_results.R")

# Load, prepare, and run GSEA
de_results <- load_de_results("de_results.csv")
ranked_genes <- create_ranked_list(de_results)
term2gene <- get_msigdb_genesets("human", c("H", "C2:CP:KEGG"))
gsea_result <- run_gsea(ranked_genes, term2gene)
generate_all_plots(gsea_result)
export_all(gsea_result, ranked_genes, output_prefix = "enrichment")

Pattern 2: GSEA + ORA for Validation

# Load required scripts
source("scripts/load_de_results.R")
source("scripts/prepare_gene_lists.R")
source("scripts/get_msigdb_genesets.R")
source("scripts/run_gsea.R")
source("scripts/run_ora.R")
source("scripts/generate_plots.R")
source("scripts/export_results.R")

# Prepare inputs and run both methods
de_results <- load_de_results("de_results.csv")
ranked_genes <- create_ranked_list(de_results)
sig_genes <- filter_significant_genes(de_results)
term2gene <- get_msigdb_genesets("human", c("H", "C2:CP:KEGG"))
gsea_result <- run_gsea(ranked_genes, term2gene)
ora_up <- run_ora(sig_genes$up, term2gene, sig_genes$background, direction = "upregulated")
ora_down <- run_ora(sig_genes$down, term2gene, sig_genes$background, direction = "downregulated")
generate_all_plots(gsea_result)
generate_ora_barplot(ora_up, "Upregulated")
generate_ora_barplot(ora_down, "Downregulated")
export_all(gsea_result, ora_up, ora_down, ranked_genes, output_prefix = "enrichment")

For results interpretation guidance, see references/interpretation_guidelines.md.

Common Errors

Suggested Next Steps

After completing functional enrichment analysis:

1. Interpret and Validate Results

Check if enriched pathways align with experimental hypothesis and are biologically coherent. Examine leading edge genes and compare with literature. See references/interpretation_guidelines.md for detailed guidance.

2. Create Additional Visualizations

Generate optional advanced plots: enrichment maps (pathway networks), concept networks (gene-pathway connections), or ridge plots (expression distributions). Use scripts/generate_plots.R.

3. Deep Dive on Key Pathways

Examine leading edge genes in top pathways, check individual gene expression in original DE results, and look up pathway diagrams in KEGG/Reactome databases.

4. Export for Publications

Use CSV results for supplementary tables, SVG plots for figures (300+ DPI), and include enrichment summary in results section. Document database versions, parameters, and software versions in methods.

Related Skills

Prerequisites (run before this skill):

• bulk-rnaseq-counts-to-de-deseq2 - DESeq2 differential expression analysis, generates required DE results
• de-results-to-gene-lists - Filters and annotates DE results, prepares gene lists

Complementary (run alongside):

• de-results-to-plots - Visualize DE results before enrichment to understand expression patterns

Downstream:

• Pathway visualization tools - Coming soon
• Network analysis of enriched pathways - Coming soon
• Integration with other omics data - Coming soon

References

Primary Citations

1. clusterProfiler: Yu G, et al. (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. DOI: 10.1089/omi.2011.0118

2. GSEA method: Subramanian A, et al. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS. DOI: 10.1073/pnas.0506580102

3. MSigDB: Liberzon A, et al. (2015) The Molecular Signatures Database Hallmark Gene Set Collection. Cell Systems. DOI: 10.1016/j.cels.2015.12.004

4. Method benchmarking: Geistlinger L, et al. (2021) Toward a gold standard for benchmarking gene set enrichment analysis. Brief Bioinform. DOI: 10.1093/bib/bbz158

Additional Resources

Detailed documentation:

• references/gsea_ora_comparison.md - Comprehensive GSEA vs ORA comparison
• references/database_guide.md - Gene set database selection guide
• references/interpretation_guidelines.md - Results interpretation guide
• references/gsea_ora_validation_framework.md - Validation framework

Scripts:

• scripts/load_de_results.R - Load and standardize DE results
• scripts/prepare_gene_lists.R - Create ranked/filtered gene lists
• scripts/get_msigdb_genesets.R - Retrieve MSigDB gene sets
• scripts/run_gsea.R - Execute GSEA
• scripts/run_ora.R - Execute ORA
• scripts/generate_plots.R - Visualization functions
• scripts/export_results.R - Export results and summaries

Evaluation:

• assets/eval/ - Evaluation test cases and benchmarking materials