EnrichmentAnalyzer: GO and Pathway Enrichment Analysis Pipeline

A reproducible pipeline for performing Gene Ontology (GO) and pathway enrichment analysis on differentially expressed genes (DEGs) from XLSX files.

Features

• Batch Processing: Analyze multiple DEG files at once
• Multiple Organisms: Support for Zebrafish, Human, and other organisms
• Comprehensive Analysis: GO terms (BP, MF, CC) and pathways (WikiPathways, KEGG)
• Automated Visualization: Generate comparison plots automatically
• Flexible Input: Works with any XLSX format with customizable column names
• Summary Reports: Detailed summaries of all analyses
• Error Handling: Robust error handling and logging

Installation

Requirements

# Install required packages
pip install numpy pandas matplotlib seaborn gseapy gprofiler-official openpyxl pathlib

Full requirements.txt

numpy>=1.21.0
pandas>=1.3.0
matplotlib>=3.4.0
seaborn>=0.11.0
gseapy>=1.0.0
gprofiler-official>=1.0.0
openpyxl>=3.0.0
pathlib>=1.0.0
pyyaml>=5.4.0

Quick Start

1. Analyze a Single File

# Basic analysis
python3 batch_processing.py quick my_degs.xlsx

# With custom sample name and output directory
python3 batch_processing.py quick my_degs.xlsx --name "Treatment_vs_Control" --output "my_results"

2. Batch Analysis of Multiple Files

# Analyze all XLSX files in a directory
python3 enrichment_pipeline.py --input "/path/to/deg_files/" --batch --output "batch_results"

3. Using the Main Pipeline Script

# Single file with custom parameters
python3 enrichment_pipeline.py --input "DEGs.xlsx" --organism "zebrafish" --p-threshold 0.01

# Batch analysis (recursive by default)
python3 enrichment_pipeline.py \
  --input "deg_directory/" \
  --batch \
  --pattern "DEGs_*.xlsx" \
  --output "results"

Input File Format

Your XLSX files should contain the following columns (customizable):

gene	p_val	avg_log2FC	pct.1	pct.2	p_val_adj	regulation
gene1	0.001	2.5	0.8	0.1	0.01	Upregulated
gene2	0.01	-1.8	0.3	0.7	0.05	Downregulated

Required Columns:

• gene: Gene symbols
• regulation: Must contain "Upregulated" and "Downregulated" values

Create Template File:

python3 batch_processing.py template

Usage Examples

Example 1: Basic Single File Analysis

from enrichment_pipeline import EnrichmentAnalyzer

# Initialize analyzer
analyzer = EnrichmentAnalyzer(organism='Zebrafish', output_dir='results')

# Analyze single sample
result = analyzer.analyze_sample('DEGs_hypoxia_vs_control.xlsx', 'hypoxia_treatment')
print(f"Found {result['up_genes_count']} upregulated and {result['down_genes_count']} downregulated genes")

Example 2: Batch Analysis with Custom Settings

from enrichment_pipeline import EnrichmentAnalyzer

analyzer = EnrichmentAnalyzer(organism='Human', output_dir='human_analysis')

# Batch analyze all files in directory
summary = analyzer.batch_analyze(
    input_dir='deg_files/',
    pattern='*_DEGs.xlsx',
    gene_col='GeneName',
    regulation_col='Direction',
    p_threshold=0.01,
    recursive=True
)

print("Analysis Summary:")
print(summary[['sample_name', 'status', 'up_genes_count', 'down_genes_count']])

Example 3: Custom Column Names

python enrichment_pipeline.py \
    --input "my_data.xlsx" \
    --gene-col "GeneName" \
    --regulation-col "Direction" \
    --organism "Human" \
    --p-threshold 0.01

Output Structure

results/
├── sample1/                        # Per-sample outputs (batch + single-file)
│   ├── plots/
│   │   ├── sample1_GO_biological_process_significant.png
│   │   ├── sample1_kegg_pathways_significant.png
│   │   └── ...
│   ├── go_results/
│   │   ├── sample1_up_GO_Biological_Process.xlsx
│   │   ├── sample1_down_GO_Molecular_Function.xlsx
│   │   └── ...
│   └── pathway_results/
│       ├── sample1_up_WikiPathways.xlsx
│       ├── sample1_down_KEGG.xlsx
│       └── ...
├── summary/                        # Analysis summaries
│   └── batch_analysis_summary.xlsx
└── enrichment_analysis.log         # Detailed log file

Supported Organisms

• Zebrafish (Zebrafish, zebrafish, d. rerio, danio rerio, drerio)
• Human (Human, human, hsapiens)
• Mouse (Mouse, mouse, mmusculus)
• Rat (supported by Enrichr; use an Enrichr-compatible identifier)
• And many others supported by GSEApy and g:Profiler

Notes for scRNA-seq DEG outputs

If your DEG XLSX files come from Seurat/Nextflow pipelines that label direction as significance instead of regulation, run with:

python3 enrichment_pipeline.py \
  --input "deg_results/" \
  --batch \
  --pattern "DEGs_*.xlsx" \
  --regulation-col significance \
  --organism zebrafish \
  --output "enrich_results"

Troubleshooting

Common Issues

1. "No significant terms found"

   • Try lowering the p-value threshold: --p-threshold 0.1
   • Check if gene symbols are correctly formatted
   • Ensure adequate number of DEGs (>10 recommended)

2. "Empty gene list"

   • Verify column names match your data
   • Check significance categories ("Upregulated"/"Downregulated")

3. Network/API errors

   • Check internet connection
   • Try running analysis again (temporary API issues)

Debug Mode

# Enable detailed logging
python enrichment_pipeline.py --input "file.xlsx" --verbose

Advanced Features

Custom Analysis Parameters

# Initialize with custom settings
analyzer = EnrichmentAnalyzer(
    organism='Human',
    output_dir='custom_results'
)

# Override default libraries
analyzer.libraries = {
    'go': ['GO_Biological_Process_2021'],
    'pathways': ['KEGG_2021_Human', 'Reactome_2022']
}

Integration with Other Tools

# Load results for further analysis
import pandas as pd

# Load GO results
go_results = pd.read_excel('results/go_results/sample_up_GO_Biological_Process.xlsx')
significant_go = go_results[go_results['Adjusted P-value'] < 0.05]

# Custom visualization
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
plt.barh(significant_go['Term'][:10], -np.log10(significant_go['Adjusted P-value'][:10]))
plt.xlabel('-log10(Adjusted P-value)')
plt.title('Top 10 GO Terms')
plt.tight_layout()
plt.show()

Citation

If you use this pipeline in your research, please cite the underlying tools:

• GSEApy: Fang et al. (2023). GSEApy: a comprehensive package for performing gene set enrichment analysis in Python.
• g:Profiler: Raudvere et al. (2019). g:Profiler: a web server for functional enrichment analysis.

License

This pipeline is provided under the MIT License. See LICENSE file for details.

Support

For issues and questions:

1. Check the troubleshooting section above
2. Review the log files for detailed error messages
3. Ensure your input data matches the expected format