Volcano Plot Script Generator

A skill for generating publication-ready volcano plots from differential gene expression analysis results.

Overview

Volcano plots visualize the relationship between statistical significance (p-values) and magnitude of change (fold changes) in gene expression data. This skill generates customizable R or Python scripts for creating high-quality figures suitable for publications.

Use Cases

• Visualize RNA-seq DEG analysis results
• Identify significantly upregulated and downregulated genes
• Highlight genes of interest (markers, pathways)
• Generate publication-quality figures for manuscripts
• Compare multiple experimental conditions

Input Requirements

Required input data format:

• Gene identifier (gene symbol or ENSEMBL ID)
• Log2 fold change values
• Adjusted or raw p-values
• Optional: gene annotations, pathways

Output

• Publication-ready volcano plot (PNG/PDF/SVG)
• Customizable R or Python script
• Optional: labeled significant gene lists

Usage

# Example: Run the volcano plot generator
python scripts/main.py --input deg_results.csv --output volcano_plot.png

Parameters

Technical Difficulty

Medium - Requires understanding of:

• DEG analysis concepts (fold change, p-values, FDR)
• Data visualization principles
• Matplotlib/ggplot2 plotting libraries

Dependencies

Python

• pandas
• matplotlib
• seaborn
• numpy

R

• ggplot2
• dplyr
• ggrepel (for label positioning)

References

• Example datasets and templates
• Best practices for volcano plot visualization
• Color schemes for accessibility

Author

Auto-generated skill for bioinformatics visualization.

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited (pandas, matplotlib, seaborn, numpy)

Prerequisites

# Python dependencies
pip install -r requirements.txt

# R dependencies (if using R)
install.packages(c("ggplot2", "dplyr", "ggrepel"))

Evaluation Criteria

Success Metrics

• Successfully generates executable Python/R script
• Output plot is publication-ready quality
• Correctly identifies significant genes based on thresholds
• Handles missing or malformed data gracefully
• Color scheme is accessible (colorblind-friendly)

Test Cases

1. Basic DEG Visualization: Input standard DESeq2 results → Valid volcano plot
2. Custom Thresholds: Adjust log2FC and p-value thresholds → Correct gene classification
3. Gene Labeling: Specify genes to label → Labels appear correctly
4. Large Dataset: Input 20,000+ genes → Performance remains acceptable
5. Malformed Data: Input with missing values → Graceful error handling

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Add interactive plot option (Plotly)
  • Support for multiple comparison groups
  • Integration with pathway enrichment tools