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

Parameter	Description	Default
--input	Path to DEG results CSV/TSV	required
--output	Output plot file path	volcano_plot.png
--log2fc-col	Column name for log2 fold change	log2FoldChange
--pvalue-col	Column name for p-value	padj
--gene-col	Column name for gene IDs	gene
--log2fc-thresh	Log2 FC threshold for significance	1.0
--pvalue-thresh	P-value threshold	0.05
--label-genes	File with genes to label	None
--top-n	Label top N significant genes	10
--color-up	Color for upregulated genes	#E74C3C
--color-down	Color for downregulated genes	#3498DB
--color-ns	Color for non-significant genes	#95A5A6

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

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output plots	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

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