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Volcano Plot Script

Generate R/Python code for volcano plots from DEG (Differentially Expressed Genes) analysis results. Triggered when user needs visualization of gene expressi...

personAuthor: ec-cyber258hubclawhub
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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

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