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BytesAgain β€Ί Skills β€Ί Volcano Plot Script
πŸ¦€ ClawHub

Volcano Plot Script

by @ec-cyber258

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

Versionv0.1.0
Downloads485
#coding#data#writing#legal#real-estate
TERMINAL
clawhub install volcano-plot-script

πŸ“– About This Skill

name: volcano-plot-script description: Generate R/Python code for volcano plots from DEG (Differentially Expressed Genes) analysis results. Triggered when user needs visualization of gene expression data, p-value vs fold-change scatter plots, publication-ready figures for bioinformatics analysis. version: 1.0.0 category: Bioinfo tags: [volcano-plot, bioinformatics, deg-analysis, r, python, visualization] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: last_updated: 2026-02-06

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 β†’ Valid volcano plot 2. Custom Thresholds: Adjust log2FC and p-value thresholds β†’ Correct gene classification 3. Gene Labeling: Specify genes to label β†’ Labels appear correctly 4. Large Dataset: Input 20,000+ genes β†’ Performance remains acceptable 5. Malformed Data: Input with missing values β†’ 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

    ⚑ When to Use

    TriggerAction
    - Identify significantly upregulated and downregulated genes
    - Highlight genes of interest (markers, pathways)
    - Generate publication-quality figures for manuscripts
    - Compare multiple experimental conditions

    πŸ’‘ Examples

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

    βš™οΈ Configuration

    # Python dependencies
pip install -r requirements.txt
    R dependencies (if using R)
    
install.packages(c("ggplot2", "dplyr", "ggrepel"))