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

CODEBLOCK0

Parameters

Parameter	Description	Default
INLINECODE0	Path to DEG results CSV/TSV	required
INLINECODE1

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

CODEBLOCK1

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. 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