dvcrn 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
- Basic DEG Visualization: Input standard DESeq2 results → Valid volcano plot
- Custom Thresholds: Adjust log2FC and p-value thresholds → Correct gene classification
- Gene Labeling: Specify genes to label → Labels appear correctly
- Large Dataset: Input 20,000+ genes → Performance remains acceptable
- 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