Skill: ec-cyber258/volcano-plot-labeler (version 0.1.0)

--- name: volcano-plot-labeler description: Automatically label top significant genes in volcano plots with repulsion algorithm version: 1.0.0 category: Visual tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # Volcano Plot Labeler (ID: 148) Automatically identify and label the Top 10 most significant genes in volcano plots using a repulsion algorithm to prevent label overlap. ## Features - **Smart Gene Selection**: Automatically identifies the top 10 most significant genes based on p-value and fold change - **Repulsion Algorithm**: Uses force-directed positioning to prevent text label overlap - **Customizable**: Configurable thresholds, label styling, and positioning options - **Multiple Output Formats**: PNG, PDF, SVG support ## Installation ```bash pip install pandas matplotlib numpy scipy ``` ## Usage ### Basic Usage ```python from volcano_plot_labeler import label_volcano_plot import pandas as pd # Load your data df = pd.read_csv('differential_expression_results.csv') # Generate labeled volcano plot fig = label_volcano_plot( df, log2fc_col='log2FoldChange', pvalue_col='padj', gene_col='gene_name', top_n=10 ) fig.savefig('volcano_plot_labeled.png', dpi=300, bbox_inches='tight') ``` ### Advanced Usage ```python from volcano_plot_labeler import label_volcano_plot fig = label_volcano_plot( df, log2fc_col='log2FoldChange', pvalue_col='padj', gene_col='gene_name', top_n=10, pvalue_threshold=0.05, log2fc_threshold=1.0, figsize=(12, 10), repulsion_iterations=100, repulsion_force=0.05, label_fontsize=10, label_color='black', arrow_color='gray', save_path='output.png' ) ``` ### Command Line Usage ```bash python scripts/main.py \ --input data/deseq2_results.csv \ --output volcano_labeled.png \ --log2fc-col log2FoldChange \ --pvalue-col padj \ --gene-col gene_name \ --top-n 10 ``` ## Input Format Expected CSV/TSV columns: - `log2FoldChange`: Log2 fold change values - `padj` or `pvalue`: Adjusted p-values or raw p-values - `gene_name`: Gene identifiers ## Algorithm ### Significance Calculation 1. Calculate `-log10(pvalue)` for all genes 2. Rank genes by combined score: `|log2FC| * -log10(pvalue)` 3. Select top N genes with highest significance ### Repulsion Algorithm 1. **Initial Placement**: Place labels at gene coordinates 2. **Force Calculation**: - Repulsive force between overlapping labels - Spring force pulling label toward its gene point - Boundary forces to keep labels within plot area 3. **Iterative Optimization**: Update positions for N iterations until convergence 4. **Arrow Drawing**: Draw connecting lines from labels to gene points ## Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `df` | DataFrame | - | Input data | | `log2fc_col` | str | 'log2FoldChange' | Column name for log2 fold change | | `pvalue_col` | str | 'padj' | Column name for p-value | | `gene_col` | str | 'gene_name' | Column name for gene names | | `top_n` | int | 10 | Number of top genes to label | | `pvalue_threshold` | float | 0.05 | P-value cutoff for coloring | | `log2fc_threshold` | float | 1.0 | Log2FC cutoff for coloring | | `repulsion_iterations` | int | 100 | Iterations for repulsion algorithm | | `repulsion_force` | float | 0.05 | Strength of repulsion force | | `label_fontsize` | int | 10 | Font size for labels | | `figsize` | tuple | (10, 10) | Figure size | ## Output - Labeled volcano plot with: - Color-coded points (up/down/not significant) - Top 10 gene labels with leader lines - No overlapping text labels ## License MIT ## 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 files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] 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 ## Prerequisites ```bash # Python dependencies pip install -r requirements.txt ``` ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support

path: requirements.txt
content: matplotlib
numpy
pandas


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)