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

CODEBLOCK0

Usage

Basic Usage

CODEBLOCK1

Advanced Usage

CODEBLOCK2

Command Line Usage

CODEBLOCK3

Input Format

Expected CSV/TSV columns:

• - log2FoldChange: Log2 fold change values
• INLINECODE1 or pvalue: Adjusted p-values or raw p-values
• INLINECODE3: Gene identifiers

Algorithm

Significance Calculation

1. 1. Calculate -log10(pvalue) for all genes
2. Rank genes by combined score: INLINECODE5
3. Select top N genes with highest significance

Repulsion Algorithm

1. 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

1. 3. Iterative Optimization: Update positions for N iterations until convergence
2. Arrow Drawing: Draw connecting lines from labels to gene points

Parameters

Parameter	Type	Default	Description
INLINECODE6	DataFrame	-	Input data
INLINECODE7

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

CODEBLOCK4

Evaluation Criteria

Success Metrics

• - [ ] Successfully executes main functionality
• [ ] Output meets quality standards
• [ ] Handles edge cases gracefully
• [ ] Performance is acceptable

Test Cases

1. 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