Volcano Plot Labeler

Automatically label top significant genes in volcano plots with repulsion algorithm

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