Western Blot Quantifier

Automatically identify Western Blot gel bands, perform densitometric analysis, and calculate normalized values relative to loading controls.

Features

• Automatic Band Detection: Detect protein band positions in gel images
• Densitometric Analysis: Calculate grayscale/optical density values for each band
• Normalization: Normalize relative to loading control proteins (e.g., GAPDH, β-actin, Tubulin)
• Data Export: Output quantitative results in CSV format

Usage

Basic Usage

# Call in Python
from skills.western_blot_quantifier.scripts.main import WesternBlotQuantifier

# Create analyzer
analyzer = WesternBlotQuantifier()

# Analyze single image
result = analyzer.analyze(
    image_path="path/to/wb_image.png",
    reference_bands=["GAPDH"],  # Loading control band names
    target_bands=["p53", "Bcl-2"],  # Target protein band names
    lane_positions=[0.2, 0.4, 0.6, 0.8]  # Lane positions (relative to image width)
)

print(result.summary())
result.save("output/quantification_results.csv")

Command Line Usage

python -m skills.western_blot_quantifier.scripts.main \
    --input path/to/wb_image.png \
    --reference GAPDH \
    --targets p53,Bcl-2 \
    --lanes 4 \
    --output results.csv

Parameter Description

Output Format

CSV Output Example

Lane,Protein,Raw_Intensity,Background,Corrected_Intensity,Normalized_to_Reference
1,GAPDH,125000.5,5000.2,120000.3,1.00
1,p53,85000.2,3000.1,82000.1,0.68
1,Bcl-2,62000.8,2500.5,59500.3,0.50
2,GAPDH,118000.3,4800.2,113200.1,1.00
...

Return Object

{
    "raw_data": DataFrame,           # Raw optical density data
    "normalized_data": DataFrame,    # Normalized data
    "band_regions": List[Dict],      # Detected band region coordinates
    "statistics": Dict,              # Statistical analysis results
    "figures": Dict                  # Visualization chart paths
}

Dependencies

numpy>=1.21.0
opencv-python>=4.5.0
pandas>=1.3.0
matplotlib>=3.4.0
scipy>=1.7.0
scikit-image>=0.18.0

Installation

pip install -r requirements.txt

Notes

1. Image Quality: High resolution, good contrast grayscale or black and white gel images are recommended
2. Loading Control Selection: Common loading controls include GAPDH, β-actin, Tubulin; selection depends on experimental conditions
3. Background Correction: Supports rolling_ball, median, none three background correction methods
4. Lane Marking: If auto-detection is inaccurate, lane positions can be manually specified

Examples

Example 1: Basic Analysis

from skills.western_blot_quantifier.scripts.main import WesternBlotQuantifier

analyzer = WesternBlotQuantifier()

# Analyze 4-lane Western Blot results
result = analyzer.analyze(
    image_path="experiment_data/wb_gel.png",
    reference_bands=["GAPDH"],
    target_bands=["p53", "p21"],
    lane_count=4
)

# View normalized results
print(result.normalized_data)

# Save charts
result.save_figures("output/")

Example 2: Batch Processing

import glob

analyzer = WesternBlotQuantifier()

for image_path in glob.glob("experiments/*.png"):
    result = analyzer.analyze(
        image_path=image_path,
        reference_bands=["β-actin"],
        target_bands=["Target_Protein"],
        lane_count=6
    )
    result.save(f"output/{Path(image_path).stem}_results.csv")

Algorithm Description

1. Image Preprocessing: Grayscale conversion → Background correction → Denoising
2. Lane Detection: Automatic lane boundary identification based on vertical projection analysis
3. Band Detection: Band localization using 1D Gaussian fitting or peak detection algorithms
4. Optical Density Calculation: Integrate grayscale values in band region, subtract background
5. Normalization: Target protein value / Loading control protein value

Author

OpenClaw Skills

Risk Assessment

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

# 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