Skip to content

Overview of user guide

MS-MINT: Mass Spectrometry Metabolomics Integration Toolkit

Introduction

MS-MINT is a powerful Python library designed for comprehensive analysis of mass spectrometry data in metabolomics research. It provides an integrated workflow for processing, analyzing, and visualizing metabolomic datasets.

Key Features

1. Data Processing

  • Support for multiple mass spectrometry file formats (mzXML, mzML, mzHDF)
  • Automated peak detection and extraction
  • Flexible target list management
  • Advanced chromatogram analysis

2. Visualization

  • Interactive and static plotting
  • Heatmaps
  • Peak shape visualization
  • Chromatogram plotting
  • 2D histogram analysis

3. Analysis Capabilities

  • Peak integration
  • Retention time alignment
  • Intensity normalization
  • Statistical comparisons

Getting Started

Installation

pip install ms-mint

Basic Workflow

from ms_mint import Mint

# Create a Mint instance
mint = Mint()

# Load target list
mint.load_targets('path/to/targets.csv')

# Load mass spectrometry files
mint.load_ms_files('path/to/ms/files/')

# Run analysis
mint.run()

# Visualize results
plotter = mint.plotter
plotter.heatmap()
plotter.peak_shapes()

Core Concepts

Target Lists

Target lists define the specific compounds or peaks of interest in your analysis. They include:

  • Peak labels
  • Theoretical m/z values
  • Retention time windows
  • Intensity thresholds

File Formats

Supported input formats:

  • CSV
  • Excel (.xlsx)
  • Mass spectrometry files:
  • mzXML
  • mzML
  • mzHDF

Analysis Steps

  1. Target Definition
  2. Create a target list with compound information

  3. Specify m/z values, retention times, and other parameters

  4. Data Loading

  5. Load mass spectrometry files
  6. Load target list

  7. Configure analysis parameters

  8. Peak Extraction

  9. Automated peak detection
  10. Integration based on target specifications

  11. Quality filtering

  12. Visualization

  13. Multiple visualization options
  14. Interactive and static plots
  15. Customizable color schemes and layouts

Advanced Usage

Customization

  • Modify peak detection parameters
  • Custom filtering
  • Advanced visualization options

Experimental Notebook Interface

from ms_mint.notebook import Mint

# Interactive Jupyter Notebook mode
mint = Mint()
mint.display()

Performance Considerations

  • Optimized for large metabolomics datasets
  • Supports parallel processing
  • Memory-efficient data handling

Best Practices

  1. Data Preparation
  2. Use high-quality, clean mass spectrometry data
  3. Create precise target lists

  4. Validate input files

  5. Parameter Tuning

  6. Adjust peak detection parameters
  7. Validate results through visualization

  8. Compare multiple analysis runs

  9. Reproducibility

  10. Document all analysis parameters
  11. Use consistent target lists and settings
  12. Export and share results

Troubleshooting

Common Issues

  • Incorrect file formats
  • Mismatched target list specifications
  • Unexpected peak detection results

Debugging Tips

  • Check input file integrity
  • Verify target list format
  • Use visualization tools to inspect data
  • Consult documentation and example datasets

Contributing

MS-MINT is an open-source project. Contributions are welcome!

  • Report issues on GitHub
  • Submit pull requests
  • Share improvements and extensions

Citation

When using MS-MINT in your research, please cite the library in your publications DOI: 10.5281/zenodo.12733875

Support

Disclaimer

MS-MINT is provided as-is. Always validate results and consult domain experts.