iMSminer 0.2.1

iMSminer provides user-friendly, partially GPU- or compiler-accelerated multi-condition, multi-ROI, and multi-dataset preprocessing and mining of larger-than-memory imaging mass spectrometry datasets in Python.

Welcome to iMSminer!

iMSminer provides user-friendly, partially GPU- or compiler-accelerated multi-ROI and multi-dataset preprocessing and mining of larger-than-memory imaging mass spectrometry datasets in Python.

Resources

• Quick Start
• Tutorials and Documentation
• Feedback Form

Features

• Interactive question prompts to enhance user-friendliness
• Preprocesses imzML datasets via peak picking, baseline subtraction (optional), mass alignment (optional), and peak integration
• Interactive ROI annotation and selection
• Optional data normalization, internal calibration, MS1 search, MS2 confirmation, and analyte filtering
• Unsupervised learning to extract patterns based on molecular co-localization or in situ molecular profile
• Univariate fold-change statistics with ROI statistics
• Visualiztion of ion image and ion statistics
• Quickstart guides on Google Colab

Installation (Local)

iMSminer

pip install iMSminer

GPU-Accelerated Packages

Cupy

RAPIDS

Call for Contributions

We appreciate contributions of any form, from feedback to debugging to method development. We enthusiastically welcome developers to interface their published models with iMSminer and host quickstart guides on Google Colab. Please feel free to contact us at [e-mail].

Citation

Please consider citing iMSminer and related packages if iMSminer is helpful to your work

@software{imsminer2024,
  author = {Yu Tin Lin and Haohui Bao and Troy R. Scoggings IV and Boone M. Prentice},
  title = {{iMSminer}: A Data Processing and Machine Learning Package for Imaging Mass Spectrometry},
  url = {https://github.com/Prentice-lab-UF/iMSminer},
  version = {1.0.0},
  year = {2024},
}

@software{pyimzml,
  author = {Alexandrov Team, EMBL},
  title = {{pyimzML}: A Parser to Read .imzML Files},
  url = {https://github.com/alexandrovteam/pyimzML},
  version = {1.5.4},
  year = {2024},
}

@software{msalign2024,
  author = {Lukasz G. Migas},
  title = {{msalign}: Spectral alignment based on MATLAB's `msalign` function},
  url = {https://github.com/lukasz-migas/msalign},
  version = {0.2.0},
  year = {2024},
}