Metadata-Version: 2.1
Name: iMSminer
Version: 1.0.1
Summary: 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.
Home-page: https://github.com/Prentice-lab-UF/iMSminer
Author: Yu Tin Lin
Author-email: yutinlin@stanford.edu
License: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: bokeh>=3.2.1
Requires-Dist: opencv-python>=4.5.0
Requires-Dist: matplotlib>=3.5.0
Requires-Dist: msalign>=0.2.0
Requires-Dist: networkx>=3.0.0
Requires-Dist: numba>=0.50.1
Requires-Dist: numpy<1.26.1,>=1.20.4
Requires-Dist: pandas<=1.5.3
Requires-Dist: psutil>=5.8.0
Requires-Dist: pyimzml>=1.5.4
Requires-Dist: scikit-learn>=1.2.2
Requires-Dist: scipy>=1.11.4
Requires-Dist: seaborn>=0.11.2
Requires-Dist: statsmodels>=0.14.0
Requires-Dist: statannotations>=0.6.0

## **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**
- [**Colab Notebooks**](https://drive.google.com/drive/folders/12Qjz5zlSMwL42W0X_yZxZVZaVXtlhylo?usp=sharing)
- [**Sample Datasets**](https://drive.google.com/drive/folders/1tkPW1K2RDtMaaXnBvIKBXggdvqlRekdR?usp=sharing)
- [**Tutorials and Documentation**](https://prentice-lab-uf.github.io/iMSminer/)
- [**Feedback Form**](https://forms.gle/C16Hrp9ibdtWgyH17)
- [**PyPI**](https://pypi.org/project/iMSminer/)

## **Features**
- Interactive input 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** 
```python
pip install iMSminer
```
### **GPU-Accelerated Packages**
#### [**Cupy**](https://docs.cupy.dev/en/stable/install.html)
#### [**RAPIDS**](https://docs.rapids.ai/install?_gl=1*1p3fcd0*_ga*MTQxMDQwNDI5NC4xNzE0ODU0NzQx*_ga_RKXFW6CM42*MTcxODg1NzY3MS4xMS4xLjE3MTg4NTc4NTYuNjAuMC4w#wsl2)

## **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 [prenticelabuf@gmail.com](mailto:prenticelabuf@gmail.com). 

## **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},
}
```
