https://github.com/warvito/neurocombat_sklearn

Implementation of Combat harmonization method with scikit-learn compatible format
https://github.com/warvito/neurocombat_sklearn

combat harmonization inter-scanner neurocombat neuroimaging normalization

Last synced: about 1 month ago
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Implementation of Combat harmonization method with scikit-learn compatible format

• Host: GitHub
• URL: https://github.com/warvito/neurocombat_sklearn
• Owner: Warvito
• License: mit
• Created: 2019-09-06T10:52:53.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-10-16T23:18:55.000Z (almost 6 years ago)
• Last Synced: 2025-09-07T07:39:17.180Z (about 1 month ago)
• Topics: combat, harmonization, inter-scanner, neurocombat, neuroimaging, normalization
• Language: Python
• Homepage:
• Size: 18.2 MB
• Stars: 23
• Watchers: 4
• Forks: 13
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

          
# NeuroCombat-sklearn

[![License: MIT](https://img.shields.io/github/license/Warvito/neurocombat_sklearn)](https://opensource.org/licenses/MIT) 

[![Version](https://img.shields.io/pypi/v/neurocombat-sklearn)](https://pypi.org/project/neurocombat-sklearn/)

[![PythonVersion](https://img.shields.io/pypi/pyversions/neurocombat-sklearn)]()

Implementation of Combat harmonization method in scikit-learn compatible format.

The Combat harmonization/normalization method uses an parametric empirical Bayes framework to robustly adjust data for site/batch effects. 

The scikit-learn compatible format was used to facilitates the use of this harmonization method in machine learning projects. 

This repository is developed by [Walter Hugo Lopez Pinaya](https://scholar.google.com/citations?user=jjT5-HUAAAAJ) at King's College London and community contributors.

## Installation

### Requirements

- Python (>= 3.5)

- [Scikit-Learn](https://scikit-learn.org/) (>= 0.21.0)

### User installation

If you already have a working installation of numpy and scipy,

the easiest way to install neurocombat-sklearn is using ``pip``   :

    pip install neurocombat-sklearn

 

## Citation

If you find this code useful for your research, please cite:

    @article{fortin2018harmonization,

      title={Harmonization of cortical thickness measurements across scanners and sites},

      author={Fortin, Jean-Philippe and Cullen, Nicholas and Sheline, Yvette I and Taylor, Warren D and Aselcioglu, Irem and Cook, Philip A and Adams, Phil and Cooper, Crystal and Fava, Maurizio and McGrath, Patrick J and others},

      journal={Neuroimage},

      volume={167},

      pages={104--120},

      year={2018},

      publisher={Elsevier}

    }

    

    @article{johnson2007adjusting,

      title={Adjusting batch effects in microarray expression data using empirical Bayes methods},

      author={Johnson, W Evan and Li, Cheng and Rabinovic, Ariel},

      journal={Biostatistics},

      volume={8},

      number={1},

      pages={118--127},

      year={2007},

      publisher={Oxford University Press}

    }

### Disclaimer

Based on:

 - https://github.com/ncullen93/neuroCombat

 - https://github.com/nih-fmrif/nielson_abcd_2018

 - https://github.com/Jfortin1/ComBatHarmonization