Skip to main content

Harmonizing neuroimaging data across sites. Implementation of neurocombat using sklearn format

Project description

NeuroCombat-sklearn

License: MIT Version PythonVersion

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 at King's College London and community contributors.

Installation

Requirements

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:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for neurocombat-sklearn, version 0.1.3
Filename, size File type Python version Upload date Hashes
Filename, size neurocombat_sklearn-0.1.3-py3-none-any.whl (7.9 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size neurocombat-sklearn-0.1.3.tar.gz (5.9 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page