Metadata-Version: 2.1
Name: neurocombat-sklearn
Version: 0.1.3
Summary: Harmonizing neuroimaging data across sites. Implementation of neurocombat using sklearn format
Home-page: https://github.com/Warvito/neurocombat-sklearn
Author: Walter Hugo Lopez Pinaya
License: UNKNOWN
Project-URL: Source Code, https://github.com/Warvito/neurocombat-sklearn
Description: # 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
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
