Metadata-Version: 2.3
Name: gnatpy
Version: 0.3.0
Summary: Analyze Rank Entropy in Python
Keywords: entropy,rank,gene,bioinformatics
Author: Braden Griebel
Author-email: Braden Griebel <bgriebel@uw.edu>
License: MIT License
         
         Copyright (c) 2026 Braden Griebel
         
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         Code from the SciPy Stats module for calculating the Kendall-Tau
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Requires-Dist: joblib>=1.4.2
Requires-Dist: numpy>=2.0.0
Requires-Dist: pandas>=2.2.0
Requires-Dist: scikit-learn>=1.6.1
Requires-Dist: scipy>=1.12.0
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Project-URL: Homepage, https://github.com/Ma-Lab-Seattle-Childrens-CGIDR/gnatpy
Project-URL: Repository, https://github.com/Ma-Lab-Seattle-Childrens-CGIDR/gnatpy
Project-URL: Documentation, https://gnatpy.readthedocs.io/en/latest/
Project-URL: Issues, https://github.com/Ma-Lab-Seattle-Childrens-CGIDR/gnatpy/issues
Project-URL: Changelog, https://github.com/Ma-Lab-Seattle-Childrens-CGIDR/gnatpy/blob/main/CHANGELOG.md
Description-Content-Type: text/markdown

# GNATpy

<img src="gnatpy_logo.png" alt="GNATpy Logo" width="200"/>

Welcome to GNATpy, the Gene Network rAnk enTropy python package!
This package includes several methods for analyzing the rank entropy
of a gene network between two different groups of samples.

## Usage

GNATpy can be installed from PyPI with

```{bash}
pip install gnatpy
```

See the [documentation](https://gnatpy.readthedocs.io/en/stable/) for more information

## Licensing

The original code for GNATpy is licensed under an MIT license,
see the LICENSE file for more information.

The code for calculating the Kendall-Tau coefficient in the
src/gnatpy/statistical_utils.py file is modified from [SciPy](https://scipy.org/), licensed under a [BSD-3-Clause license](https://github.com/scipy/scipy/blob/main/LICENSE.txt) (which is reproduced in the LICENSE file), and had a copyright disclaimer that is reproduced in the statistical_utils.py file.

The GNATpy logo is licensed under a [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.

## References

### DIRAC

- Eddy, J. A., Hood, L., Price, N. D., & Geman, D. (2010). Identifying Tightly Regulated and
  Variably Expressed Networks by Differential Rank Conservation (DIRAC). PLoS Computational
  Biology, 6(5), e1000792.
  [https://doi.org/10.1371/journal.pcbi.1000792](https://doi.org/10.1371/journal.pcbi.1000792)
