Metadata-Version: 2.1
Name: xomics
Version: 0.2.0
Summary: Python framework for eXplainable Omics analysis
License: MIT
Author: Stephan Breimann
Author-email: stephanbreimann@gmail.de
Requires-Python: >=3.9,<=3.11
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
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Project-URL: Documentation, https://xomics.readthedocs.io
Project-URL: Repository, https://github.com/breimanntools/xomics
Description-Content-Type: text/x-rst

Welcome to the xOmics documentation
===================================
.. Developer Notes:
    Please update badges in README.rst and vice versa
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   :target: https://github.com/breimanntools/xomics/actions
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   :target: https://github.com/breimanntools/xomics/actions
   :alt: Python-check

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   :target: https://pypi.org/project/xomics/
   :alt: PyPI - Status

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   :target: https://pypi.python.org/pypi/xomics
   :alt: Supported Python Versions

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   :target: https://pypi.python.org/pypi/xomics
   :alt: PyPI - Package Version

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   :target: https://anaconda.org/conda-forge/xomics
   :alt: Conda - Package Version

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   :alt: Documentation Status

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   :target: https://github.com/breimanntools/xomics/blob/master/LICENSE
   :alt: License

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   :target: https://pepy.tech/project/xomics
   :alt: Downloads

**xOmics** (eXplainable Omics) is a Python framework developed for streamlined and explainable omics analysis, with a
spotlight on differential proteomics expression data. It introduces the following key algorithms:

- **cImpute**: Conditional Imputation - A transparent method for hybrid missing value imputation.
- **xOmicsIntegrate**: Protein-centric integration of multiple (prote)omic datasets to find commonalities and differences.
- **xOmicsRank**: Protein-centric ranking of (prote)omic data, leveraging functional enrichment results.

In addition, **xOmics** provides functional capabilities for efficiently loading benchmark proteomics datasets via
**load_datasets**, accompanied by corresponding enrichment data.A suite of supportive functions is also available to
facilitate a smooth and efficient (prote)omic analysis pipeline.

Install
=======
**xOmics** can be installed either from `PyPi <https://pypi.org/project/xomics>`_ or
`conda-forge <https://anaconda.org/conda-forge/xomics>`_:

.. code-block:: bash

   pip install -u xomics
   or
   conda install -c conda-forge xomics

Contributing
============
We appreciate bug reports, feature requests, or updates on documentation and code. For details, please refer to
`Contributing Guidelines <CONTRIBUTING.rst>`_. For further questions or suggestions, please email stephanbreimann@gmail.com.

Citations
=========
If you use xOmics in your work, please cite the respective publication as follows:

**xOmics**:
   [Citation details and link if available]

**cImpute**:
   [Citation details and link if available]

**xOmicsIntegrate**:
   [Citation details and link if available]

