xomics 0.2.0
Python framework for eXplainable Omics analysis
License: MIT
Requires Python: >=3.9,<=3.11

Welcome to the xOmics documentation
===================================
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**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]