Metadata-Version: 2.4
Name: scspecies
Version: 0.1.4
Summary: scspecies allows users to align latent representations of single-cell datasets from different species.
Author: Clemens Schächter
License: MIT
Project-URL: Homepage, https://scspecies.readthedocs.io/
Project-URL: Repository, https://github.com/cschaech/scspecies_package
Project-URL: Documentation, https://scspecies.readthedocs.io/
Keywords: single-cell,comparative analysis,cross-species,transfer learning,alignment,deep-learning,scspecies
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE.txt
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Dynamic: license-file

# scspecies

**scSpecies** is a deep‐learning framework for aligning single‐cell RNA-seq datasets across species.  
Built on top of scVI and transfer-learning principles, it learns a shared embedding space that directly matches cell populations from different organisms.

## Installation

To install the latest stable release of scSpecies run one of the following commands.
scSpecies defines two extras required to run the tutorial notebooks, **plotting** and **notebooks**.

.. code-block:: bash

    pip install scspecies

After installing,  confirm that scSpecies loads:

.. code-block:: bash

    python -c "import scspecies; print(scspecies.__version__)"
    
## Documentation 

Full API docs, tutorials, and examples are available at:
[scSpecies Documentation (Read the Docs)](https://scspecies.readthedocs.io/en/latest/)

## Tutorial Notebooks

Start with running the tutorial notebooks. They can be downloaded from the [GitHub repository](https://github.com/cschaech/scspecies_package/tree/main/docs/source/tutorials).
