SPARCSpy - image-based single cell analysis at scale in python
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SPARCSpy is a scalable toolkit to analyse single-cell image datasets. This Python implementation efficiently segments individual cells, generates single-cell datasets and provides tools for the efficient deep learning classification of their phenotypes for downstream applications.

.. image:: pages/images/graphical_abstract_without_title.png
   :width: 100%
   :align: center
   :alt: graphical abstract

Installation
=============

Check out the installation instructions :ref:`here<installation>`. You can validate your installation by running one of the example notebooks `here <https://github.com/MannLabs/SPARCSpy/tree/main/docs_source/pages/notebooks>`_.

Getting Started
===============

You can check out our :ref:`quickstart<quickstart>` guide to get started with SPARCSpy. For more detailed information on the package, we have written an in depth :ref:`computational workflow <pipeline>` guide. In the github repository you can also find some `tutorial notebooks <https://github.com/MannLabs/SPARCSpy/tree/main/docs_source/pages/notebooks>`_ as well as `small example datasets <https://github.com/MannLabs/SPARCSpy/tree/main/example_data>`_ to get started with. If you encounter issues feel free to `open up a git issue <https://github.com/MannLabs/SPARCSpy/issues>`_.

Citing our Work
================

SPARCSpy was developed by Sophia Mädler, Georg Wallmann and Niklas Schmacke in the labs of `Matthias Mann <https://www.biochem.mpg.de/de/mann>`_ and `Veit Hornung <https://www.genzentrum.uni-muenchen.de/research-groups/hornung/index.html>`_ in 2023. SPARCSpy is actively developed with support from the labs of Matthias Mann, Veit Hornung and `Fabian Theis <https://www.helmholtz-munich.de/en/icb/research-groups/theis-lab>`_.

If you use our code please cite the `following manuscript <https://doi.org/10.1101/2023.06.01.542416>`_:

SPARCS, a platform for genome-scale CRISPR screening for spatial cellular phenotypes
Niklas Arndt Schmacke, Sophia Clara Maedler, Georg Wallmann, Andreas Metousis, Marleen Berouti, Hartmann Harz, Heinrich Leonhardt, Matthias Mann, Veit Hornung
bioRxiv 2023.06.01.542416; doi: https://doi.org/10.1101/2023.06.01.542416

Contributing
============

We are excited for people to adapt and extend SPARCSpy to their needs. If you are interested in contributing to SPARCSpy, please reach out to the developers or open a pull request on our github repository.

Documentation
==============

.. toctree::
   :maxdepth: 2
   :caption: SPARCSpy Ecosystem

   pages/quickstart/ 
   pages/pipeline/
   pages/example_notebooks

.. toctree::
   :maxdepth: 2
   :includehidden:
   :caption: Module API

   pages/sparcscmd
   pages/module