.. scloop documentation master file, created by
   sphinx-quickstart on Mon Jan 30 12:46:05 2023.
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.. image:: _static/icon.png
  :width: 128
  :alt: scloop logo

scloop: embedding and clustering analysis of single-cell Hi-C
=============================================================

.. only:: html

    :Release: |version|
    :Date: |today|

scloop is a Python package for analyzing single-cell Hi-C data. 
It provides a unified interface for existing single-cell Hi-C embedding and clustering methods and allows for easy comparison of different methods and testing of new methods.

Typical usage will take a single-cell cooler file and a cell metadata file as input:

.. code-block:: bash
   
   scloop embed --dset human_pfc \  # dataset name
                --scool pfc.scool \  # single-cell cooler file
                --reference pfc_ref \  # cell metadata (e.g celltype, batch, depth, etc)
                --methods scHiCluster higashi VaDE  # embedding methods to run

The following sections go into more detail about how to prepare the input data for a new dataset and some of the most common arguments that can be used to customize the analysis.

.. toctree::
   :maxdepth: 2
   :caption: Getting started:

   examples/basics
   examples/new_dataset

.. toctree::
   :maxdepth: 2
   :caption: Find optimal methods and settings:

   examples/resolution
   examples/distance
   examples/sweeps
   examples/compare

.. toctree::
   :maxdepth: 2
   :caption: List of all methods and options:

   examples/methods

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

Citing
------

To cite scloop please use the following publication:


Bibliography
------------

.. [Bollobas01] Xinjun Li, Fan Feng, Wai Yan Leung and Jie Liu, "scHiCTools: a computational toolbox for analyzing single cell Hi-C data",
   PLOS Computational Biology 17(5): e1008978. https://doi.org/10.1371/journal.pcbi.1008978