HoloNet: Decoding functional cell–cell communication events by multi-view graph learning on spatial transcriptomics

|docs| |pypi|

.. |docs| image:: https://readthedocs.org/projects/holonet-doc/badge/?version=latest :target: https://holonet-doc.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status

.. |pypi| image:: https://img.shields.io/pypi/v/HoloNet :target: https://pypi.org/project/HoloNet/ :alt: PyPI

HoloNet is a powerful tool on spatial transcriptomic data to help understand the shaping of cellular phenotypes through cell–cell communications in a microenvironment. HoloNet plays nicely with scanpy <https://scanpy.readthedocs.io/en/stable/index.html>_.

Cell–cell communication events (CEs) mediated by multiple ligand–receptor pairs construct a complex intercellular signaling network. Usually only a subset of CEs directly works for a specific downstream response in certain microenvironment. We call them as the functional communication events (FCEs).

.. image:: img/github_readme_figure01.png :align: center :alt: The The overall workflow of HoloNet

Spatial transcriptomic methods can profile the spatial distribution of gene expression levels of ligands, receptors and their downstream genes. This provides a new possibility for revealing the panorama of cell–cell communications. We developed a computational method HoloNet for decoding FCEs using spatial transcriptomic data. We modeled CEs as a multi-view network, developed an attention-based graph learning model on the network to predict the target gene expression, and decode the FCEs for specific downstream genes by interpreting the trained model.

.. image:: img/github_readme_figure02.png :align: center :alt: The The overall workflow of HoloNet

Installation ^^^^^^^^^^^^ You need to have Python 3.8 or newer installed on your system.

The latest release of HoloNet can be installed from PyPI <https://pypi.org/project/infercnvpy/>_:

.. code-block::

    pip install HoloNet

Getting started ^^^^^^^^^^^^^^^ Please refer to the Documentation <https://holonet-doc.readthedocs.io/en/latest/>_, including:

• Tutorials <https://holonet-doc.readthedocs.io/en/latest/main_tutorial.html>_
• API <https://holonet-doc.readthedocs.io/en/latest/api.html>_

Citation ^^^^^^^^^^^^^^^ Li H, Ma T, Hao M, et al. Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics. Brief Bioinform. 2023;24(6):bbad359. doi:10.1093/bib/bbad359