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https://github.com/lhc17/HoloNet

HoloNet. Reveal the holograph of functional communication events in spatial transcriptomics. Help understand how microenvironments shaping cellular phenotypes
https://github.com/lhc17/HoloNet

bioinformatics cell-cell-communication python spatial-transcriptomics

Last synced: 4 months ago
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HoloNet. Reveal the holograph of functional communication events in spatial transcriptomics. Help understand how microenvironments shaping cellular phenotypes

Host: GitHub
URL: https://github.com/lhc17/HoloNet
Owner: lhc17
License: mit
Created: 2022-06-03T00:18:48.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2023-10-18T16:11:55.000Z (8 months ago)
Last Synced: 2024-02-12T10:42:22.762Z (4 months ago)
Topics: bioinformatics, cell-cell-communication, python, spatial-transcriptomics
Language: Python
Homepage:
Size: 6.47 MB
Stars: 17
Watchers: 1
Forks: 3
Open Issues: 7
Metadata Files:
- Readme: README.rst
- License: LICENSE

Lists

awesome-cell-cell-communication - HoloNet - [python]- Functional cell–cell communication events (FCEs) is mediated by ligand–receptor pairs and works directly for specific downstream response (expression of FCEs regulated target genes) in a particular microenvironment. HoloNet is designed for decoding FCEs using spatial transcriptomic data by integrating ligand–receptor pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. (Uncategorized / Uncategorized)

README

        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 `_.

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 `_:

.. code-block::

        

        pip install HoloNet

Getting started

^^^^^^^^^^^^^^^

Please refer to the `Documentation `_, including:

- `Tutorials `_

- `API `_

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