Metadata-Version: 2.1
Name: Bering
Version: 0.1.1
Summary: Bering: Transfer Learning of Cell Segmentation and Annotation for Spatial Omics
Home-page: https://github.com/KANG-BIOINFO/Bering
Author: Kang Jin
Author-email: jinkg@mail.uc.edu
Maintainer: Kang Jin
Project-URL: Ducumentation, https://celldrift.readthedocs.io/en/latest/index.html#
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scanpy (>=1.6.0)
Requires-Dist: matplotlib
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: igraph
Requires-Dist: leidenalg
Requires-Dist: torch (==1.12.1)
Requires-Dist: torch-geometric (==2.3.1)

Bering - Spatial Segmentation and Cell Annotation in Python
================================================

**Bering** is a deep learning algorithm for simultaneous molecular annotation and cell segmentation in single-cell spatial transcriptomics data. 
It builds on top of `torch_geometric`_ and `scanpy`_, from which it inherits modularity and scalability.
It provides versatile models that leverages the spatial coordinates of the data, as well as pre-trained models across spatial technologies and tissues.

Visit our `documentation`_ for installation, tutorials, examples and more.

Manuscript
----------
The manuscript has been submittet for peer review. A preprint will be released soon.

Bering's key applications
--------------------------
- Identify background and real signals in noisy spatial transcriptomics data.
- Identify cell annotations for transcripts on single-cell spatial data.
- Efficiently cell segmentation with cell annotations.
- Build and fine-tune pre-trained model on new data using transfer learning.

Installation
------------
Install Bering via PyPI by running::

    pip install Bering

or via Conda as::

    conda install -c conda-forge Bering

Contributing to Bering
-----------------------
We are happy about any contributions! Before you start, check out our `contributing guide <CONTRIBUTING.rst>`_.


.. _Palla, Spitzer et al. (2022): https://doi.org/10.1038/s41592-021-01358-2
.. _scanpy: https://scanpy.readthedocs.io/en/stable/
.. _torch_geometric: https://pytorch-geometric.readthedocs.io/en/latest/
.. _documentation: https://celldrift.readthedocs.io/en/latest/index.html
