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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.

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

Manuscript

Please refer to our manuscript Jin, Zhang et al. (2023)_ for more details.

Contact

We are happy about any feedback! If you have any questions, please feel free to contact Kang.Jin@cchmc, zuobai.zhang@mila.quebec. Find more research in Shu_Jian_Lab_.

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.. _Jin, Zhang et al. (2023): https://www.biorxiv.org/content/10.1101/2023.09.19.558548v1 .. _scanpy: https://scanpy.readthedocs.io/en/stable/ .. _torch_geometric: https://pytorch-geometric.readthedocs.io/en/latest/ .. _documentation: https://bering.readthedocs.io/en/latest/ .. _Shu_Jian_Lab: https://www.jianshulab.org/team