Bering 0.1.1

Bering: Transfer Learning of Cell Segmentation and Annotation for Spatial Omics

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