Transfer learning with Architecture Surgery on Single-cell data
Project description
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scArches - single-cell architecture surgery
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scArches is a package to integrate newly produced single-cell datasets into integrated reference atlases. Our method can facilitate large collaborative projects with decentralise training and integration of multiple datasets by different groups. scArches is compatible with scanpy <https://scanpy.readthedocs.io/en/stable/>_, and hosts efficient implementations of all conditional generative models for single-cell data.
What can you do with scArches?
- Integrate many single-cell datasets and share the trained model and the data (if possible).
- Download a pre-trained model for your atlas of interest, update it with new datasets and share with your collaborators.
- Construct a customized reference by downloading a reference atlas, add a few pre-trained adaptors (datasets) and project your own data in to this customized reference atlas.
- Project and integrate query datasets on the top of a reference and use latent representation for downstream tasks, e.g.: diff testing, clustering.
Usage and installation
See here <https://scarches.readthedocs.io/>_ for documentation and tutorials.
Support and contribute
If you have a question or new architecture or a model that could be integrated into our pipeline, you can
post an issue <https://github.com/theislab/scarches/issues/new>__. Our package supports tf/keras now but pytorch version will be added very soon.
Reference
If scArches is useful in your research, please consider citing this preprint <https://www.biorxiv.org/content/10.1101/2020.07.16.205997v1/>_.
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