scArchest 0.0.1

Transfer learning with Architecture Surgery on Single-cell data

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scArches (PyTorch) - single-cell architecture surgery

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This is a Pytorch version of scArches which can be found here <https://github.com/theislab/scArches/>. 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://scarchest.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/scarchesp/issues/new>__ or reach us by email <mailto:[e-mail],[e-mail],[e-mail]>_. 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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