scArches (PyTorch) - single-cell architecture surgery

This is a Pytorch version of scArches which can be found here. 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, and hosts efficient implementations of all conditional generative models for single-cell data.

Note

This is an ongoing project, for all `svVI <https://www.scvi-tools.org/en/latest/`_ models please refere to the original code.

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 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 or reach us by email. 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.