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.
- 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.
See here for documentation and tutorials.
If you have a question or new architecture or a model that could be integrated into our pipeline, you can post an issue. Our package supports tf/keras now but pytorch version will be added very soon.
If scArches is useful in your research, please consider citing this preprint.