scArches 0.3.0

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?

• Construct single or multi-modal (CITE-seq) reference atlases and share the trained model and the data (if possible).
• Download a pre-trained model for your atlas of interest, update it wih new datasets and share with your collaborators.
• Project and integrate query datasets on the top of a reference and use latent representation for downstream tasks, e.g.:diff testing, clustering, classification

What are different models?

scArches is itself and algorithm to map to project query on the top of reference datasets and is applicable to different models. Here we provide a short explanation and hints when to use which model. Our models are divided into three categories:

What are different models?

scArches is itself and algorithm to map to project query on the top of reference datasets and is applicable to different models. Here we provide a short explanation and hints when to use which model. Our models are divided into three categories:

Unsupervised This class of algortihms need no cell type labels, meaning that you can creat a reference and project a query without having access to cell type labeles. We implemented two algorithms:

• scVI (Lopez et al.,2018 <https://www.nature.com/articles/s41592-018-0229-2>_.): Requires access to raw counts values for data integration and assumes count distribution on the data (NB, ZINB, Poission).

• trVAE (Lotfollahi et al.,2019 <https://arxiv.org/abs/1910.01791>_.): It supports both normalized log tranformed or count data as input and applies additional MMD loss to have better mearging in the latent space.

Supervised and Semi-supervised This class of algorithmes assume the user has access to cell type labels when creating the reference data and usaully perfomr better integration compared to. unsupervised methods. However, the query data still can be unlabaled. In addition to integration , you can classify your query cells using these methods.

• scANVI (Xu et al.,2019 <https://www.biorxiv.org/content/10.1101/532895v1>_.): It neeeds cell type labels for reference data. Your query data can be either unlabeled or labeled. In case of unlabeled query data you can use this method to also classify your query cells using reference labels.

Multi-modal These algorithms can be used to contstruct multi-modal references atlas and map query data from either modalities on the top of the reference.

• totalVI (Gayoso al.,2019 <https://www.biorxiv.org/content/10.1101/532895v1>_.): This model can be used to build multi-modal CITE-seq reference atalses. Query datasets can be either from sc-RNAseq or CITE-seq. In addition to integrating query with reference one can use this model to impute the Proteins in the query datasets.

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>__ or reach us by email <mailto:[e-mail],[e-mail],[e-mail]>_.

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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