Mapping single-cell data to reference atlases by transfer learning

🤖 Plain-English Summary

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states.

🔑 Key Findings

• Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data.
• Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches).
• scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data.

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