Mapping single-cell data to reference atlases by transfer learning.

ABSTRACT: Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. 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. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing bat

SUBMITTER: Lotfollahi M 

PROVIDER: S-EPMC8763644 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature