期刊
NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-022-33758-z
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资金
- State Key Research Development Program of China [2019YFA0110002]
- National Natural Science Foundation of China [32125007, 91940306]
- Beijing Advanced Innovation Center for Structural Biology
- Tsinghua-Peking Joint Center for Life Sciences
- Tsinghua University Branch of China National Center for Protein Sciences
- King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) [FCC/1/1976-44-01, FCC/1/1976-45-01, URF/1/4352-01-01, URF/1/4663-01-01]
SCALEX is a deep-learning method for online integration of diverse single-cell data. It accurately aligns different modalities of single-cell data, retains true biological differences, and has superior performance in large-scale single-cell applications.
Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.
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