期刊
CELL
卷 177, 期 7, 页码 1888-+出版社
CELL PRESS
DOI: 10.1016/j.cell.2019.05.031
关键词
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资金
- NIH New Innovator Award [1DP2HG00962301]
- NIH Human Biomolecular Atlas Program Award [1OT2OD026673-01, 5R01MH071679-12]
- Chan Zuckerberg Awards [HCA-A1704-01895, HCA2-A-1708-02755]
- NSF Graduate Fellowship [DGE1342536]
Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to anchor diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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