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• W2949177718 endingPage "1902.e21" @default.
• W2949177718 startingPage "1888" @default.
• W2949177718 abstract "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." @default.
• W2949177718 created "2019-06-27" @default.
• W2949177718 creator A5003957777 @default.
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• W2949177718 creator A5072183933 @default.
• W2949177718 creator A5078971011 @default.
• W2949177718 creator A5084129649 @default.
• W2949177718 date "2019-06-01" @default.
• W2949177718 modified "2023-10-18" @default.
• W2949177718 title "Comprehensive Integration of Single-Cell Data" @default.
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