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• W2898821675 abstract "Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to “anchor” diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat" @default.
• W2898821675 created "2018-11-09" @default.
• W2898821675 creator A5003957777 @default.
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• W2898821675 date "2018-11-02" @default.
• W2898821675 modified "2023-10-16" @default.
• W2898821675 title "Comprehensive integration of single cell data" @default.
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• W2898821675 doi "https://doi.org/10.1101/460147" @default.
• W2898821675 hasPublicationYear "2018" @default.
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