Cell
Volume 177, Issue 7, 13 June 2019, Pages 1888-1902.e21
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Comprehensive Integration of Single-Cell Data

https://doi.org/10.1016/j.cell.2019.05.031Get rights and content
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Highlights

  • Seurat v3 identifies correspondences between cells in different experiments

  • These “anchors” can be used to harmonize datasets into a single reference

  • Reference labels and data can be projected onto query datasets

  • Extends beyond RNA-seq to single-cell protein, chromatin, and spatial data

Summary

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.

Keywords

single cell
integration
multi-modal
single-cell RNA sequencing
scRNA-seq
single-cell ATAC sequencing
scATAC-seq

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4

These authors contributed equally

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