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Cell. 2019 Jun 13;177(7):1888-1902.e21. doi: 10.1016/j.cell.2019.05.031. Epub 2019 Jun 6.

Comprehensive Integration of Single-Cell Data.

Author information

1
New York Genome Center, New York, NY, USA.
2
New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA.
3
Technology Innovation Lab, New York Genome Center, New York, NY, USA.
4
New York Genome Center, New York, NY, USA; Center for Genomics and Systems Biology, New York University, New York, NY, USA. Electronic address: rsatija@nygenome.org.

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.

KEYWORDS:

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

PMID:
31178118
PMCID:
PMC6687398
[Available on 2020-06-13]
DOI:
10.1016/j.cell.2019.05.031

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