Comprehensive Integration of Single-Cell Data

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...

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Bibliographic Details
Published in:Cell Vol. 177; no. 7; p. 1888
Main Authors: Stuart, Tim, Butler, Andrew, Hoffman, Paul, Hafemeister, Christoph, Papalexi, Efthymia, Mauck, 3rd, William M, Hao, Yuhan, Stoeckius, Marlon, Smibert, Peter, Satija, Rahul
Format: Journal Article
Language:English
Published: United States 13.06.2019
Subjects:
Databases, Nucleic Acid
Gene Expression Profiling
Humans
Sequence Analysis, RNA
Single-Cell Analysis
Software
Transcriptome
single-cell RNA sequencing
scRNA-seq
single cell
single-cell ATAC sequencing
integration
scATAC-seq
multi-modal
ISSN:1097-4172, 1097-4172
Online Access:Get more information
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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.
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ISSN:1097-4172
1097-4172
DOI:10.1016/j.cell.2019.05.031