Comprehensive Integration of Single-Cell Data.
Cell
; 177(7): 1888-1902.e21, 2019 06 13.
Article
em En
| MEDLINE
| ID: mdl-31178118
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.
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Base de dados:
MEDLINE
Assunto principal:
Software
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Análise de Sequência de RNA
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Perfilação da Expressão Gênica
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Bases de Dados de Ácidos Nucleicos
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Análise de Célula Única
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Transcriptoma
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article