Cited by 1,460Single-cell and spatial transcriptomicsBioinformatics and Genomic NetworksGene Regulatory Network Analysis

Integrative single-cell analysis

Stuart, Tim, Satija, Rahul·2019·Nature Reviews Genetics

Abstract

The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.

Integrity signals

Registry

Crossref · 10.1038/s41576-019-0093-7

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133 of 133 references verified0 citation errors · 0 not checkable

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  • Existing computational best practices and tool recommendations for scRNA-seq are outdated or incomplete.

    “although computational best practices and tool recommendations have previously been outlined for scRNA-seq2,5,6,17, they are either outdated or incomplete.”

    From Best practices for single-cell analysis across modalities (2023) · cites this paper as [17]

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Authors
Stuart, T., & Satija, R.
Affiliations
New York Genome Center (US)
Corresponding author
Satija, Rahul
Venue
Nature Reviews Genetics
Published
2019-01-29
Volume
20
Issue
5
Pages
257-272
ISSN
  • 1471-0056 (ISSN-L)
  • 1471-0064
OpenAlex
W2912390237
Cited by
1,460
References
133
Citations by year
2026: 216 · 2025: 209 · 2024: 157 · 2023: 176 · 2022: 199 · 2021: 255 · 2020: 181 · 2019: 65 · 2015: 1 · 2012: 1
FWCI
15.53
Type
journal article
Language
en
Open access
closed
Locations
  • Nature Reviews Genetics (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institutes of Health
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
single-cell multi-omics integration, single-cell RNA sequencing, epigenomics, spatial transcriptomics, proteomics, single-cell lineage tracing
MeSH terms
Humans, Proteins, RNA, Computational Biology, Epigenesis, Genetic, Data Mining, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Datasets as Topic

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