Integrative single-cell analysis
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.
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Existing computational best practices and tool recommendations for scRNA-seq are outdated or incomplete.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best practices and tool recommendations have previously been outlined for scRNA-seq2,5,6,17, they are either outdated or incomplete.”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
- Licence
- www.springer.com/tdm (tdm)
- 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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