The DOI matches its Crossref record; 91 of the 91 references it cites were found in a registry or an index.
Computational approaches for interpreting sc RNA‐seq data
Open accessAbstract
The recent developments in high‐throughput single‐cell RNA sequencing technology (sc RNA‐seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high‐dimensional data mining techniques. Here, we consider biological questions for which sc RNA‐seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene‐level analyses are particularly informative areas of computational analysis.
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“Recently developed single-cell RNA-sequencing technology enabled the generation of vast amounts of the transcriptomic data at cellular resolution19.”
From High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa (2020) · cites this paper as [19]
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High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa
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Metadata
- Authors
- Rostom, R., Svensson, V., Teichmann, S. A., & Kar, G.
- Affiliations
- Wellcome Sanger Institute (GB)
- European Bioinformatics Institute (GB)
- Corresponding author
- Kar, Gozde
- Venue
- FEBS Letters
- Publisher
- Wiley
- Published
- 2017-06-12
- Volume
- 591
- Issue
- 15
- Pages
- 2213-2225
- ISSN
- 0014-5793 (ISSN-L)
- 1873-3468
- PMC
- PMC5575496
- OpenAlex
- W2615816755
- Cited by
- 107
- References
- 89
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 18 · 2021: 14 · 2020: 19 · 2019: 20 · 2018: 9 · 2017: 11
- FWCI
- 1.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- FEBS Letters (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, scRNA-seq data analysis, cell clustering, gene expression analysis, high-dimensional data mining
- MeSH terms
- Animals, Humans, Gene Expression, Cluster Analysis, Sequence Analysis, RNA, Computational Biology, Single-Cell Analysis
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