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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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