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UCell: Robust and scalable single-cell gene signature scoring
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Abstract
UCell is an R package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with Seurat objects. The UCell package and documentation are available on GitHub at https://github.com/carmonalab/UCell.
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“Optionally, hdWGCNA can use alternative gene scoring methods such as or UCell18 or Seurat’s AddModuleScore function, and we show that these scores are correlated with MEs (Figure S3).”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [18]
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Metadata
- Authors
- Andreatta, M., & Carmona, S. J.
- Affiliations
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Andreatta, Massimo
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2021-01
- Volume
- 19
- Pages
- 3796-3798
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC8271111
- OpenAlex
- W3174072032
- Cited by
- 1,104
- References
- 12
- Citations by year
- 2026: 355 · 2025: 337 · 2024: 255 · 2023: 111 · 2022: 39 · 2021: 6
- FWCI
- 36.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- SERVAL (Université de Lausanne) (publishedVersion, cc-by)
- IRIS (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (180010)
- 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 › Bioinformatics and Genomic Networks
- Keywords
- single-cell RNA sequencing, R package, computational efficiency, scalability, biological heterogeneity
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