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UCell: Robust and scalable single-cell gene signature scoring
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 processin...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271111/ https://www.ncbi.nlm.nih.gov/pubmed/34285779 http://dx.doi.org/10.1016/j.csbj.2021.06.043 |
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author | Andreatta, Massimo Carmona, Santiago J. |
author_facet | Andreatta, Massimo Carmona, Santiago J. |
author_sort | Andreatta, Massimo |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8271111 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-82711112021-07-19 UCell: Robust and scalable single-cell gene signature scoring Andreatta, Massimo Carmona, Santiago J. Comput Struct Biotechnol J Research Article 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. Research Network of Computational and Structural Biotechnology 2021-06-30 /pmc/articles/PMC8271111/ /pubmed/34285779 http://dx.doi.org/10.1016/j.csbj.2021.06.043 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Andreatta, Massimo Carmona, Santiago J. UCell: Robust and scalable single-cell gene signature scoring |
title | UCell: Robust and scalable single-cell gene signature scoring |
title_full | UCell: Robust and scalable single-cell gene signature scoring |
title_fullStr | UCell: Robust and scalable single-cell gene signature scoring |
title_full_unstemmed | UCell: Robust and scalable single-cell gene signature scoring |
title_short | UCell: Robust and scalable single-cell gene signature scoring |
title_sort | ucell: robust and scalable single-cell gene signature scoring |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271111/ https://www.ncbi.nlm.nih.gov/pubmed/34285779 http://dx.doi.org/10.1016/j.csbj.2021.06.043 |
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