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Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC
SkewC is a single-cell RNA sequencing (scRNA-seq) data quality evaluation tool. The approach is based on determining gene body coverage, and its skewness, as a quality metric for each individual cell. SkewC distinguishes between two types of single cells: typical cells with prototypical gene body co...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873502/ https://www.ncbi.nlm.nih.gov/pubmed/36853658 http://dx.doi.org/10.1016/j.xpro.2022.102038 |
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author | Abugessaisa, Imad Hasegawa, Akira Katayama, Shintaro Kere, Juha Kasukawa, Takeya |
author_facet | Abugessaisa, Imad Hasegawa, Akira Katayama, Shintaro Kere, Juha Kasukawa, Takeya |
author_sort | Abugessaisa, Imad |
collection | PubMed |
description | SkewC is a single-cell RNA sequencing (scRNA-seq) data quality evaluation tool. The approach is based on determining gene body coverage, and its skewness, as a quality metric for each individual cell. SkewC distinguishes between two types of single cells: typical cells with prototypical gene body coverage profiles and skewed cells with skewed gene body coverage profiles. SkewC can be used on any scRNA-seq data as it is independent from the underlying technology used to generate the data. For complete details on the use and execution of this protocol, please refer to Abugessaisa et al. (2022).(1) |
format | Online Article Text |
id | pubmed-9873502 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-98735022023-01-26 Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC Abugessaisa, Imad Hasegawa, Akira Katayama, Shintaro Kere, Juha Kasukawa, Takeya STAR Protoc Protocol SkewC is a single-cell RNA sequencing (scRNA-seq) data quality evaluation tool. The approach is based on determining gene body coverage, and its skewness, as a quality metric for each individual cell. SkewC distinguishes between two types of single cells: typical cells with prototypical gene body coverage profiles and skewed cells with skewed gene body coverage profiles. SkewC can be used on any scRNA-seq data as it is independent from the underlying technology used to generate the data. For complete details on the use and execution of this protocol, please refer to Abugessaisa et al. (2022).(1) Elsevier 2023-01-18 /pmc/articles/PMC9873502/ /pubmed/36853658 http://dx.doi.org/10.1016/j.xpro.2022.102038 Text en © 2022 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 | Protocol Abugessaisa, Imad Hasegawa, Akira Katayama, Shintaro Kere, Juha Kasukawa, Takeya Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC |
title | Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC |
title_full | Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC |
title_fullStr | Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC |
title_full_unstemmed | Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC |
title_short | Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC |
title_sort | computational approach to evaluate scrna-seq data quality and gene body coverage with skewc |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873502/ https://www.ncbi.nlm.nih.gov/pubmed/36853658 http://dx.doi.org/10.1016/j.xpro.2022.102038 |
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