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scRNA‐seq data analysis method to improve analysis performance

With the development of single‐cell RNA sequencing technology (scRNA‐seq), we have the ability to study biological questions at the level of the individual cell transcriptome. Nowadays, many analysis tools, specifically suitable for single‐cell RNA sequencing data, have been developed. In this revie...

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Autores principales: Lu, Junru, Sheng, Yuqi, Qian, Weiheng, Pan, Min, Zhao, Xiangwei, Ge, Qinyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10190501/
https://www.ncbi.nlm.nih.gov/pubmed/36727937
http://dx.doi.org/10.1049/nbt2.12115
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author Lu, Junru
Sheng, Yuqi
Qian, Weiheng
Pan, Min
Zhao, Xiangwei
Ge, Qinyu
author_facet Lu, Junru
Sheng, Yuqi
Qian, Weiheng
Pan, Min
Zhao, Xiangwei
Ge, Qinyu
author_sort Lu, Junru
collection PubMed
description With the development of single‐cell RNA sequencing technology (scRNA‐seq), we have the ability to study biological questions at the level of the individual cell transcriptome. Nowadays, many analysis tools, specifically suitable for single‐cell RNA sequencing data, have been developed. In this review, the currently commonly used scRNA‐seq protocols are discussed. The upstream processing flow pipeline of scRNA‐seq data, including goals and popular tools for reads mapping and expression quantification, quality control, normalization, imputation, and batch effect removal is also introduced. Finally, methods to evaluate these tools in both cellular and genetic dimensions, clustering and differential expression analysis are presented.
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spelling pubmed-101905012023-05-18 scRNA‐seq data analysis method to improve analysis performance Lu, Junru Sheng, Yuqi Qian, Weiheng Pan, Min Zhao, Xiangwei Ge, Qinyu IET Nanobiotechnol Selected Extended Papers from the 12th International Conference on Post‐genomic Technologies With the development of single‐cell RNA sequencing technology (scRNA‐seq), we have the ability to study biological questions at the level of the individual cell transcriptome. Nowadays, many analysis tools, specifically suitable for single‐cell RNA sequencing data, have been developed. In this review, the currently commonly used scRNA‐seq protocols are discussed. The upstream processing flow pipeline of scRNA‐seq data, including goals and popular tools for reads mapping and expression quantification, quality control, normalization, imputation, and batch effect removal is also introduced. Finally, methods to evaluate these tools in both cellular and genetic dimensions, clustering and differential expression analysis are presented. John Wiley and Sons Inc. 2023-02-02 /pmc/articles/PMC10190501/ /pubmed/36727937 http://dx.doi.org/10.1049/nbt2.12115 Text en © 2023 The Authors. IET Nanobiotechnology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Selected Extended Papers from the 12th International Conference on Post‐genomic Technologies
Lu, Junru
Sheng, Yuqi
Qian, Weiheng
Pan, Min
Zhao, Xiangwei
Ge, Qinyu
scRNA‐seq data analysis method to improve analysis performance
title scRNA‐seq data analysis method to improve analysis performance
title_full scRNA‐seq data analysis method to improve analysis performance
title_fullStr scRNA‐seq data analysis method to improve analysis performance
title_full_unstemmed scRNA‐seq data analysis method to improve analysis performance
title_short scRNA‐seq data analysis method to improve analysis performance
title_sort scrna‐seq data analysis method to improve analysis performance
topic Selected Extended Papers from the 12th International Conference on Post‐genomic Technologies
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10190501/
https://www.ncbi.nlm.nih.gov/pubmed/36727937
http://dx.doi.org/10.1049/nbt2.12115
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