Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1785043289256230912 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10190501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lujunru scrnaseqdataanalysismethodtoimproveanalysisperformance AT shengyuqi scrnaseqdataanalysismethodtoimproveanalysisperformance AT qianweiheng scrnaseqdataanalysismethodtoimproveanalysisperformance AT panmin scrnaseqdataanalysismethodtoimproveanalysisperformance AT zhaoxiangwei scrnaseqdataanalysismethodtoimproveanalysisperformance AT geqinyu scrnaseqdataanalysismethodtoimproveanalysisperformance |