scRNA‐seq data analysis method to improve analysis performance
Abstract 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 t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-05-01
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| Series: | IET Nanobiotechnology |
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| Online Access: | https://doi.org/10.1049/nbt2.12115 |
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| _version_ | 1832559564840501248 |
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| author | Junru Lu Yuqi Sheng Weiheng Qian Min Pan Xiangwei Zhao Qinyu Ge |
| author_facet | Junru Lu Yuqi Sheng Weiheng Qian Min Pan Xiangwei Zhao Qinyu Ge |
| author_sort | Junru Lu |
| collection | DOAJ |
| description | Abstract 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 | Article |
| id | doaj-art-7cd818632821400687a006569e4f9e6a |
| institution | Kabale University |
| issn | 1751-8741 1751-875X |
| language | English |
| publishDate | 2023-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Nanobiotechnology |
| spelling | doaj-art-7cd818632821400687a006569e4f9e6a2025-02-03T01:29:43ZengWileyIET Nanobiotechnology1751-87411751-875X2023-05-0117324625610.1049/nbt2.12115scRNA‐seq data analysis method to improve analysis performanceJunru Lu0Yuqi Sheng1Weiheng Qian2Min Pan3Xiangwei Zhao4Qinyu Ge5State Key Laboratory of Bioelectronics School of Biological Science & Medical Engineering Southeast University Nanjing ChinaState Key Laboratory of Bioelectronics School of Biological Science & Medical Engineering Southeast University Nanjing ChinaState Key Laboratory of Bioelectronics School of Biological Science & Medical Engineering Southeast University Nanjing ChinaSchool of Medicine Southeast University Nanjing ChinaState Key Laboratory of Bioelectronics School of Biological Science & Medical Engineering Southeast University Nanjing ChinaState Key Laboratory of Bioelectronics School of Biological Science & Medical Engineering Southeast University Nanjing ChinaAbstract 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.https://doi.org/10.1049/nbt2.12115bioinformaticsbiomedical engineeringgenomicsRNA |
| spellingShingle | Junru Lu Yuqi Sheng Weiheng Qian Min Pan Xiangwei Zhao Qinyu Ge scRNA‐seq data analysis method to improve analysis performance IET Nanobiotechnology bioinformatics biomedical engineering genomics RNA |
| 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 | bioinformatics biomedical engineering genomics RNA |
| url | https://doi.org/10.1049/nbt2.12115 |
| work_keys_str_mv | AT junrulu scrnaseqdataanalysismethodtoimproveanalysisperformance AT yuqisheng scrnaseqdataanalysismethodtoimproveanalysisperformance AT weihengqian scrnaseqdataanalysismethodtoimproveanalysisperformance AT minpan scrnaseqdataanalysismethodtoimproveanalysisperformance AT xiangweizhao scrnaseqdataanalysismethodtoimproveanalysisperformance AT qinyuge scrnaseqdataanalysismethodtoimproveanalysisperformance |