SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics
The basic analysis steps of spatial transcriptomics require obtaining gene expression information from both space and cells. The existing tools for these analyses incur performance issues when dealing with large datasets. These issues involve computationally intensive spatial localizati...
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| Format: | Article |
| Language: | English |
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GigaScience Press
2024-02-01
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| Series: | GigaByte |
| Online Access: | https://gigabytejournal.com/articles/111 |
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| author | Chun Gong Shengkang Li Leying Wang Fuxiang Zhao Shuangsang Fang Dong Yuan Zijian Zhao Qiqi He Mei Li Weiqing Liu Zhaoxun Li Hongqing Xie Sha Liao Ao Chen Yong Zhang Yuxiang Li Xun Xu |
| author_facet | Chun Gong Shengkang Li Leying Wang Fuxiang Zhao Shuangsang Fang Dong Yuan Zijian Zhao Qiqi He Mei Li Weiqing Liu Zhaoxun Li Hongqing Xie Sha Liao Ao Chen Yong Zhang Yuxiang Li Xun Xu |
| author_sort | Chun Gong |
| collection | DOAJ |
| description |
The basic analysis steps of spatial transcriptomics require obtaining gene expression information from both space and cells. The existing tools for these analyses incur performance issues when dealing with large datasets. These issues involve computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the analysis. Here, a high-performance and accurate spatial transcriptomics data analysis workflow, called Stereo-seq Analysis Workflow (SAW), was developed for the Stereo-seq technology developed at BGI. SAW includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation, and clustering. The workflow outputs files in a universal format for subsequent personalized analysis. The execution time for the entire analysis is ∼148 min with 1 GB reads 1 × 1 cm chip test data, 1.8 times faster than with an unoptimized workflow.
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| format | Article |
| id | doaj-art-26cefe0a254d499eae1deed26e7f694e |
| institution | DOAJ |
| issn | 2709-4715 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | GigaScience Press |
| record_format | Article |
| series | GigaByte |
| spelling | doaj-art-26cefe0a254d499eae1deed26e7f694e2025-08-20T03:13:10ZengGigaScience PressGigaByte2709-47152024-02-0110.46471/gigabyte.111SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomicsChun Gong 0https://orcid.org/0009-0004-0902-0654Shengkang Li 1Leying Wang 2Fuxiang Zhao 3Shuangsang Fang 4https://orcid.org/0000-0002-4126-0074Dong Yuan 5Zijian Zhao 6Qiqi He 7Mei Li 8https://orcid.org/0000-0003-3310-2911Weiqing Liu 9Zhaoxun Li 10Hongqing Xie 11Sha Liao 12Ao Chen 13https://orcid.org/0000-0002-9699-8340Yong Zhang 14Yuxiang Li 15Xun Xu 16https://orcid.org/0000-0002-5338-5173BGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, China, BGI-Beijing, Beijing, 102601, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Shenzhen, Shenzhen, Guangdong, ChinaBGI-Wuhan, Wuhan, Hubei, China The basic analysis steps of spatial transcriptomics require obtaining gene expression information from both space and cells. The existing tools for these analyses incur performance issues when dealing with large datasets. These issues involve computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the analysis. Here, a high-performance and accurate spatial transcriptomics data analysis workflow, called Stereo-seq Analysis Workflow (SAW), was developed for the Stereo-seq technology developed at BGI. SAW includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation, and clustering. The workflow outputs files in a universal format for subsequent personalized analysis. The execution time for the entire analysis is ∼148 min with 1 GB reads 1 × 1 cm chip test data, 1.8 times faster than with an unoptimized workflow. https://gigabytejournal.com/articles/111 |
| spellingShingle | Chun Gong Shengkang Li Leying Wang Fuxiang Zhao Shuangsang Fang Dong Yuan Zijian Zhao Qiqi He Mei Li Weiqing Liu Zhaoxun Li Hongqing Xie Sha Liao Ao Chen Yong Zhang Yuxiang Li Xun Xu SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics GigaByte |
| title | SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics |
| title_full | SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics |
| title_fullStr | SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics |
| title_full_unstemmed | SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics |
| title_short | SAW: an efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics |
| title_sort | saw an efficient and accurate data analysis workflow for stereo seq spatial transcriptomics |
| url | https://gigabytejournal.com/articles/111 |
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