Computational Approaches and Challenges in Spatial Transcriptomics
The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Genomics, Proteomics & Bioinformatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1672022922001292 |
| _version_ | 1797330595006644224 |
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| author | Shuangsang Fang Bichao Chen Yong Zhang Haixi Sun Longqi Liu Shiping Liu Yuxiang Li Xun Xu |
| author_facet | Shuangsang Fang Bichao Chen Yong Zhang Haixi Sun Longqi Liu Shiping Liu Yuxiang Li Xun Xu |
| author_sort | Shuangsang Fang |
| collection | DOAJ |
| description | The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development. |
| first_indexed | 2024-03-08T07:22:04Z |
| format | Article |
| id | doaj.art-27d821db2f914f399ad2e56aab663076 |
| institution | Directory Open Access Journal |
| issn | 1672-0229 |
| language | English |
| last_indexed | 2024-03-08T07:22:04Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Genomics, Proteomics & Bioinformatics |
| spelling | doaj.art-27d821db2f914f399ad2e56aab6630762024-02-02T23:04:15ZengElsevierGenomics, Proteomics & Bioinformatics1672-02292023-02-012112447Computational Approaches and Challenges in Spatial TranscriptomicsShuangsang Fang0Bichao Chen1Yong Zhang2Haixi Sun3Longqi Liu4Shiping Liu5Yuxiang Li6Xun Xu7BGI-Shenzhen, Shenzhen 518083, China; BGI-Beijing, Beijing 100101, ChinaBGI-Shenzhen, Shenzhen 518083, ChinaBGI-Shenzhen, Shenzhen 518083, China; Guangdong Bigdata Engineering Technology Research Center for Life Sciences, Shenzhen 518083, ChinaBGI-Shenzhen, Shenzhen 518083, ChinaBGI-Shenzhen, Shenzhen 518083, ChinaBGI-Shenzhen, Shenzhen 518083, ChinaBGI-Shenzhen, Shenzhen 518083, China; Guangdong Bigdata Engineering Technology Research Center for Life Sciences, Shenzhen 518083, China; Corresponding authors.BGI-Shenzhen, Shenzhen 518083, China; Corresponding authors.The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.http://www.sciencedirect.com/science/article/pii/S1672022922001292Spatial transcriptomicsComputational approachData qualityData interpretationMulti-omics integration |
| spellingShingle | Shuangsang Fang Bichao Chen Yong Zhang Haixi Sun Longqi Liu Shiping Liu Yuxiang Li Xun Xu Computational Approaches and Challenges in Spatial Transcriptomics Genomics, Proteomics & Bioinformatics Spatial transcriptomics Computational approach Data quality Data interpretation Multi-omics integration |
| title | Computational Approaches and Challenges in Spatial Transcriptomics |
| title_full | Computational Approaches and Challenges in Spatial Transcriptomics |
| title_fullStr | Computational Approaches and Challenges in Spatial Transcriptomics |
| title_full_unstemmed | Computational Approaches and Challenges in Spatial Transcriptomics |
| title_short | Computational Approaches and Challenges in Spatial Transcriptomics |
| title_sort | computational approaches and challenges in spatial transcriptomics |
| topic | Spatial transcriptomics Computational approach Data quality Data interpretation Multi-omics integration |
| url | http://www.sciencedirect.com/science/article/pii/S1672022922001292 |
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