Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning
Multi-view graph approaches could enhance the analysis of tissue heterogeneity in spatial transcriptomics. Here, the authors develop the Spatial Transcriptomics data analysis by Multiple View Collaborative-learning - stMVC - framework, and apply it to detect spatial domains and cell states in brain...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-33619-9 |
| _version_ | 1817983938665119744 |
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| author | Chunman Zuo Yijian Zhang Chen Cao Jinwang Feng Mingqi Jiao Luonan Chen |
| author_facet | Chunman Zuo Yijian Zhang Chen Cao Jinwang Feng Mingqi Jiao Luonan Chen |
| author_sort | Chunman Zuo |
| collection | DOAJ |
| description | Multi-view graph approaches could enhance the analysis of tissue heterogeneity in spatial transcriptomics. Here, the authors develop the Spatial Transcriptomics data analysis by Multiple View Collaborative-learning - stMVC - framework, and apply it to detect spatial domains and cell states in brain and tumor tissues. |
| first_indexed | 2024-04-13T23:39:23Z |
| format | Article |
| id | doaj.art-dcecdfd9d65a44fba9e667c202b3545e |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-13T23:39:23Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-dcecdfd9d65a44fba9e667c202b3545e2022-12-22T02:24:36ZengNature PortfolioNature Communications2041-17232022-10-0113111410.1038/s41467-022-33619-9Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learningChunman Zuo0Yijian Zhang1Chen Cao2Jinwang Feng3Mingqi Jiao4Luonan Chen5Institute of Artificial Intelligence, Donghua UniversityDepartment of General Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineSchool of Biomedical Engineering and Informatics, Nanjing Medical UniversityKey Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical UniversityKey Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of SciencesKey Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of SciencesMulti-view graph approaches could enhance the analysis of tissue heterogeneity in spatial transcriptomics. Here, the authors develop the Spatial Transcriptomics data analysis by Multiple View Collaborative-learning - stMVC - framework, and apply it to detect spatial domains and cell states in brain and tumor tissues.https://doi.org/10.1038/s41467-022-33619-9 |
| spellingShingle | Chunman Zuo Yijian Zhang Chen Cao Jinwang Feng Mingqi Jiao Luonan Chen Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning Nature Communications |
| title | Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning |
| title_full | Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning |
| title_fullStr | Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning |
| title_full_unstemmed | Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning |
| title_short | Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning |
| title_sort | elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi view graph collaborative learning |
| url | https://doi.org/10.1038/s41467-022-33619-9 |
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