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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.10936 |
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| _version_ | 1866913545599320064 |
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| author | Zhao, Siyuan Marai, G. Elisabeta |
| author_facet | Zhao, Siyuan Marai, G. Elisabeta |
| contents | Spatial transcriptomics methods capture cellular measurements such as gene expression and cell types at specific locations in a cell, helping provide a localized picture of tissue health. Traditional visualization techniques superimpose the tissue image with pie charts for the cell distribution. We design an interactive visual analysis system that addresses perceptual problems in the state of the art, while adding filtering, drilling, and clustering analysis capabilities. Our approach can help researchers gain deeper insights into the molecular mechanisms underlying complex biological processes within tissues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10936 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Part-to-Whole Circular Cell Explorer Zhao, Siyuan Marai, G. Elisabeta Quantitative Methods Graphics Spatial transcriptomics methods capture cellular measurements such as gene expression and cell types at specific locations in a cell, helping provide a localized picture of tissue health. Traditional visualization techniques superimpose the tissue image with pie charts for the cell distribution. We design an interactive visual analysis system that addresses perceptual problems in the state of the art, while adding filtering, drilling, and clustering analysis capabilities. Our approach can help researchers gain deeper insights into the molecular mechanisms underlying complex biological processes within tissues. |
| title | A Part-to-Whole Circular Cell Explorer |
| topic | Quantitative Methods Graphics |
| url | https://arxiv.org/abs/2410.10936 |