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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.20660 |
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| _version_ | 1866913454753841152 |
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| author | Chelebian, Eduard Avenel, Christophe Wählby, Carolina |
| author_facet | Chelebian, Eduard Avenel, Christophe Wählby, Carolina |
| contents | Spatial omics has transformed our understanding of tissue architecture by preserving spatial context of gene expression patterns. Simultaneously, advances in imaging AI have enabled extraction of morphological features describing the tissue. The intersection of spatial omics and imaging AI presents opportunities for a more holistic understanding. In this review we introduce a framework for categorizing spatial omics-morphology combination methods, focusing on how morphological features can be translated or integrated into spatial omics analyses. By translation we mean finding morphological features that spatially correlate with gene expression patterns with the purpose of predicting gene expression. Such features can be used to generate super-resolution gene expression maps or infer genetic information from clinical H&E-stained samples. By integration we mean finding morphological features that spatially complement gene expression patterns with the purpose of enriching information. Such features can be used to define spatial domains, especially where gene expression has preceded morphological changes and where morphology remains after gene expression. We discuss learning strategies and directions for further development of the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20660 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What makes for good morphology representations for spatial omics? Chelebian, Eduard Avenel, Christophe Wählby, Carolina Computer Vision and Pattern Recognition Spatial omics has transformed our understanding of tissue architecture by preserving spatial context of gene expression patterns. Simultaneously, advances in imaging AI have enabled extraction of morphological features describing the tissue. The intersection of spatial omics and imaging AI presents opportunities for a more holistic understanding. In this review we introduce a framework for categorizing spatial omics-morphology combination methods, focusing on how morphological features can be translated or integrated into spatial omics analyses. By translation we mean finding morphological features that spatially correlate with gene expression patterns with the purpose of predicting gene expression. Such features can be used to generate super-resolution gene expression maps or infer genetic information from clinical H&E-stained samples. By integration we mean finding morphological features that spatially complement gene expression patterns with the purpose of enriching information. Such features can be used to define spatial domains, especially where gene expression has preceded morphological changes and where morphology remains after gene expression. We discuss learning strategies and directions for further development of the field. |
| title | What makes for good morphology representations for spatial omics? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.20660 |