Global Context-aware Representation Learning for Spatially Resolved Transcriptomics
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915354549157888 |
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| author | Oh, Yunhak Lee, Junseok Kim, Yeongmin Seo, Sangwoo Lee, Namkyeong Park, Chanyoung |
| author_facet | Oh, Yunhak Lee, Junseok Kim, Yeongmin Seo, Sangwoo Lee, Namkyeong Park, Chanyoung |
| contents | Spatially Resolved Transcriptomics (SRT) is a cutting-edge technique that captures the spatial context of cells within tissues, enabling the study of complex biological networks. Recent graph-based methods leverage both gene expression and spatial information to identify relevant spatial domains. However, these approaches fall short in obtaining meaningful spot representations, especially for spots near spatial domain boundaries, as they heavily emphasize adjacent spots that have minimal feature differences from an anchor node. To address this, we propose Spotscape, a novel framework that introduces the Similarity Telescope module to capture global relationships between multiple spots. Additionally, we propose a similarity scaling strategy to regulate the distances between intra- and inter-slice spots, facilitating effective multi-slice integration. Extensive experiments demonstrate the superiority of Spotscape in various downstream tasks, including single-slice and multi-slice scenarios. Our code is available at the following link: https: //github.com/yunhak0/Spotscape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Global Context-aware Representation Learning for Spatially Resolved Transcriptomics Oh, Yunhak Lee, Junseok Kim, Yeongmin Seo, Sangwoo Lee, Namkyeong Park, Chanyoung Machine Learning Computer Vision and Pattern Recognition Spatially Resolved Transcriptomics (SRT) is a cutting-edge technique that captures the spatial context of cells within tissues, enabling the study of complex biological networks. Recent graph-based methods leverage both gene expression and spatial information to identify relevant spatial domains. However, these approaches fall short in obtaining meaningful spot representations, especially for spots near spatial domain boundaries, as they heavily emphasize adjacent spots that have minimal feature differences from an anchor node. To address this, we propose Spotscape, a novel framework that introduces the Similarity Telescope module to capture global relationships between multiple spots. Additionally, we propose a similarity scaling strategy to regulate the distances between intra- and inter-slice spots, facilitating effective multi-slice integration. Extensive experiments demonstrate the superiority of Spotscape in various downstream tasks, including single-slice and multi-slice scenarios. Our code is available at the following link: https: //github.com/yunhak0/Spotscape. |
| title | Global Context-aware Representation Learning for Spatially Resolved Transcriptomics |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.15698 |