Global Context-aware Representation Learning for Spatially Resolved Transcriptomics

Yunhak Oh, Junseok Lee, Yeongmin Kim, Sangwoo Seo, Namkyeong Lee, Chanyoung Park
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:47017-47043, 2025.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-oh25d, title = {Global Context-aware Representation Learning for Spatially Resolved Transcriptomics}, author = {Oh, Yunhak and Lee, Junseok and Kim, Yeongmin and Seo, Sangwoo and Lee, Namkyeong and Park, Chanyoung}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {47017--47043}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/oh25d/oh25d.pdf}, url = {https://proceedings.mlr.press/v267/oh25d.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Global Context-aware Representation Learning for Spatially Resolved Transcriptomics %A Yunhak Oh %A Junseok Lee %A Yeongmin Kim %A Sangwoo Seo %A Namkyeong Lee %A Chanyoung Park %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-oh25d %I PMLR %P 47017--47043 %U https://proceedings.mlr.press/v267/oh25d.html %V 267 %X 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.
APA
Oh, Y., Lee, J., Kim, Y., Seo, S., Lee, N. & Park, C.. (2025). Global Context-aware Representation Learning for Spatially Resolved Transcriptomics. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:47017-47043 Available from https://proceedings.mlr.press/v267/oh25d.html.

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