Global Context-aware Representation Learning for Spatially Resolved Transcriptomics
This addresses a bottleneck in spatial transcriptomics analysis for researchers, though it appears incremental as it builds on existing graph-based methods.
The paper tackles the problem of obtaining meaningful spot representations in Spatially Resolved Transcriptomics, especially near boundaries, by proposing Spotscape with a Similarity Telescope module and similarity scaling strategy, achieving superior performance in downstream tasks.
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.