提出Spotscape框架，提升空间转录组中细胞点的全局表征能力。
Global Context-aware Representation Learning for Spatially Resolved Transcriptomics
- 引入相似性望远镜模块，捕捉多点间的全局关系。
- 通过相似性缩放策略，实现多切片数据的有效整合。
- 特别改善边界区域点的表征，适用于复杂组织研究。
空间分辨转录组（SRT）是一种前沿技术，可捕获组织内细胞的空间上下文，用于研究复杂的生物网络。现有基于图的方法虽融合基因表达与空间信息以识别相关空间域，但在获取有意义的点表征方面仍不足，尤其在空间域边界附近的点表现不佳，因过度依赖邻近点而忽略特征差异较小的锚点。为此，我们提出Spotscape框架，引入相似性望远镜模块以捕捉多个点之间的全局关系，并设计相似性缩放策略，调节切片内与切片间点的距离，促进有效多切片集成。大量实验表明，Spotscape在单切片和多切片多种下游任务中均具优越性能。代码已开源：https://github.com/yunhak0/Spotscape。
原文摘要 · Abstract (English)
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.
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