标题： SToFM：空间转录组学的多尺度基础模型 显示英文标题

标题： SToFM: a Multi-scale Foundation Model for Spatial Transcriptomics

摘要： 空间转录组学（ST）技术通过保留细胞的空间上下文，为生物学家提供了关于单细胞生物学的丰富见解。 为ST构建基础模型可以显著增强对大量和复杂数据源的分析，揭示生物组织复杂性的新视角。 然而，由于需要从包含大量细胞的组织切片中提取多尺度信息，建模ST数据本质上具有挑战性。 这个过程需要整合宏观尺度的组织形态、微观尺度的细胞微环境以及基因尺度的基因表达谱。 为了解决这一挑战，我们提出了SToFM，一个多层次的空间转录组学基础模型。 SToFM首先对每个ST切片进行多尺度信息提取，以构建一组聚合宏观、微观和基因尺度信息的ST子切片。 然后使用SE(2)变换器从子切片中获得高质量的细胞表示。 此外，我们构建了\textbf{SToCorpus-88M}，这是最大的高分辨率空间转录组学语料库，用于预训练。 SToFM在多种下游任务中表现出色，例如组织区域语义分割和细胞类型注释，通过捕捉和整合多尺度信息展示了其对ST数据的全面理解。 显示英文摘要

摘要： Spatial Transcriptomics (ST) technologies provide biologists with rich insights into single-cell biology by preserving spatial context of cells. Building foundational models for ST can significantly enhance the analysis of vast and complex data sources, unlocking new perspectives on the intricacies of biological tissues. However, modeling ST data is inherently challenging due to the need to extract multi-scale information from tissue slices containing vast numbers of cells. This process requires integrating macro-scale tissue morphology, micro-scale cellular microenvironment, and gene-scale gene expression profile. To address this challenge, we propose SToFM, a multi-scale Spatial Transcriptomics Foundation Model. SToFM first performs multi-scale information extraction on each ST slice, to construct a set of ST sub-slices that aggregate macro-, micro- and gene-scale information. Then an SE(2) Transformer is used to obtain high-quality cell representations from the sub-slices. Additionally, we construct \textbf{SToCorpus-88M}, the largest high-resolution spatial transcriptomics corpus for pretraining. SToFM achieves outstanding performance on a variety of downstream tasks, such as tissue region semantic segmentation and cell type annotation, demonstrating its comprehensive understanding of ST data through capturing and integrating multi-scale information.