HyperST：用于空间转录组学预测的层次化双曲学习
HyperST: Hierarchical Hyperbolic Learning for Spatial Transcriptomics Prediction
将双曲几何引入跨模态对齐，利用层次结构建模图像与基因表达的多层级关系，为视觉-组学跨模态预测提供可复用的表征学习框架。
原文依据 learns multi-level image-gene representations by modeling the data's inherent hierarchy within hyperbolic space
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论空间转录组学（ST）融合了病理图像与基因表达的优势，将分子图谱与组织结构联系起来，从而全面分析斑点级功能。从组织学图像预测基因表达是昂贵ST技术的一种经济有效的替代方案。然而，现有方法主要关注斑点级图像到基因的匹配，未能利用ST数据的完整层次结构，尤其是在基因表达侧，导致图像-基因对齐不完整。此外，一个挑战源于固有的信息不对称：基因表达谱包含更多分子细节，这些细节可能在组织学图像中缺乏显著的视觉对应物，需要复杂的表征学习方法弥合模态差距。我们提出HyperST，一个ST预测框架，通过在双曲空间（此类结构的自然几何设置）中建模数据固有的层次结构来学习多级图像-基因表征。首先，我们设计多级表征提取器，从每种模态中捕获斑点级和生态位级表征，提供超越单个斑点级图像-基因对的上下文感知信息。其次，引入层次化双曲对齐模块来统一这些表征，在执行空间对齐的同时层次化地构建图像和基因嵌入。这种对齐策略用分子语义丰富图像表征，显著改善跨模态预测。HyperST在来自不同组织的四个公开数据集上达到最优性能，为可扩展且准确的空间转录组学预测铺平了道路。
英文原文摘要 1724 字符
Spatial Transcriptomics (ST) merges the benefits of pathology images and gene expression, linking molecular profiles with tissue structure to analyze spot-level function comprehensively. Predicting gene expression from histology images is a cost-effective alternative to expensive ST technologies. However, existing methods mainly focus on spot-level image-to-gene matching but fail to leverage the full hierarchical structure of ST data, especially on the gene expression side, leading to incomplete image-gene alignment. Moreover, a challenge arises from the inherent information asymmetry: gene expression profiles contain more molecular details that may lack salient visual correlates in histological images, demanding a sophisticated representation learning approach to bridge this modality gap. We propose HyperST, a framework for ST prediction that learns multi-level image-gene representations by modeling the data's inherent hierarchy within hyperbolic space, a natural geometric setting for such structures. First, we design Multi-Level Representation Extractors to capture both spot-level and niche-level representations from each modality, providing context-aware information beyond individual spot-level image-gene pairs. Second, a Hierarchical Hyperbolic Alignment module is introduced to unify these representations, performing spatial alignment while hierarchically structuring image and gene embeddings. This alignment strategy enriches the image representations with molecular semantics, significantly improving cross-modal prediction. HyperST achieves state-of-the-art performance on four public datasets from different tissues, paving the way for scalable and accurate spatial transcriptomics prediction.