Zhang Zhiwei, Wang Mengqiu, Zhang Xinxin, Dai Ruoyan, Wang Zhenghui, Lei Lixin, Li Zhenxing, Han Kaitai, Wang Zijun, Shi Chaojing, Guo Qianjin
Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
School of Biomedical Engineering & Imaging Sciences, King's College London, SE1 7EH, United Kingdom.
Comput Methods Programs Biomed. 2025 Nov;271:109032. doi: 10.1016/j.cmpb.2025.109032. Epub 2025 Aug 20.
Spatial multi-omics technologies have revolutionized our understanding of biological systems by enabling simultaneous measurement of multiple molecular features while preserving spatial information. However, the effective integration and analysis of these complex datasets remain challenging. Here we introduce SpaOmicsVAE, a novel computational framework that combines variational autoencoder architecture with a dual graph neural network to address these challenges. The framework uniquely integrates spatial and feature information through an attention-based mechanism, effectively handling data sparsity and noise while preserving crucial spatial relationships. We demonstrate SpaOmicsVAE's superior performance through comprehensive benchmarking against existing methods using both experimental and simulated datasets. Applied to diverse tissue samples including thymus, spleen, hippocampus, and brain tissues, SpaOmicsVAE revealed previously uncharacterized spatial patterns in T cell development, immune cell organization, and epigenetic regulation. Our framework provides a powerful tool for deciphering the spatial organization of complex biological systems, offering new insights into tissue architecture and function.
空间多组学技术通过在保留空间信息的同时能够对多种分子特征进行同步测量，彻底改变了我们对生物系统的理解。然而，对这些复杂数据集进行有效的整合和分析仍然具有挑战性。在此，我们介绍SpaOmicsVAE，这是一种新颖的计算框架，它将变分自编码器架构与双图神经网络相结合，以应对这些挑战。该框架通过基于注意力的机制独特地整合空间和特征信息，在保留关键空间关系的同时有效地处理数据稀疏性和噪声。我们通过使用实验和模拟数据集与现有方法进行全面基准测试，证明了SpaOmicsVAE的卓越性能。应用于包括胸腺、脾脏、海马体和脑组织在内的多种组织样本时，SpaOmicsVAE揭示了T细胞发育、免疫细胞组织和表观遗传调控中以前未被表征的空间模式。我们的框架为解读复杂生物系统的空间组织提供了一个强大的工具，为组织结构和功能提供了新的见解。
