融合图扩散与空间注意力，提升空间转录组数据的完整性和生物可解释性。
SpatialMAGIC: A Hybrid Framework Integrating Graph Diffusion and Spatial Attention for Spatial Transcriptomics Imputation
- 结合图扩散捕捉长程依赖，空间注意力建模局部结构。
- 在多个数据集上达到最高聚类准确率，如Stereo-Seq达0.3301 ARI。
- 适合研究组织结构和基因调控的生物学家，提升下游分析可靠性。
空间转录组技术能以空间上下文映射基因表达，但受限于高稀疏性和技术噪声，掩盖了真实生物学信号并阻碍下游分析。为应对这一挑战，提出SpatialMagic，一种融合MAGIC图扩散与Transformer空间自注意力的混合重构模型。图扩散捕捉基因表达中的长程依赖，空间注意力模型保留局部邻域结构，从而恢复缺失表达值并维持空间一致性。在多个平台中，SpatialMagic持续优于现有基线，包括MAGIC和注意力模型，在高分辨率Stereo-Seq数据上达到0.3301的最高调整兰德指数（ARI），Slide-Seq为0.3074，Sci-Space为0.4216。除了定量提升，SpatialMagic显著增强下游分析能力，改善上调与下调基因的检测，同时保持跨数据集的调控一致性。恢复基因的通路富集分析显示其参与关键代谢、转运和神经信号通路，表明该框架在提升数据质量的同时保持生物学可解释性。总体而言，SpatialMagic的混合扩散-注意力策略与精炼模块在量化指标上超越当前最优方法，并通过保留组织架构揭示生物学相关基因，提供更可信的重构数据。
原文摘要 · Abstract (English)
Spatial transcriptomics (ST) enables mapping gene expression with spatial context but is severely affected by high sparsity and technical noise, which conceals true biological signals and hinders downstream analyses. To address these challenges, SpatialMagic was proposed, which is a hybrid imputation model combining MAGIC-based graph diffusion with transformer-based spatial self-attention. The long-range dependencies in the gene expression are captured by graph diffusion, and local neighborhood structure is captured by spatial attention models, which allow for recovering the missing expression values, retaining spatial consistency. Across multiple platforms, SpatialMagic consistently outperforms existing baselines, including MAGIC and attention-based models, achieving peak Adjusted Rand Index (ARI) scores in clustering accuracy of 0.3301 on high-resolution Stereo-Seq data, 0.3074 on Slide-Seq, and 0.4216 on the Sci-Space dataset. Beyond quantitative improvements, SpatialMagic substantially enhances downstream biological analyses by improving the detection of both up- and down-regulated genes while maintaining regulatory consistency across datasets. The pathway enrichment analysis of the recovered genes indicates that they are involved in consistent processes across key metabolic, transport, and neural signaling pathways, suggesting that the framework improves data quality while preserving biological interpretability. Overall, SpatialMagic's hybrid diffusion attention strategy and refinement module outperform state-of-the-art baselines on quantitative metrics and provide a better understanding of the imputed data by preserving tissue architecture and uncovering biologically relevant genes. The source code and datasets are provided in the following link: https://github.com/sayeemzzaman/SpatialMAGIC
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。