SpatialMAGIC: A Hybrid Framework Integrating Graph Diffusion and Spatial Attention for Spatial Transcriptomics Imputation

TL;DR

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.

Abstract

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

Figures (6)

• Figure 1: Overview of the SpatialMAGIC framework. The input gene expression matrix undergoes MAGIC-based diffusion to recover local gene patterns. Simultaneously, spatial coordinates are embedded using a transformer encoder to capture spatial relationships. The outputs from both branches are fused and passed through a neural decoder to reconstruct an enhanced expression profile for downstream spatial clustering and analysis.
• Figure 2: Clustering results of the Stereo-seq dataset before and after imputations.
• Figure 3: ARI Comparison Across Imputation Methods (Stereo-seq).
• Figure 4: ARI Comparison Across Imputation Methods (Slide-seq).
• Figure 5: ARI comparison across imputation methods (Sci-space).