GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Abstract

Spatial transcriptomics (ST) often requires aligning multiple tissue slices to reconstruct three-dimensional biological structures, a task hindered by complex deformations and structural heterogeneity. We propose GMSA, a synergistic alignment framework that integrates graph matching with point cloud registration. Unlike existing tools, GMSA first identifies high-confidence correspondences through subgraph matching based on gene expression and spatial topology, which provides a robust initialization for subsequent rigid Iterative Closest Point (ICP) or Nonrigid Iterative Closest Point (NICP) registration. Benchmark results on dorsolateral prefrontal cortex (DLPFC), spatially-resolved transcript amplicon readout mapping (STARmap), and multiplexed error-robust fluorescence in situ hybridization (MERFISH) datasets demonstrate that GMSA consistently outperforms state-of-the-art methods in alignment accuracy. Notably, GMSA's nonrigid strategy successfully resolves complex structural distortions in MERFISH data where traditional methods fail, while maintaining stable gene expression distributions across aligned slices. This framework provides a flexible and precise solution for multimodal spatial transcriptomics integration.