GatorST: A Versatile Contrastive Meta-Learning Framework for Spatial Transcriptomic Data Analysis

Recent advances in spatial transcriptomics (ST) have revolutionized the understanding of cellular functions by providing gene expression profiles with rich spatial context. Effectively learning spatial representations is essential for downstream analyses and requires robust integration of spatial and transcriptomic information. Although existing methods show promise, they often fail to capture local (neighbor-level) and global (tissue-wide) contexts, and methods based on contrastive learning often rely on augmentation strategies that introduce noise and instability. GatorST, a novel and versatile framework, explicitly integrates graph-based modeling with advanced meta-learning strategies to generate spatially informed representations of ST data. Locally, a spot-spot graph connects each node to its nearest neighbors, while two-hop subgraphs capture fine-grained spatial context. Globally, gene expression profiles are clustered to produce pseudo-labels, providing weak supervision for representation learning. An episodic training strategy inspired by meta-learning further enhances GatorST's ability to generalize to new spatial contexts, ensuring robust integration of local and global spatial information. Comprehensive comparisons with fifteen state-of-the-art methods demonstrate that GatorST consistently outperforms existing approaches in identifying spatial domains, imputing gene expression, removing batch effects, and inferring spatial trajectories. By integrating local spatial topology with global gene expression patterns, GatorST provides biologically meaningful representations that advance key downstream analyses.