PathCLAST: pathway-augmented contrastive learning with attention for interpretable spatial transcriptomics

Abstract

Deciphering how molecular programs are spatially organized within tissues is pivotal for understanding tumor evolution and microenvironmental interactions. Existing spatial transcriptomics tools either rely on gene-level features, ignoring the rich topology of biological pathways, or deliver black-box clusters with little mechanistic insight; thus, they limit their translational impact. A method that simultaneously leverages pathway structures and spatially matched histopathology could produce domain delineations that are both accurate and biologically interpretable. We introduce PathCLAST (Pathway-augmented Contrastive Learning with Attention for interpretable Spatial Transcriptomics), which is a framework that integrates gene expression, histopathological images, and curated pathway graphs via bi-modal contrastive learning. By embedding expression profiles into biologically structured graphs, and aligning them with local image features, PathCLAST achieves state-of-the-art spatial domain identification on multiple public datasets, while offering pathway-level attention scores for mechanistic interpretation. The pathway embedding also serves as an explicit, biology-informed dimensionality reduction scheme. PathCLAST not only uncovers domain-specific pathways and spatially organized signaling activities, but also quantifies intra-domain heterogeneity, spatial autocorrelation, and inter-domain crosstalk, providing fine-grained insights into tumor progression and tissue architecture. PathCLAST is available at https://github.com/sslim-aidrug/PathCLAST.