A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics

Multi-modal Graph Integration for Biologically Interpretable Domain Identification Using Spatial Transcriptomics

Functional domain identification in spatial transciptomics transforms spatial molecular measurements into mechanistic insights into tissue physiology and pathology. However, the inherent noise and sparsity of gene expression data, along with the locality-biased design of conventional graph-based approaches, fundamentally limit the accurate identification of complex tissue domains. In this study, we propose a novel Biologically Interpretable multi-modal Graph using Spatial Transcriptomics, called BIGraph-ST, that integrates pathway activity scores and histological image features for robust spatial domain identification. BIGraph-ST represents modality-specific similarity through affinity graphs and propagates spatial topology to capture higher-order connectivity within the tissue microenvironment. Experimental results demonstrated robust performance and notable improvements across multiple gold-standard benchmark datasets, particularly in cancer tissues. Moreover, BIGraph-ST provides biologically interpretable pathway-level representations of domains, which ultimately offers a valuable tool to gain biological in-sights into complex tissue architectures. The source code will be publicly available upon acceptance.

Spot-Adaptive Structural Rectification for Spatially Resolved Transcriptomics Data Clustering

Deciphering tissue architecture with StKAN: A multi-modal deep learning framework combining morphology and spatial transcriptomics.

StKAN is introduced, a novel framework integrating Kolmogorov-Arnold Network with variational autoencoder to effectively model spatially resolved gene expression with graph attention network and shows strong potential for downstream analyses, offering deeper insights into disease pathology and tumor invasion.

ENGGT: An Edge-Node Guided Graph Transformer for Spatial Domain Parsing with Accurate Boundaries

Spatial transcriptomics (ST) enables the simultaneous measurement of high-throughput gene expression and spatial structural information, offering a powerful means to decipher tissue heterogeneity. However, current spatial domain identification methods struggle to accurately distinguish continuous biological boundaries, such as smooth tissue transitions or invasive tumor margins. To overcome this challenge, we propose ENGGT, an edge-node guided graph transformer framework that identifies spatial domains with accurate biological boundaries by modeling interactions between node and edge representations. ENGGT employs a dual-branch architecture to capture spatial patterns. Specifically, an edge transformer branch encodes edge features to characterize the spatial and expression relationships of spots pairs within a local tissue microenvironment. It incorporates a topology-aware edge masking strategy to prune unreliable connections and enhance boundary sensitivity. A node transformer branch then integrates the learned edge representations as local attention biases into a global self-attention module, promoting intra-region consistency while limiting error propagation across biological boundaries. Evaluated on ST datasets from multiple measurement platforms, ENGGT consistently outperforms seven state-of-the-art spatial domain identification methods across all evaluation metrics. Moreover, the learned edge-bias matrix offers traceable biological interpretability and enables biological boundary localization. ENGGT provides a robust and interpretable tool for spatial transcriptomics analysis by identifying spatially coherent tissue structures and delineating precise boundaries.

DAHGT-CCI: Dynamic Heterogeneous Graph Transformer for Spatial Transcriptomics Cell-Cell Interaction Inference

It is demonstrated that DAHGT-CCI can more accurately reconstruct cell communication networks in complex tissue microenvironments, offering an indispensable computational tool for studying developmental processes, disease mechanisms, and potential therapeutic targets from a spatially resolved perspective.

Spatial Topology Reveals Biologically Distinct Recurrent Motifs in Colorectal Cancer

Most spatial transcriptomic analyses of solid tumors focus on individual cell states or single-sample spatial domains rather than on recurrent multicellular tissue architectures shared across patients, and typically depend on predefined cell-type annotations or compartment definitions. We developed STORM (Spatial Topology analysis of Recurrent Motifs), an unsupervised graph-attention variational autoencoder that learns recurrent spatial motifs directly from cell-level graph structure and molecular profiles without cell-type labels, manual annotation, or predefined compartments. We applied to 32 Xenium sections from 16 patients with paired early-onset (EOCRC) and average-onset (AOCRC) colorectal cancer, STORM identified 10 recurrent motifs that self-organized into tumor-parenchymal, stromal, and immune macro-compartments. Among these, the Desmoplastic Fibrotic Barrier (DFB) motif, a CAF- and ECM-rich boundary architecture, was associated with restricted CD8+ T-cell geodesic access to tumor cores independently of CD8+ abundance, as demonstrated by abundance-normalized neighborhood enrichment statistics and within-sample mixed-effects models. EOCRC selectively amplified this barrier–exclusion architecture, exhibiting tighter tumor parenchyma, denser DFB shells, and a DFB-specific ECM activation program that yielded an age-specific prognostic signature in TCGA-COAD. Translation of motif macro-classes to H&E images via a Vision Transformer classifier produced an image-derived DFB-barrier composite that predicted overall survival in advanced-stage TCGA colorectal cancer. STORM provides an annotation-free framework for discovering recurrent spatial motifs and identifies a fibroblast barrier architecture whose topological association with immune exclusion is independent of effector abundance, amplified in early-onset disease, and translatable to a deployable pathology-based prognostic biomarker.