Deciphering Microenvironmental Heterogeneity by Scalable Niche Guided Module Discovery

Abstract Spatial transcriptomics provides high-dimensional gene expression data while preserving spatial context, offering novel insights into tissue composition and heterogeneity. Each spot or cell in the spatial transcriptome could be reflected as gene modules influenced by its surrounding microenvironment, with module interactions vital for tissue architecture and function. Here, we present Scalable Niche Guided Module Discovery (SIGMOD), a method that integrates prior constructed microenvironment information with gene expression decompositions to uncover gene modules, enabling a deeper understanding of crosstalk within the microenvironment. SIGMOD identifies cell type–specific and cell state–specific, clinically relevant gene modules, uncovering gene module–module interactions in 10X ST, Visium, Xenium, and CosMX data, demonstrating its effectiveness and broad applicability.