A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies

The rapid advancement of spatially resolved transcriptomics (SRT) technology enables gene expression profiling across tissue locations while preserving spatial context. Gene co-expression analysis in SRT data provides critical insights into how genes function together within the tissue microenvironment. However, existing methods fail to effectively capture the joint influence of gene-gene interactions and spatial dependencies, limiting their biological interpretability. Here, we introduce spMOCA (SPatially informed Matrix-nOrmal model for gene Co-expression Analysis), a statistical framework for inferring gene co-expression networks while explicitly modeling spatial dependencies. By leveraging a matrix-normal model, spMOCA jointly accounts for gene-gene and spatial covariance, disentangling intrinsic co-expression relationships from spatially induced effects. Through extensive simulations, we show that spMOCA provides more accurate and unbiased estimates of gene-gene correlations than existing approaches across a range of spatial dependency levels. In applications to nine SRT datasets spanning diverse technologies, tissues, and species, spMOCA consistently identifies more experimentally validated transcription factor target genes than alternative methods. In tumors, it uncovers gene modules linked to tumorigenesis and immune pathways, revealing prognostic markers. In aging mouse brains, it captures dynamic co-expression changes associated with neurodegeneration. In cross-species analyses, it detects conserved gene modules and cell type-specific pathways in the mouse and human cortex.