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

ABSTRACT: 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, disentanglin

SUBMITTER: Tan C 

PROVIDER: S-EPMC12693644 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature