Scalable topic modelling decodes spatial tissue architecture for large-scale multiplexed imaging analysis.

ABSTRACT: Recent progress in multiplexed tissue imaging is deepening our understanding of tumor microenvironments related to treatment response and disease progression. However, analyzing whole-slide images with millions of cells remains computationally challenging, and few methods provide a principled approach for integrative analysis across images. Here, we introduce SpatialTopic, a spatial topic model designed to decode high-level spatial tissue architecture from multiplexed images. By integrating both cell type and spatial information, SpatialTopic identifies recurrent spatial patterns, or "topics," that reflect biologically meaningful tissue structures. We benchmarked SpatialTopic across diverse single-cell spatial transcriptomic and proteomic imaging platforms spanning multiple tissue types. W

SUBMITTER: Peng X 

PROVIDER: S-EPMC12274411 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature