POPARI: Modeling multisample variation in spatial transcriptomics

Abstract Integrating spatially-resolved transcriptomics (SRT) across biological samples is essential for understanding dynamic changes in tissue architecture and cell-cell interactions in situ . While tools exist for multisample single-cell RNA-seq, methods tailored to multisample SRT remain limited. Here, we introduce P opari , a probabilistic graphical model for factor-based decomposition of multisample SRT that captures condition-specific changes in spatial organization. P opari jointly learns spatial metagenes – linear gene expression programs – and their spatial affinities across samples. Its key innovations include a differential prior to regularize spatial accordance and spatial downsampling to enable multiresolution, hierarchical analysis. Simulations show P opari outperforms existing methods on multisample and multi-resolution spatial metrics. Applications to real datasets uncover spatial metagene dynamics, spatial accordance, and cell identities. In mouse brain (STARmap PLUS), P opari identifies spatial metagenes linked to AD; in thymus (Slide-TCR-seq), it captures increasing colocalization of V(D)J recombination and T cell proliferation; and in ovarian cancer (CosMx), it reveals sample-specific malignant-immune interactions. Overall, P opari provides a general, interpretable framework for analyzing variation in multisample SRT.