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 Popari, a probabilistic graphical model for factor-based decomposition of multisample SRT that captures condition-specific changes in spatial organization. Popari 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 Popari 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), Popari 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, Popari provides a general, interpretable framework for analyzing variation in multisample SRT. Competing Interest Statement F.C. is an academic founder of Curio Bioscience and Doppler Biosciences, and scientific advisor for Amber Bio. F.C.s interests were reviewed and managed by the Broad Institute in accordance with their conflict-of-interest policies. All other authors declare no competing interests.