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bioRxiv · 10.1101/2025.05.08.652741

POPARI: Modeling multisample variation in spatial transcriptomics

Alam, S.·Zhou, T.·Haber, E.·Chidester, B.·Liu, S.·Chen, F.·Ma, J.
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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 PO_SCPLOWOPARIC_SCPLOW, a probabilistic graphical model for factor-based decomposition of multisample SRT that captures condition-specific changes in spatial organization. PO_SCPLOWOPARIC_SCPLOW 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 PO_SCPLOWOPARIC_SCPLOW 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), PO_SCPLOWOPARIC_SCPLOW 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, PO_SCPLOWOPARIC_SCPLOW provides a general, interpretable framework for analyzing variation in multisample SRT.

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Alam, S., Zhou, T., Haber, E., Chidester, B., Liu, S., Chen, F., Ma, J.. 2025-05-13. POPARI: Modeling multisample variation in spatial transcriptomics. https://doi.org/10.1101/2025.05.08.652741

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