Alam Shahul, Zhou Tianming, Haber Ellie, Chidester Benjamin, Liu Sophia, Chen Fei, Ma Jian
Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
bioRxiv. 2025 May 13:2025.05.08.652741. doi: 10.1101/2025.05.08.652741.
Integrating spatially-resolved transcriptomics (SRT) across biological samples is essential for understanding dynamic changes in tissue architecture and cell-cell interactions . 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.
整合跨生物样本的空间分辨转录组学（SRT）对于理解组织结构的动态变化和细胞间相互作用至关重要。虽然存在用于多样本单细胞RNA测序的工具，但针对多样本SRT量身定制的方法仍然有限。在这里，我们介绍Popari，这是一种基于因子的多样本SRT分解的概率图形模型，可捕捉空间组织中特定条件下的变化。Popari联合学习空间元基因（线性基因表达程序）及其跨样本的空间亲和力。其关键创新包括用于规范空间一致性的差异先验和用于实现多分辨率分层分析的空间下采样。模拟表明，Popari在多样本和多分辨率空间指标上优于现有方法。在真实数据集上的应用揭示了空间元基因动态、空间一致性和细胞身份。在小鼠大脑（STARmap PLUS）中，Popari识别出与阿尔茨海默病相关的空间元基因；在胸腺（Slide-TCR-seq）中，它捕捉到V(D)J重组和T细胞增殖的共定位增加；在卵巢癌（CosMx）中，它揭示了样本特异性的恶性-免疫相互作用。总体而言，Popari为分析多样本SRT中的变异提供了一个通用的、可解释的框架。
