Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics
Shuangsang Fang, Mengyang Xu, Lei Cao, Xiaobin Liu, Marija Bezulj, Liwei Tan, Zhiyuan Yuan, Yao Li, Tianyi Xia, Longyu Guo, Vladimir Kovačević, Junhou Hui, Lidong Guo, Chao Liu, Mengnan Cheng, Liang Lin, Zhenbin Wen, Bojana Josic, Nikola Milićević, Ping Qiu, Lü Qin, Yumei Li, Leying Wang, Luni Hu, Chao Zhang, Qiang Kang, Fengzhen Chen, Ziqing Deng, Junhua Li, Mei Li, Shengkang Li, Yi Zhao, Guangyi Fan, Yong Zhang, Ao Chen, Yuxiang Li, Xun Xu, Yuxiang Li, Yuxiang Li, Xun Xu
Abstract
Understanding complex biological systems requires tracing cellular dynamic changes across conditions, time, and space. However, integrating multi-sample data in a unified way to explore cellular heterogeneity remains challenging. Here, we present Stereopy, a flexible framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics with interactive data visualization. To optimize this framework, we devise a universal container, a scope controller, and an integrative transformer tailored for multi-sample multimodal data storage, management, and processing. Stereopy showcases three representative applications: investigating specific cell communities and genes responsible for pathological changes, detecting spatiotemporal gene patterns by considering spatial and temporal features, and inferring three-dimensional niche-based cell-gene interaction network that bridges intercellular communications and intracellular regulations. Stereopy serves as both a comprehensive bioinformatics toolbox and an extensible framework that empowers researchers with enhanced data interpretation abilities and new perspectives for mining multi-sample spatial transcriptomics data. Tracing cellular changes in complex biological systems is challenging. Here, authors present a flexible framework that integrates multi-sample data with in-house algorithms to infer comparative and spatiotemporal cell-gene patterns, advancing understanding of cellular dynamics.