Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

作者：Shuangsang Fang, Mengyang Xu, Lei Cao, Xiaobin Liu, Marija Bezulj, Liwei Tan, Zhiyuan Yuan, Yao Li, 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, Qin Lu, Yumei Li, 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, Yuxiang Li, Xun Xu · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2023 · DOI：10.1101/2023.12.04.569485 · 被引用次数：24 · 研究领域：Single-cell and spatial transcriptomics、Cell Image Analysis Techniques、Gene Regulatory Network Analysis

Abstract Tracing cellular dynamic changes across conditions, time, and space is crucial for understanding the molecular mechanisms underlying complex biological systems. However, integrating multi-sample data in a unified and flexible way to explore cellular heterogeneity remains a major challenge. Here, we present Stereopy, a flexible and versatile framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics with interactive data visualization. To optimize this flexible framework, we have developed three key components: a multi-sample tailored data container, a scope controller, and an analysis transformer. Furthermore, Stereopy showcases three transformative applications supported by pivotal algorithms. Firstly, the multi-sample cell community detection (CCD) algorithm introduces an innovative capability to detect specific cell communities and identify genes responsible for pathological changes in comparable datasets. Secondly, the spatially resolved temporal gene pattern inference (TGPI) algorithm represents a notable advancement in detecting important spatiotemporal gene patterns while concurrently considering spatial and temporal features, which enhances the identification of important genes, domains and regulatory factors closely associated with temporal datasets. Finally, the 3D niche-based regulation inference tool, named NicheReg3D, reconstructs the 3D cell niches to enable the inference of cell-gene interactio...