Original title: Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics
Authors: Shuangsang Fang,Mengyang Xu,Lei Cao,Xiaobin Liu,Marija Bezulj,Liwei Tan,Zhiyuan Yuan,Yao Li,Tianyi Xia,Longyu Guo,Vladimir Kovacevic,Junhao Xu,Lidong Guo,Chao Liu,Mengnan Cheng,Li’ang Lin,Zhenbin Wen,Bojana Josic,Nikola Milicevic,Ping Qiu,Qin Lu,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
In this article, the authors introduce a new framework called Stereopy that allows for the analysis of multi-sample spatial transcriptomics data. The goal of this framework is to better understand complex biological systems by tracing cellular changes across different conditions, time points, and locations. The authors have developed three key components to optimize the framework: a tailored data container, a scope controller, and an analysis transformer. They also provide three transformative applications that demonstrate the capabilities of the framework. These applications include a cell community detection algorithm, a gene pattern inference algorithm, and a 3D niche-based regulation inference tool. Overall, Stereopy serves as a bioinformatics toolbox and a flexible framework that can help researchers interpret and analyze multi-sample spatial transcriptomics data more effectively.
Original article: https://www.biorxiv.org/content/10.1101/2023.12.04.569485v1