ClusterMap: multi-scale clustering analysis of spatial gene expression

• DOI: 10.1101/2021.02.18.431337
• Publication Date: 2021-02-19T19:21:10Z
• Authors: Yichun He; Xin Tang; Jiahao Huang; Haowen Zhou; Kevin Chen; Albert Liu; Jingyi Ren; Hailing Shi; Zuwan Lin; Qiang Li; Abhishek Aditham; Jian Shu; Jia Liu; Xiao Wang
• Keywords: 0206 medical engineering; 02 engineering and technology

AbstractQuantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues.In situtranscriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis ofin situtranscriptomic data. Here, we present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to variousin situtranscriptomic measurements to uncover gene expression patterns, cell-cell interactions, and tissue organization principles from high-dimensional transcriptomic images.