ClusterMap for multi-scale clustering analysis of spatial gene expression

Yichun He, Xin Tang, Jiahao Huang, Jingyi Ren, Haowen Zhou, Kevin Chen, Albert Liu, Hailing Shi, Zuwan Lin, Qiang Li, Abhishek Aditham, Johain Ounadjela, Emanuelle I. Grody, Jian Shu, Jia Liu () and Xiao Wang ()
Additional contact information
Yichun He: Harvard University
Xin Tang: Harvard University
Jiahao Huang: Broad Institute of MIT and Harvard
Jingyi Ren: Broad Institute of MIT and Harvard
Haowen Zhou: Broad Institute of MIT and Harvard
Kevin Chen: Harvard University
Albert Liu: Broad Institute of MIT and Harvard
Hailing Shi: Broad Institute of MIT and Harvard
Zuwan Lin: Broad Institute of MIT and Harvard
Qiang Li: Harvard University
Abhishek Aditham: Broad Institute of MIT and Harvard
Johain Ounadjela: Broad Institute of MIT and Harvard
Emanuelle I. Grody: Broad Institute of MIT and Harvard
Jian Shu: Broad Institute of MIT and Harvard
Jia Liu: Harvard University
Xiao Wang: Broad Institute of MIT and Harvard

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we introduce an unsupervised and annotation-free framework, termed ClusterMap, which incorporates the physical location and gene identity of RNAs, formulates the task as a point pattern analysis problem, and identifies biologically meaningful structures by density peak clustering (DPC). Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and performs consistently on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell niche, and tissue organization principles from images with high-dimensional transcriptomic profiles.

Date: 2021
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DOI: 10.1038/s41467-021-26044-x

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