ClusterMap for multi-scale clustering analysis of spatial gene expression
Name
s41467-021-26044-x.pdf
Description
Published version
Size
17.25 MB
Format
Unknown
Checksum (MD5)
a8b579f518903d893aa4f91867853156
Author(s)
He, Yichun
Tang, Xin
Huang, Jiahao
Ren, Jingyi
Zhou, Haowen
Chen, Kevin
Liu, Albert
Shi, Hailing
Lin, Zuwan
Li, Qiang
Date Issued
October 2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
He, Yichun, Tang, Xin, Huang, Jiahao, Ren, Jingyi, Zhou, Haowen et al. 2021. "ClusterMap for multi-scale clustering analysis of spatial gene expression." Nature Communications, 12 (1).
Version
Final published version
Abstract
AbstractQuantifying 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.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Department of Biological Engineering
Whitehead Institute for Biomedical Research
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41467-021-26044-x
