Benchmarking spatial clustering methods with spatially resolved transcriptomics data

doi:10.1038/s41592-024-02215-8

CSpace

	Benchmarking spatial clustering methods with spatially resolved transcriptomics data
	Yuan, Zhiyuan 1,2; Zhao, Fangyuan 3,4; Lin, Senlin 3,4; Zhao, Yu 5; Yao, Jianhua 5; Cui, Yan 1,2,6; Zhang, Xiao-Yong 1; Zhao, Yi 3,4
	2024-03-15
发表期刊	NATURE METHODS
ISSN	1548-7091
页码	25
摘要	Spatial clustering, which shares an analogy with single-cell clustering, has expanded the scope of tissue physiology studies from cell-centroid to structure-centroid with spatially resolved transcriptomics (SRT) data. Computational methods have undergone remarkable development in recent years, but a comprehensive benchmark study is still lacking. Here we present a benchmark study of 13 computational methods on 34 SRT data (7 datasets). The performance was evaluated on the basis of accuracy, spatial continuity, marker genes detection, scalability, and robustness. We found existing methods were complementary in terms of their performance and functionality, and we provide guidance for selecting appropriate methods for given scenarios. On testing additional 22 challenging datasets, we identified challenges in identifying noncontinuous spatial domains and limitations of existing methods, highlighting their inadequacies in handling recent large-scale tasks. Furthermore, with 145 simulated data, we examined the robustness of these methods against four different factors, and assessed the impact of pre- and postprocessing approaches. Our study offers a comprehensive evaluation of existing spatial clustering methods with SRT data, paving the way for future advancements in this rapidly evolving field. A benchmark study compares 13 spatial clustering methods on spatial transcriptomics data.
DOI	10.1038/s41592-024-02215-8
收录类别	SCI
语种	英语
资助项目	National Nature Science Foundation of China[62303119] ; Chenguang Program of Shanghai Education Development Foundation[22CGA02] ; Shanghai Municipal Education Commission[22CGA02] ; Shanghai Science and Technology Development Funds[23YF1403000] ; Tencent AI Lab Rhino-Bird Focused Research Program[RBFR2023008] ; Innovation Fund of Institute of Computing and Technology, CAS[E161080] ; Innovation Fund of Institute of Computing and Technology, CAS[E161030] ; Beijing Natural Science Foundation Haidian Origination and Innovation Joint Fund[L222007] ; Shanghai Municipal Science and Technology Major Project[2018SHZDZX01] ; The 111 Project[B18015] ; ZJ Lab ; Shanghai Center for Brain Science and Brain-Inspired Technology
WOS研究方向	Biochemistry & Molecular Biology
WOS类目	Biochemical Research Methods
WOS记录号	WOS:001185508900001
出版者	NATURE PORTFOLIO
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38743
专题	中国科学院计算技术研究所
通讯作者	Yuan, Zhiyuan; Zhao, Yi
作者单位	1.Fudan Univ, Pudong Med Ctr, Shanghai Pudong Hosp, Ctr Med Res & Innovat, Shanghai, Peoples R China 2.Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, MOE Frontiers Ctr Brain Sci, MOE Key Lab Computat Neurosci & Brain Inspired Int, Shanghai, Peoples R China 3.Chinese Acad Sci, Res Ctr Ubiquitous Comp Syst, Inst Comp Technol, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Tencent AI Lab, Shenzhen, Peoples R China 6.Kyoto Univ, Inst Chem Res, Bioinformat Ctr, Kyoto, Japan
推荐引用方式 GB/T 7714	Yuan, Zhiyuan,Zhao, Fangyuan,Lin, Senlin,et al. Benchmarking spatial clustering methods with spatially resolved transcriptomics data[J]. NATURE METHODS,2024:25.
APA	Yuan, Zhiyuan.,Zhao, Fangyuan.,Lin, Senlin.,Zhao, Yu.,Yao, Jianhua.,...&Zhao, Yi.(2024).Benchmarking spatial clustering methods with spatially resolved transcriptomics data.NATURE METHODS,25.
MLA	Yuan, Zhiyuan,et al."Benchmarking spatial clustering methods with spatially resolved transcriptomics data".NATURE METHODS (2024):25.