Benchmarking spatial clustering methods with spatially resolved transcriptomics data
文献类型：期刊论文
| 作者 | Yuan, Zhiyuan5,6; Zhao, Fangyuan3,4; Lin, Senlin3,4; Zhao, Yu2; Yao, Jianhua2; Cui, Yan1,5,6; Zhang, Xiao-Yong6; Zhao, Yi3,4 |
| 刊名 | NATURE METHODS
 |
| 出版日期 | 2024-03-15 |
| 页码 | 25 |
| ISSN号 | 1548-7091 |
| DOI | 10.1038/s41592-024-02215-8 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| 出版者 | NATURE PORTFOLIO |
| WOS记录号 | WOS:001185508900001 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/38742]  |
| 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.Kyoto Univ, Inst Chem Res, Bioinformat Ctr, Kyoto, Japan 2.Tencent AI Lab, Shenzhen, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Sci, Res Ctr Ubiquitous Comp Syst, Inst Comp Technol, Beijing, Peoples R China 5.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 6.Fudan Univ, Pudong Med Ctr, Shanghai Pudong Hosp, Ctr Med Res & Innovat, Shanghai, Peoples R China |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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