Streamlining spatial omics data analysis with Pysodb

doi:10.1038/s41596-023-00925-5

	Streamlining spatial omics data analysis with Pysodb
	Lin, Senlin 1,2; Zhao, Fangyuan 1,2; Wu, Zihan 3; Yao, Jianhua 3; Zhao, Yi 1,2; Yuan, Zhiyuan 4,5
	2023-12-22
发表期刊	NATURE PROTOCOLS
ISSN	1754-2189
页码	72
摘要	Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website (https://protocols-pysodb.readthedocs.io/) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires similar to 12 h to complete.
DOI	10.1038/s41596-023-00925-5
收录类别	SCI
语种	英语
资助项目	Chenguang Program of Shanghai Education Development Foundation ; Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission, Shanghai Science and Technology Development Funds[23YF1403000] ; Tencent AI Lab Rhino-Bird Focused Research Program[RBFR2023008] ; Shanghai Municipal Science and Technology Major Project[2018SHZDZX01]
WOS研究方向	Biochemistry & Molecular Biology
WOS类目	Biochemical Research Methods
WOS记录号	WOS:001130466600001
出版者	NATURE PORTFOLIO
引用统计	被引频次：4[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/2XEOYT63/38426
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhao, Yi; Yuan, Zhiyuan
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Tencent AI Lab, Shenzhen, Peoples R China 4.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 5.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	Lin, Senlin,Zhao, Fangyuan,Wu, Zihan,et al. Streamlining spatial omics data analysis with Pysodb[J]. NATURE PROTOCOLS,2023:72.
APA	Lin, Senlin,Zhao, Fangyuan,Wu, Zihan,Yao, Jianhua,Zhao, Yi,&Yuan, Zhiyuan.(2023).Streamlining spatial omics data analysis with Pysodb.NATURE PROTOCOLS,72.
MLA	Lin, Senlin,et al."Streamlining spatial omics data analysis with Pysodb".NATURE PROTOCOLS (2023):72.