Pielawski Nicolas, Andersson Axel, Avenel Christophe, Behanova Andrea, Chelebian Eduard, Klemm Anna, Nysjö Fredrik, Solorzano Leslie, Wählby Carolina
Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Heliyon. 2023 Apr 17;9(5):e15306. doi: 10.1016/j.heliyon.2023.e15306. eCollection 2023 May.
Spatially resolved techniques for exploring the molecular landscape of tissue samples, such as spatial transcriptomics, often result in millions of data points and images too large to view on a regular desktop computer, limiting the possibilities in visual interactive data exploration. TissUUmaps is a free, open-source browser-based tool for GPU-accelerated visualization and interactive exploration of 10+ data points overlaying tissue samples.
Herein we describe how TissUUmaps 3 provides instant multiresolution image viewing and can be customized, shared, and also integrated into Jupyter Notebooks. We introduce new modules where users can visualize markers and regions, explore spatial statistics, perform quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics data.
We show that thanks to targeted optimizations the time and cost associated with interactive data exploration were reduced, enabling TissUUmaps 3 to handle the scale of today's spatial transcriptomics methods.
TissUUmaps 3 provides significantly improved performance for large multiplex datasets as compared to previous versions. We envision TissUUmaps to contribute to broader dissemination and flexible sharing of largescale spatial omics data.
用于探索组织样本分子格局的空间分辨技术,如空间转录组学,常常会产生数百万个数据点以及大到普通台式计算机无法查看的图像,这限制了视觉交互式数据探索的可能性。TissUUmaps是一款基于浏览器的免费开源工具,用于对叠加在组织样本上的10多个数据点进行GPU加速可视化和交互式探索。
在此,我们描述了TissUUmaps 3如何提供即时多分辨率图像查看,以及如何进行定制、共享,还能集成到Jupyter Notebook中。我们介绍了新模块,用户可以在其中可视化标记和区域、探索空间统计、对组织形态进行定量分析,以及评估原位转录组学数据的解码质量。
我们表明,通过有针对性的优化,与交互式数据探索相关的时间和成本得以降低,使TissUUmaps 3能够处理当今空间转录组学方法的规模。
与之前版本相比,TissUUmaps 3在处理大型多重数据集时性能有了显著提升。我们设想TissUUmaps将有助于大规模空间组学数据的更广泛传播和灵活共享。