SCelVis: Powerful explorative single cell data analysis on the desktop and in the cloud
SCelVis: Powerful explorative single cell data analysis on the desktop and in the cloud
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SCelVis：在桌面和云端进行强大的探索性单细胞数据分析
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
D. Beule
中科院分区：
文献类型：
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作者：
B. Obermayer;M. Holtgrewe;Mikko Nieminen;Clemens Messerschmidt;D. Beule
单细胞组学技术为从实验室到临床的生物医学和生命科学提供了独特的机会，但这些数据的高维性质给计算分析和解释带来了挑战。此外，FAIR数据管理以及数据隐私和安全在处理临床数据时变得至关重要，特别是在跨机构和转化环境中。现有的解决方案要么绑定到一个研究人员的桌面上，要么依赖于特定于供应商的云存储或用户身份验证技术。为了方便没有生物信息学专业知识的用户分析和解释单细胞数据，我们提出了SCelVis，这是一个灵活、交互式和用户友好的应用程序，用于基于web的预处理单细胞数据可视化。用户可以调查其单细胞表达数据和细胞注释的多个交互式可视化，并下载原始或处理过的数据以进行进一步的离线分析。SCelVis既可以在桌面系统上运行，也可以在云系统上运行，使用标准和开放协议接受来自本地和各种远程源的输入，并允许在云和本地托管数据。SCelVis是通过Plotly在Python中使用Dash实现的。它可以作为一个独立的应用程序作为Python包，通过Conda/Bioconda和Docker镜像获得。在宽松的MIT许可下，所有组件都是开源的，并且基于开放标准和接口，可以进一步开发和集成第三方管道和分析组件。GitHub存储库是https://github.com/bihealth/scelvis。
Background Single cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies on vendor-specific technology for cloud storage or user authentication. Results To facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally. Methods SCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis.
影响因子：
48
作者：
Jean Fan;N. Salathia;R. Liu;Gwendolyn E. Kaeser;Y. Yung;Joseph L. Herman;F. Kaper;Jian-Bing Fan
通讯作者：
Jean Fan;N. Salathia;R. Liu;Gwendolyn E. Kaeser;Y. Yung;Joseph L. Herman;F. Kaper;Jian-Bing Fan