Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data
Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data
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DOI：
10.1186/s12864-019-6053-y
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发表时间：
2019-08-27
期刊：
影响因子：
4.4
通讯作者：
Yue, Yong G.
中科院分区：
文献类型：
--
作者：
Feng, Di;Whitehurst, Charles E.;Yue, Yong G.
背景细胞转录组测序已成为一种越来越有价值的技术，可以通过散装测序以不可能的分辨率解剖复杂生物学。但是，与当前可用的工具相处所需的高维数据所需的技术专长之间所需的技术专业知识之间的差距以及在其生物环境中解释结果所需的生物学专业知识仍然没有完全解决。ResultSingleCell Explorer是一家基于Python的Web服务器我们开发的应用程序是为了使计算和实验科学家在迭代和协作中介绍用户友好和视觉吸引力的平台内的细胞表达表型。这些注释可以由多个用户进行修改和共享，以便在计算科学家和实验生物学家之间轻松合作。数据处理和分析工作流可以使用Jupyter笔记本集成到系统中。该应用程序可以实现强大而易于访问的功能，例如使用标记基因或差异基因表达模式的用户定义的细胞群体鉴定差异基因表达模式以及细胞类型的方便注释。用户能够生产地块而无需Python或R编码技能。因此，通过使单个细胞RNA-seq数据共享并更加用户友好地查询，该软件可以通过采用单个细胞转录组方法来促进更深入的理解和创新。ConclusionsSingleCell Explorer是一种可自由利用的单细胞转录分析工具，可实现计算和实验生物学家将在灵活的软件环境和集中数据库服务器中进行协作，注释和共享结果支持数据门户功能。
BackgroundSingle cell transcriptome sequencing has become an increasingly valuable technology for dissecting complex biology at a resolution impossible with bulk sequencing. However, the gap between the technical expertise required to effectively work with the resultant high dimensional data and the biological expertise required to interpret the results in their biological context remains incompletely addressed by the currently available tools.ResultsSingle Cell Explorer is a Python-based web server application we developed to enable computational and experimental scientists to iteratively and collaboratively annotate cell expression phenotypes within a user-friendly and visually appealing platform. These annotations can be modified and shared by multiple users to allow easy collaboration between computational scientists and experimental biologists. Data processing and analytic workflows can be integrated into the system using Jupyter notebooks. The application enables powerful yet accessible features such as the identification of differential gene expression patterns for user-defined cell populations and convenient annotation of cell types using marker genes or differential gene expression patterns. Users are able to produce plots without needing Python or R coding skills. As such, by making single cell RNA-seq data sharing and querying more user-friendly, the software promotes deeper understanding and innovation by research teams applying single cell transcriptomic approaches.ConclusionsSingle cell explorer is a freely-available single cell transcriptomic analysis tool that enables computational and experimental biologists to collaboratively explore, annotate, and share results in a flexible software environment and a centralized database server that supports data portal functionality.