使用SCTK2灵活工作流程对单细胞数据进行交互式分析。

Suppr 超能文献

核心技术专利：CN118964589B侵权必究

Suppr 超能文献

核心技术专利：CN118964589B侵权必究

Wang Yichen, Sarfraz Irzam, Pervaiz Nida, Hong Rui, Koga Yusuke, Akavoor Vidya, Cao Xinyun, Alabdullatif Salam, Zaib Syed Ali, Wang Zhe, Jansen Frederick, Yajima Masanao, Johnson W Evan, Campbell Joshua D

Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.

Bioinformatics Program, Boston University, Boston, MA, USA.

Patterns (N Y). 2023 Aug 3;4(8):100814. doi: 10.1016/j.patter.2023.100814. eCollection 2023 Aug 11.

Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single-Cell Toolkit 2 (SCTK2), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users.

单细胞RNA测序（scRNA-seq）数据的分析能够揭示复杂生物系统异质性的全新见解。人们已经开发了许多工具和工作流程来进行不同类型的分析。然而，这些工具分散在不同的软件包或编程环境中，依赖不同的底层数据结构，并且只有具备编程语言知识的人才能使用。在单细胞工具包2（SCTK2）中，我们整合了各种流行的工具和工作流程，以执行scRNA-seq分析的各个方面。所有工具和工作流程都可以在R控制台中运行，或使用基于R/Shiny构建的直观图形用户界面运行。使用Rmarkdown生成的HTML报告可用于记录和概括各个步骤或整个分析工作流程。我们表明，与现有工具相比，该工具包具有更多功能，并且允许非计算专业用户无缝分析scRNA-seq数据。