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), researchers from the Boston University School of Medicine 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. The researchers 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.
Overview of curated analysis workflows
Availability – All original source code has been deposited on GitHub (https://github.com/compbiomed/singleCellTK) and is publicly available as of the date of publication. Meanwhile, the package is available on Bioconductor as singleCellTK (https://bioconductor.org/packages/singleCellTK/) and can be installed in R with Bioconductor. A live application is deployed at https://sctk.bu.edu/ for installation free access.
Wang Y, Sarfraz I, Pervaiz N, Hong R, Koga Y, Akavoor V, Cao X, Alabdullatif S, Zaib SA, Wang Z, Jansen F, Yajima M, Johnson WE, Campbell JD. (2023) Interactive analysis of single-cell data using flexible workflows with SCTK2. Patterns (N Y) 4(8):100814. [article]
