Analysis of single-cell RNA-seq (scRNA-seq) data can reveal novel insights into heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analysis. 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 2.0 (SCTK2.0), 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.
Wang, Y., Sarfraz, I., Hong, R., Koga, Y., Akavoor, V., Cao, X., Alabdullatif, S., Pervaiz, N., Zaib, S. A., Wang, Z., Jansen, F., Yajima, M., Johnson, W. E., & Campbell, J. D. (2022). Interactive analysis of single-cell data using flexible workflows with SCTK2.0. BioRxiv. https://doi.org/10.1101/2022.07.13.499900