Background: It is not a trivial step to move from single-cell RNA-sequencing (scRNA-seq) data production to data analysis. There is a lack of intuitive training materials and easy-to-use analysis tools, and researchers can find it difficult to master the basics of scRNA-seq quality control and the later analysis.
Results: We have developed a range of practical scripts, together with their corresponding Galaxy wrappers, that make scRNA-seq training and quality control accessible to researchers previously daunted by the prospect of scRNA-seq analysis. We implement a "visualize-filter-visualize" paradigm through simple command line tools that use the Loom format to exchange data between the tools. The point-and-click nature of Galaxy makes it easy to assess, visualize, and filter scRNA-seq data from short-read sequencing data.
Conclusion: We have developed a suite of scRNA-seq tools that can be used for both training and more in-depth analyses.
Keywords: Galaxy; scRNA-seq; scater; single cell; training.
© The Author(s) 2019. Published by Oxford University Press.