Dissemin is shutting down on January 1st, 2025

Published in

Oxford University Press (OUP), GigaScience, 12(8), 2019

DOI: 10.1093/gigascience/giz144

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A Galaxy-based training resource for single-cell RNA-sequencing quality control and analyses

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Postprint: archiving allowed
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Data provided by SHERPA/RoMEO

Abstract

Abstract 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.