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

Graham J. Etherington, Nicola Soranzo, Suhaib Mohammed, Wilfried Haerty, Robert P. Davey, Federica DI Palma

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article numbergiz144
JournalGigaScience
Volume8
Issue number12
DOIs
Publication statusPublished - 1 Dec 2019

Keywords

  • Galaxy
  • scater
  • scRNA-seq
  • single cell
  • training

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