Dataset Information
A Galaxy-based training resource for single-cell RNA-sequencing quality control and analyses.
Ontology highlight
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.
SUBMITTER: Etherington GJ
PROVIDER: S-EPMC6905351 | biostudies-literature | 2019 Dec
REPOSITORIES: biostudies-literature
Publications
A Galaxy-based training resource for single-cell RNA-sequencing quality control and analyses.
GigaScience 20191201 12
<h4>Background</h4>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.<h4>Results</h4>We have developed a range of practical scripts, together with their corresponding Galaxy wrappers, that make scRNA-seq training and quality control accessible to r ...[more]