Single-cell RNA-Seq analysis made easy

Researchers at EMBL’s European Bioinformatics Institute (EMBL-EBI), Wellcome Sanger Institute, University of Freiburg and colleagues have created a set of user-friendly tools built on the Galaxy platform to make single-cell RNA sequencing (scRNA-seq) analysis accessible to all researchers.

scRNA-seq has become a widespread technique within the life sciences but analysis of the data has previously required an in-depth understanding of complex bioinformatics techniques. With such a huge increase in the number of scRNA-seq datasets generated, the scientific community benefits greatly from easy-to-use analysis tools.

The new setup, called Single Cell interactive application (SCiAp), is a computational interface that provides easy access to data from the Human Cell Atlas (HCA) and EMBL-EBI’s Single Cell Expression Atlas (SCEA) made possible through a dedicated interface in the European Galaxy Server, a de.NBI service. This new, free-to-use interface makes analysis of large-scale scRNA-seq projects simple for the scientific community.

Fig. 1

(1) Load matrix data from HCA or SCEA directly into SCiAp Galaxy. (2) Run configurable scRNA-seq analysis through SCiAp. (3) Inspect results interactively through UCSC-CellBrowser and plots within Galaxy.

Flexible cloud computing

Basing SCiAp on Galaxy means that it benefits from Galaxy’s user-friendly framework for building and sharing workflows. By receiving constant support from a community of bioinformaticians, Galaxy is up-to-date and equipped with the latest analysis methods. Using Galaxy also means that users can couple SCiAp with multiple cloud providers through Kubernetes – an open-source platform for automating compute jobs –  to make large-scale scRNA-seq analysis more accessible for everyone.

“SCiAp is geared towards researchers that are generating their own data and are unable to run their own bioinformatics tools,” says Pablo Moreno, Expression Atlas Technical Coordinator at EMBL-EBI. “It’s single-cell analysis made easy but without compromising on scalability. By making it compatible with cloud computing it’s possible to use SCiAp for very large jobs, anywhere, at any time.”

“We have used SCiAp ourselves to analyse huge datasets for the Single Cell Expression Atlas but it can also be used to analyse individual datasets that researchers generate themselves,” says Irene Papatheodorou, Team Leader at EMBL-EBI. “No job is too big or too small for SCiAp. Even if you are working with large amounts of single-cell data you can use this within your own hardware. All of the components are relocatable in this way so it can be used anywhere.”

Get help with your scRNA-seq analysis

To make sure anyone can use Galaxy to analyse scRNA-seq data, the EMBL-EBI Training Team have put together online training materials to guide researchers through everything they need to know. This includes the basics of using a Galaxy interface for scRNA-seq to more in-depth Galaxy tutorials like ‘Generating a cell matrix using Alevin’, which sit alongside the Galaxy Training Network’s suite of training tutorials and presentations.

“We’ve worked closely with the researchers behind SCiAp to put together training materials to make single-cell analysis accessible for anyone,” says Wendi Bacon, Scientific Training Officer at EMBL-EBI. “Working together with the Galaxy Training Network teams in Freiburg, we are in the process of building a suite of tutorials which will be found in Galaxy Training.”

“The approachability of the tools, workflows and training materials enable users to understand all the ins-and-outs of a single-cell analysis,” says Mehmet Tekman, Single-Cell Tools Developer and Specialist at the Freiburg Galaxy Team. “From the expert in-house knowledge of the specialists, to the background computational methods of the statisticians – all presented in an extremely digestible manner.”


Moreno P, Huang N, Manning JR, Mohammed S, Solovyev A, Polanski K, Bacon W, Chazarra R, Talavera-López C, Doyle MA, Marnier G, Grüning B, Rasche H, George N, Fexova SK, Alibi M, Miao Z, Perez-Riverol Y, Haeussler M, Brazma A, Teichmann S, Meyer KB, Papatheodorou I. (2021) User-friendly, scalable tools and workflows for single-cell RNA-seq analysis. Nat Methods 18(4):327-328. [article]

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