Single Cell

Training material and practicals for all kinds of single cell analysis (particularly scRNA-seq!).

Material

Jump to a section!

• Introduction
• Your first analysis
• Case study
• Case study: Reloaded
• End-to-end scRNA-seq Analyses
• Deconvolution
• Multiomic Analyses
• Tips, tricks & other hints
• Changing data formats & preparing objects
• Exploratory Analyses

Introduction

Start here if you are new to single cell analysis and want to learn the concepts.

Your first analysis

Start here if you are new to single cell analysis in Galaxy and want to try analysing data.

Case study

These tutorials take you from raw scRNA sequencing reads to inferred trajectories to replicate a published analysis. The data is messy. The decisions are tough. The interpretation is meaningful. Come here to advance your single cell skills! Note that you get two options for inferring trajectories.

Case study: Reloaded

These tutorials let you follow the same case study analysis of real, messy data but in a programming environment, hosted on Galaxy. So if you want more flexibility, but the same guided steps as the Case Study, you can skip the Case Study and start here instead. Alternatively, try these after completing the Case Study for an easier jump to a coding environment.

End-to-end scRNA-seq Analyses

These tutorials use different methods to analyse scRNA-seq samples

Deconvolution

These tutorials infer cell compositions from bulk RNA-seq data using a scRNA-seq reference

Multiomic Analyses

This section lets you build on mere scRNA analyses into a multiomic future!

Tips, tricks & other hints

These tutorials cover helpful skills for scRNA-seq analysis

Changing data formats & preparing objects

These tutorials cover a range of needs for importing data from different sources, to changing data into different formats to move from one analysis to the other.

Exploratory Analyses

What do you do with your list of genes? Come here to explore your results more!

Tip: Troubleshooting errors

When something goes wrong in Galaxy, there are a number of things you can do to find out what it was. Error messages can help you figure out whether it was a problem with one of the settings of the tool, or with the input data, or maybe there is a bug in the tool itself and the problem should be reported. Below are the steps you can follow to troubleshoot your Galaxy errors.

1. Expand the red history dataset by clicking on it.
   • Sometimes you can already see an error message here

2. View the error message by clicking on the bug icon galaxy-bug

3. Check the logs. Output (stdout) and error logs (stderr) of the tool are available:
   • Expand the history item
   • Click on the details icon
   • Scroll down to the Job Information section to view the 2 logs:
     • Tool Standard Output
     • Tool Standard Error
   • For more information about specific tool errors, please see the Troubleshooting section
4. Submit a bug report! If you are still unsure what the problem is.
   • Click on the bug icon galaxy-bug
   • Write down any information you think might help solve the problem
     • See this FAQ on how to write good bug reports
   • Click galaxy-bug Report button
5. Ask for help!
   • Where?
     • In the User community chatspace in Slack in our #single-cell-users channel
     • In the GTN Matrix Channel
     • In the Galaxy Matrix Channel
     • Browse the Galaxy Help Forum to see if others have encountered the same problem before (or post your question).
   • When asking for help, it is useful to share a link to your history

Want to explore analysis beyond our tutorials?

Want to contribute?

If you want to help us behind the scenes, from testing workflows and tutorials to building tools, join our Galaxy Single-cell & sPatial Omics Community of Practice!

• point-right Community of Practice
• feedback Matrix Chat Forum
• email Mailing List

Frequently Asked Questions

Common questions regarding this topic have been collected on a dedicated FAQ page . Common questions related to specific tutorials can be accessed from the tutorials themselves.

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Editorial Board

This material is reviewed by our Editorial Board:

Wendi Bacon

Mehmet Tekman

Pavankumar Videm

Morgan Howells

Marisa Loach

Florian Heyl

Contributors

This material was contributed to by:

Nicola Soranzo

Wolfgang Maier

Cristóbal Gallardo

Anika Erxleben

Anthony Bretaudeau

Simon Bray

Martin Čech

David López

Teresa Müller

Beatriz Serrano-Solano

Timon Schlegel

Alex Ostrovsky

Björn Grüning

Daniel Blankenberg

Marisa Loach

Helena Rasche

Saskia Hiltemann

Julia Jakiela

Pavankumar Videm

Camila Goclowski

Hans-Rudolf Hotz

Graeme Tyson

Menna Gamal

Matthias Bernt

Stéphanie Robin

Morgan Howells

Graham Etherington

Mehmet Tekman

Wendi Bacon

Pablo Moreno

Katarzyna Kamieniecka

Jonathan Manning

Diana Chiang Jurado

Bérénice Batut

Funding

These individuals or organisations provided funding support for the development of this resource

EOSC-Life

EOSC-Life has received funding from the European Union’s Horizon 2020 programme under grant agreement number 824087

EPSRC/OU

The research internship was supported through EPSRC Training Grant DTP 2020-2021 Open University

ELIXIR Fair Data

This work has been funded by the ELIXIR-UK FAIR Data Stewardship training UKRI award (MR/V038966/1)

References

• Tekman, Mehmet and Batut, Bérénice; Ostrovsky, Alexander; Antoniewski, Christophe; Clements, Dave; Ramirez, Fidel; Etherington, Graham J; Hotz, Hans-Rudolf; Scholtalbers, Jelle; Manning, Jonathan R; Bellenger, Lea; Doyle, Maria A; Heydarian, Mohammad; Huang, Ni; Soranzo, Nicola; Moreno, Pablo; Mautner, Stefan; Papatheodorou, Irene; Nekrutenko, Anton; Taylor, James; Blankenberg, Daniel; Backofen, Rolf; Grüning, Björn;: A single-cell RNA-sequencing training and analysis suite using the Galaxy framework
• Pablo Moreno, Ni Huang, Jonathan R Manning, Suhaib Mohammed, Andrey Solovyev, Krzysztof Polanski, Wendi Bacon, Ruben Chazarra, Carlos Talavera-López, Maria A Doyle, Guilhem Marnier, Björn Grüning, Helena Rasche, Nancy George, Silvie Korena Fexova, Mohamed Alibi, Zhichao Miao, Yasset Perez-Riverol, Maximilian Haeussler, Alvis Brazma, Sarah Teichmann, Kerstin B Meyer, Irene Papatheodorou;: User-friendly, scalable tools and workflows for single-cell RNA-seq analysis