Single Cell

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

Pre-requisites: If you’ve never used Galaxy before, first try the:

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Try a Single Cell Learning Pathway! For Beginners For Intermediate Users For Coding Enthusiasts

Material

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You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides (slides) and tutorial (tutorial) buttons below.

Introduction

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

Lesson Slides Hands-on Recordings Input dataset Workflows
An introduction to scRNA-seq data analysis
Understanding Barcodes
Plates, Batches, and Barcodes
Single-cell Formats and Resources
Trajectory analysis
Automated Cell Annotation

Your first analysis

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

Lesson Slides Hands-on Recordings Input dataset Workflows
Pre-processing of 10X Single-Cell RNA Datasets
10x
Clustering 3K PBMCs with Scanpy
10x

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.

Lesson Slides Hands-on Recordings Input dataset Workflows
Generating a single cell matrix using Alevin
Combining single cell datasets after pre-processing
Filter, plot and explore single-cell RNA-seq data with Scanpy
Filter, plot, and explore single cell RNA-seq data with Seurat
Inferring single cell trajectories with Scanpy
Inferring single cell trajectories with Monocle3

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.

Lesson Slides Hands-on Recordings Input dataset Workflows
Generating a single cell matrix using Alevin and combining datasets (bash + R)
Filter, plot and explore single-cell RNA-seq data with Scanpy (Python)
Filter, plot, and explore single cell RNA-seq data with Seurat (R)
Inferring single cell trajectories with Scanpy (Python)
Inferring single cell trajectories with Monocle3 (R)

End-to-end scRNA-seq Analyses

These tutorials use different methods to analyse scRNA-seq samples

Lesson Slides Hands-on Recordings Input dataset Workflows
Pre-processing of Single-Cell RNA Data
Downstream Single-cell RNA analysis with RaceID
Analysis of plant scRNA-Seq Data with Scanpy

Deconvolution

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

Lesson Slides Hands-on Recordings Input dataset Workflows
Bulk RNA Deconvolution with MuSiC
Comparing inferred cell compositions using MuSiC deconvolution

Multiomic Analyses

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

Lesson Slides Hands-on Recordings Input dataset Workflows
Pre-processing of 10X Single-Cell ATAC-seq Datasets
Single-cell ATAC-seq standard processing with SnapATAC2

Tips, tricks & other hints

These tutorials cover helpful skills for scRNA-seq analysis

Lesson Slides Hands-on Recordings Input dataset Workflows
Single-cell quality control with scater
Removing the effects of the cell cycle
10x
Scanpy Parameter Iterator

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.

Lesson Slides Hands-on Recordings Input dataset Workflows
Converting between common single cell data formats
Importing files from public atlases
Converting NCBI Data to the AnnData Format
Matrix Exchange Format to ESet | Creating a single-cell RNA-seq reference dataset for deconvolution
Bulk matrix to ESet | Creating the bulk RNA-seq dataset for deconvolution

Exploratory Analyses

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

Lesson Slides Hands-on Recordings Input dataset Workflows
GO Enrichment Analysis on Single-Cell RNA-Seq Data

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!

Want to explore analysis beyond our tutorials?

Public workflows

News and Events

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!

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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Community Resources

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Editorial Board

This material is reviewed by our Editorial Board:

orcid logoWendi Bacon avatar Wendi BaconMehmet Tekman avatar Mehmet Tekmanorcid logoPavankumar Videm avatar Pavankumar Videmorcid logoMorgan Howells avatar Morgan Howellsorcid logoMarisa Loach avatar Marisa Loachorcid logoFlorian Heyl avatar Florian Heyl

Contributors

This material was contributed to by:

orcid logoPavankumar Videm avatar Pavankumar Videmorcid logoBjörn Grüning avatar Björn Grüningorcid logoTimon Schlegel avatar Timon Schlegelorcid logoAnthony Bretaudeau avatar Anthony BretaudeauPablo Moreno avatar Pablo MorenoTeresa Müller avatar Teresa MüllerSimon Bray avatar Simon Brayorcid logoSaskia Hiltemann avatar Saskia Hiltemannorcid logoDaniel Blankenberg avatar Daniel Blankenbergorcid logoAlex Ostrovsky avatar Alex Ostrovskyorcid logoHans-Rudolf Hotz avatar Hans-Rudolf HotzCamila Goclowski avatar Camila Goclowskiorcid logoWolfgang Maier avatar Wolfgang MaierJonathan Manning avatar Jonathan Manningorcid logoGraham Etherington avatar Graham Etheringtonorcid logoDiana Chiang Jurado avatar Diana Chiang Juradoorcid logoJulia Jakiela avatar Julia Jakielaorcid logoStéphanie Robin avatar Stéphanie Robinorcid logoMorgan Howells avatar Morgan HowellsMenna Gamal avatar Menna Gamalorcid logoWendi Bacon avatar Wendi Baconorcid logoMartin Čech avatar Martin ČechDavid López avatar David Lópezorcid logoKatarzyna Kamieniecka avatar Katarzyna KamienieckaMatthias Bernt avatar Matthias Berntorcid logoCristóbal Gallardo avatar Cristóbal Gallardoorcid logoAnika Erxleben avatar Anika Erxlebenorcid logoNicola Soranzo avatar Nicola Soranzoorcid logoHelena Rasche avatar Helena Rascheorcid logoGraeme Tyson avatar Graeme Tysonorcid logoBeatriz Serrano-Solano avatar Beatriz Serrano-Solanoorcid logoMarisa Loach avatar Marisa LoachMehmet Tekman avatar Mehmet Tekmanorcid logoBérénice Batut avatar Bérénice Batut

Funding

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

References