Single Cell

Training material and practicals for all kinds of single cell analysis (particularly scRNA-seq!). When you generate your lovely gene lists for your cells, consider checking out our Transcriptomics tutorials for further network analysis!

What else do you want to see? You can submit tool and/or tutorial request on our Single Cell Community Tool Request Spreadsheet.

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

Requirements

Before diving into this topic, we recommend you to have a look at:

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Material

Introduction

Start here if you are new to single cell analysis in Galaxy 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
Automated Cell Annotation
Trajectory analysis

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 (Scanpy)
Filter, plot, and explore single cell RNA-seq data (Seurat)
Inferring single cell trajectories (Scanpy)
10x
Inferring single cell trajectories (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 (Scanpy, Python)
Filter, plot, and explore single cell RNA-seq data (Seurat, R)
Inferring single cell trajectories (Scanpy, Python)
Inferring single cell trajectories (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

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

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?

Check out workflows shared by users like you!

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 Community of Practice!

Galaxy instances

You can use a public Galaxy instance which has been tested for the availability of the used tools. They are listed along with the tutorials above.

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:

orcid logoWendi Bacon avatar Wendi BaconMehmet Tekman avatar Mehmet Tekmanorcid logoPavankumar Videm avatar Pavankumar Videm

For any question related to this topic and the content, you can contact them or visit our Gitter channel.

Contributors

This material was contributed to by:

orcid logoDaniel Blankenberg avatar Daniel Blankenbergorcid logoCristóbal Gallardo avatar Cristóbal GallardoJulia Jakiela avatar Julia Jakielaorcid logoHelena Rasche avatar Helena Rascheorcid logoAlex Ostrovsky avatar Alex Ostrovskyorcid logoHans-Rudolf Hotz avatar Hans-Rudolf Hotzorcid logoMarisa Loach avatar Marisa Loachorcid logoPavankumar Videm avatar Pavankumar Videmorcid logoBérénice Batut avatar Bérénice Batutorcid logoAnika Erxleben avatar Anika ErxlebenJonathan Manning avatar Jonathan Manningorcid logoWolfgang Maier avatar Wolfgang Maierorcid logoGraeme Tyson avatar Graeme Tysonorcid logoNicola Soranzo avatar Nicola SoranzoPablo Moreno avatar Pablo Morenoorcid logoSaskia Hiltemann avatar Saskia Hiltemannorcid logoBeatriz Serrano-Solano avatar Beatriz Serrano-Solanoorcid logoMorgan Howells avatar Morgan HowellsCamila Goclowski avatar Camila Goclowskiorcid logoGraham Etherington avatar Graham Etheringtonorcid logoWendi Bacon avatar Wendi BaconMehmet Tekman avatar Mehmet Tekman

Funders

This material was funded by:

EOSC-Life avatar EOSC-LifeELIXIR-UK: FAIR Data Stewardship training avatar ELIXIR Fair DataEPSRC Training Grant DTP 2020-2021 Open University avatar EPSRC/OU

References