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.
Before diving into this topic, we recommend you to have a look at:
Start here if you are new to single cell analysis in Galaxy and want to learn the concepts.
An introduction to scRNA-seq data analysis
Plates, Batches, and Barcodes
Automated Cell Annotation
Your first analysis
Start here if you are new to single cell analysis in Galaxy and want to try analysing data.
|Pre-processing of 10X Single-Cell RNA Datasets|
|Clustering 3K PBMCs with Scanpy|
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.
|Generating a single cell matrix using Alevin|
|Combining single cell datasets after pre-processing|
|Filter, plot and explore single-cell RNA-seq data (Scanpy)|
|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.
|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
Pre-processing of Single-Cell RNA Data
Downstream Single-cell RNA analysis with RaceID
|Analysis of plant scRNA-Seq Data with Scanpy|
These tutorials infer cell compositions from bulk RNA-seq data using a scRNA-seq reference
|Bulk RNA Deconvolution with MuSiC|
|Comparing inferred cell compositions using MuSiC deconvolution|
This section lets you build on mere scRNA analyses into a multiomic future!
|Pre-processing of 10X Single-Cell ATAC-seq Datasets|
Tips, tricks & other hints
These tutorials cover helpful skills for scRNA-seq analysis
Single-cell quality control with scater
|Removing the effects of the cell cycle|
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.
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!
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 QuestionsCommon 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.
This material is reviewed by our Editorial Board:Wendi Bacon Mehmet Tekman Pavankumar Videm
For any question related to this topic and the content, you can contact them or visit our Gitter channel.
This material was contributed to by:Daniel Blankenberg Marisa Loach Wolfgang Maier Mehmet Tekman Cristóbal Gallardo Morgan Howells Alex Ostrovsky Pavankumar Videm Anika Erxleben Graham Etherington Helena Rasche Nicola Soranzo Graeme Tyson Beatriz Serrano-Solano Julia Jakiela Camila Goclowski Saskia Hiltemann Hans-Rudolf Hotz Bérénice Batut Wendi Bacon Jonathan Manning
This material was funded by:ELIXIR Fair Data EOSC-Life EPSRC/OU
- 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