Introduction to Galaxy and Single Cell RNA Sequence analysis
purlPURL: https://gxy.io/GTN:P00007Comment: What is a Learning Pathway?
We recommend you follow the tutorials in the order presented on this page. They have been selected to fit together and build up your knowledge step by step. If a lesson has both slides and a tutorial, we recommend you start with the slides, then proceed with the tutorial.
This learning path aims to teach you the basics of Galaxy and analysis of Single Cell RNA-seq data. You will learn how to use Galaxy for analysis, and an important Galaxy feature for iterative single cell analysis. You’ll tbe guided through the general theory of single analysis and then perform a basic analysis of 10X chromium data. For support throughout these tutorials, join our Galaxy single cell chat group on Matrix to ask questions!
New to Galaxy and/or the field of scRNA-seq? Follow this learning path to get familiar with the basics!
Module 1: Introduction to Galaxy
Get a first look at the Galaxy platform for data analysis. We start with a short introduction (video slides & practical) to familiarize you with the Galaxy interface, and then proceed with a short tutorial of how to tag - and organise! - your history.
Time estimation: 1 hour
Learning Objectives
- Learn how to upload a file
- Learn how to use a tool
- Learn how to view results
- Learn how to view histories
- Learn how to extract and run a workflow
- Learn how to share a history
- Learn how to set name tags
- Learn how they are propagated
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| A short introduction to Galaxy | |||
|
Name tags for following complex histories
|
Module 2: Theory of Single-Cell RNA-seq
When analysing sequencing data, you should always start with a quality control step to clean your data and make sure your data is good enough to answer your research question. After this step, you will often proceed with a mapping (alignment) or genome assembly step, depending on whether you have a reference genome to work with.
Time estimation: 30 minutes
Learning Objectives
- To understand the pitfalls in scRNA-seq sequencing and amplification, and how they are overcome.
- Know the types of variation in an analysis and how to control for them.
- Grasp what dimension reduction is, and how it might be performed.
- Be familiarised with the main types of clustering techniques and when to use them.
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
|
An introduction to scRNA-seq data analysis
|
Module 3: Time to analyse data!
It’s time to apply your skills! You’ll now analyse some clean data from the 10X Chromium platform.
Time estimation: 9 hours
Learning Objectives
- Demultiplex single-cell FASTQ data from 10X Genomics
- Learn about transparent matrix formats
- Understand the importance of high and low quality cells
- Describe an AnnData object to store single-cell data
- Explain the preprocessing steps for single-cell data
- Evaluate quality of single-cell data and apply steps to select and filter cells and genes based on QC
- Execute data normalization and scaling
- Identify highly variable genes
- Construct and run a dimensionality reduction using Principal Component Analysis
- Perform a graph-based clustering for cells
- Identify marker genes for the clusters
- Construct and run a cell type annotation for the clusters
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Pre-processing of 10X Single-Cell RNA Datasets | |||
| Clustering 3K PBMCs with Scanpy |
The End!
And now you’re done! There are still loads of resources to take you from basic analysis to more difficult decision-making, deconvolution, multiomics, or ingesting from different data sources. See the Galaxy Single Cell Training page for more!
Editorial Board
This material is reviewed by our Editorial Board:
Wendi Bacon
Pavankumar Videm