Introduction to Galaxy and Single Cell RNA Sequence analysispurlPURL: https://gxy.io/GTN:P00007
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.
|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.
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.
|Pre-processing of 10X Single-Cell RNA Datasets
|Clustering 3K PBMCs with Scanpy
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!
This material is reviewed by our Editorial Board:Wendi Bacon Pavankumar Videm