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

• e-learning

  A short introduction to Galaxy

  

  • beginner

  Introduction to Galaxy Analyses español
• e-learning

  Name tags for following complex histories

  

  • beginner

  Using Galaxy and Managing your Data

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.

• slides

  An introduction to scRNA-seq data analysis

  

  •• intermediate

  Transcriptomics Single Cell

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

• e-learning

  Pre-processing of 10X Single-Cell RNA Datasets

  

  • beginner

  Transcriptomics 10x Single Cell
• e-learning

  Clustering 3K PBMCs with Scanpy

  

  • beginner

  Transcriptomics 10x Single Cell