This course utilises Galaxy pipelines, an online open-access resource that allows even the most computer-phobic bench scientists to analyse their biological data. Participants will be guided through the droplet-based scRNA-seq analysis pipelines from raw reads to cell cluster comparisons using data extracted from the Single Cell Expression Atlas. In addition to running a basic pipeline, participants will explore the variety of options within the Galaxy resource and individually analyse a given dataset. The results will be compared across the cohort to assess reproducibility and demonstrate the effect of analytical choice on research output. Finally, participants will learn about data submission, resources, and standards within the single cell field. 

Please note that participants will not be able to use their own data during the course practicals. However, there will be plenty of time to discuss their research and exchange ideas with other participants and the trainers. Opportunities will include poster sessions, evening discussions and small group chats with the trainers.

Who is this course for?

This course is aimed at researchers who are generating, planning on generating, or working with single cell RNA sequencing data. 

Prerequisites

Participants will be using a Galaxy resource in-depth. Participants may also be asked to do brief coding in R. Please ensure that you complete the free tutorials before you attend the course:

• Introduction to Galaxy: https://galaxyproject.org/tutorials/g101/

• Basic R concept tutorials: www.r-tutor.com/r-introduction

There are other tutorials here, although they are not required: https://galaxyproject.org/learn/

What will I learn?

Learning outcomes

After this course, you should be able to: 

• Explain the steps in the scRNA-seq pipeline
• Repeat the course analysis of scRNA-seq data from extraction to cluster maps on other datasets
• Recognise decision-making steps along the analysis pipeline and justify your choices
• Employ appropriate data standards for repository submission and contribution to global cell atlases
• Define best practice for managing cellular resolution data

Course content

During this course you will learn about:

• Single cell RNA-seq experimental design
• EMBL-EBI Single Cell Expression Atlas Service
• Galaxy scRNA-seq pipelines, including: Seurat, SC3, scanpy, and Scater
• Case study of single cell data
• Human Cell Atlas data & metadata standards
• General principles of data management, data FAIRification and best practice for generating and working with single cell RNA sequencing and image-based transcriptomics data

Trainers

Please read our page on application advice before starting your application. In order to be considered for a place on this course, you must do the following:

1. Complete the online application form
2. Submit a Microsoft Word (.docx) document containing three short paragraphs with a biography, work history and description of your current research interests; each paragraph should be no more than 100 words
3. Provide a letter of support from a supervisor or a senior co-worker explaining why you should be selected for this course

Please submit all documents to Meredith Willmott (meredith@ebi.ac.uk) by midnight GMT on Friday 24 January, 2020.

Incomplete applications will not be considered.