Monday 30th January 09:30 - 16:30 Bartolome House, Seminar Room EG03
This workshop will be in-person only. We will run online sessions in the future
Some basic R knowledge is assumed (and is essential). Without it, you will struggle on this course. If you are not familiar with the R statistical programming language we strongly encourage you to work through an introductory R course before attempting these materials. We recommend reviewing Parts 1 and 2 of our R introductory course before deciding if you can attend
You can sign-up to attend either in-person or online using this link
If you are registering to attend as a University of Sheffield staff or student, you will be prompted for a password. This should have been sent to you in an announcement email.
Recent advances in gene expression technologies have made it possible to measure transcription for individual cells - enabling researchers to understand cellular heterogeneity and understand complex tissue types. In this course we describe the analysis of data derived from such experiments using the R language.
We will focus specifically on data generated using the 10X protocol, although the methods will relevant to other technologies
Who should attend this course?
Researchers in life sciences who want to get an appreciation for the computational steps involved in Single-cell RNA-seq analysis, and how to execute best-practice RNA-seq workflows in R.
Objectives:- After this course you should be able to:
- Read data from a 10X experiment into R
- Perform quality assessment on single-cell RNA-seq data
- Execute different clustering methods
- Identify a set of markers to distinguish unique cell clusters
Aims:- During this course you will learn about:
- The Seurat R package
- What QC metrics are commonly-used for single-cell RNA-seq
- Normalization and integration of single-cell datasets
- The theory behind popular clustering methods for single-cell data
- Dr. Emily Chambers, Bioinformatics Core Scientist
- Dr. Mark Dunning, Bioinformatics Core Director
- Dr. Niamh Errington, Bioinformatics Postdoctural scientist