Introduction to Single-cell RNA-seq in R

Workshop Information

Tuesday 3rd October 09:30 - 16:30

Barber House Boardroom, The University of Sheffield (PROVISONAL), or online, Book here

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

Registration Link

You can sign-up to attend either in-person or online using this link

https://onlineshop.shef.ac.uk/conferences-and-events/faculty-of-medicine-dentistry-and-health/medical-school

Overview

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

Setup

1) First, install both R and RStudio for your operating system.

Windows

Install R by downloading and running this .exe file from CRAN. Also, please install the RStudio IDE. Note that if you have separate user and admin accounts, you should run the installers as administrator (right-click on .exe file and select “Run as administrator” instead of double-clicking). Otherwise problems may occur later, for example when installing R packages.

Mac

Install R by downloading and running this .pkg file from CRAN. Also, please install the free RStudio IDE

Linux

You can download the binary files for your distribution from CRAN. Or you can use your package manager (e.g. for Debian/Ubuntu run sudo apt-get install r-base and for Fedora run sudo yum install R). Also, please install free the RStudio IDE.

2) Please download and extract (un-zip) this zip file into the directory on the computer that you wish to work in

Course Data

3) Type the following into the R console to install some extra R packages required for the workshop

source("https://raw.githubusercontent.com/sheffield-bioinformatics-core/scrnaseq_course/main/install_packages.R")

Mac Users may get the following error message when trying to install these packages

xcrun error: inactive developer path (/Library/Developer/CommandLineTools), missing xcrun at:.....

If this is the case, you will need to follow the instructions from this link to install “Xcode”

https://apple.stackexchange.com/questions/254380/why-am-i-getting-an-invalid-active-developer-path-when-attempting-to-use-git-a

Window users might get a message that Rtools is required. This shouldn’t be necessary, but you might need it for other packages. It can be installed here:-