SingleCell_RNAseq_Feb26

Introduction to single-cell RNA-seq data analysis

9, 16, 23 February 2026 || 09:30 - 17:30

Online via Zoom

Instructors

• Abbi Edwards (CRUK)
• Adam Reid (Gurdon Institute)
• Betty Wang (Clinical Neurosciences)
• Hugo Tavares (CRIT)
• Guest Speaker: Kasia Kania (Cosyne Therapeutics)

Outline

This workshop is aimed at biologists interested in learning how to perform standard single-cell RNA-seq analyses.

This will focus on the droplet-based assay by 10X genomics and include running the accompanying cellranger pipeline to align reads to a genome reference and count the number of read per gene, reading the count data into R, quality control, normalisation, data set integration, clustering and identification of cluster marker genes, as well as differential expression and abundance analyses. You will also learn how to generate common plots for analysis and visualisation of gene expression data, such as TSNE, UMAP and violin plots.

Prerequisites

**Some basic experience of using a UNIX/LINUX command line is assumed**

**Some R knowledge is assumed and 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 our Introduction to R course.

Data

• The course data is based on ‘CaronBourque2020’ relating to pediatric leukemia, with four sample types, including:
  • pediatric Bone Marrow Mononuclear Cells (PBMMCs)
  • three tumour types: ETV6-RUNX1, HHD, PRE-T
• The data used in the course can be downloaded from Dropbox. Please note that:
  • these data have been processed for teaching purposes and are therefore not suitable for research use;
  • all the data is provided on our training machines, you don’t need to download it to attend the course.

Schedule

PDF of materials: if you want a PDF version of the materials go to the “Print” option on your browser and select “Print to PDF” (all major browsers have this functionality).

Latest Course Materials

The materials for this course are regularly updated. Please ensure you have the latest version by visiting our course history page: Single-cell RNA-seq analysis - course listing

Day 1

• 09:30 - 09:45 Welcome - Abbi
• 09:45 - 10:30 Introduction to Single Cell Technologies - Katarzyna Kania
  • Slides
• 10:30 - 10:44 Preamble: data set and workflow - Adam
  • Slides
• 10:45 - 11:00 - Break
• 11:00 - 12:30 Library structure, cellranger for alignment and cell calling - Adam
  • Slides
  • Demonstration
• 12:30 - 13:30 Lunch break
• 13:30 - 17:00 QC and exploratory analysis - Abbi
  • Slides
  • Demonstration

Day 2

• 09:30 - 09:40 Recap - Adam
  • Slides
• 09:40 - 12:30 Normalisation and feature selection - Adam
  • Slides
  • Demonstration
• 12:30 - 13:30 lunch break
• 13:30 - 15:25 Dimensionality reduction - Betty
  • Slides
  • Demonstration
• 15:25 - 15:35 10 min break
• 15:35 - 17:30 Batch correction and data set integration - Abbi
  • Slides
  • Demonstration

Day 3

• 09:30 - 09:40 Recap - Abbi
• 09:40 - 11:05 Cell clustering - Abbi
  • Slides
  • Demonstration
• 11:05 - 11:15 10 min break
• 11:15 - 12:30 Identification of cluster marker genes - Abbi
  • Slides
  • Demonstration
• 12:30 - 13:30 lunch break
• 13:30 - 16.00 Differential Expression Analysis - Adam
  • Slides
  • Demonstration
• 16.00 - 17.30 Differential Abundance Analysis - Betty
  • Slides
  • Conversion to SingleCellExperiment
  • Milo Demonstration

Software Installation

You can make use of the computer environment provided for the course, which is ready for use and have the necessary data & software installed. However, if you want to run the analysis on your own computer, you can follow these instructions.

• Download and install R: https://cloud.r-project.org/
  • (Windows users only): Download and install RTools: https://cran.r-project.org/bin/windows/Rtools/
• Download and install RStudio: https://www.rstudio.com/products/rstudio/download/#download

• Open RStudio and run the following commands from the console:

    install.packages("BiocManager")
    BiocManager::install(c("sctransform",
                           "Seurat",
                           "tidyverse",
                           "ggbeeswarm",
                           "glmGamPoi",
                           "patchwork",
                           "SparseArray",
                           "scales",
                           "bluster",
                           "cluster",
                           "DESeq2",
                           "SingleCellExperiment",
                           "scater",
                           "BiocParallel",
                           "miloR",
                           "scran",
                           "Matrix"))
  

For Cellranger, you will need to use a Linux machine. See the installation instructions from 10x Genomics.

Acknowledgments:

This version of the course is a rewrite by Abigail Edwards and Hugo Tavares using the Seurat package of the original course developed as detailed below

Much of the material has been derived from the demonstrations found in the OSCA book and the Hemberg Group course materials. Additional material concerning miloR has been based on the demonstration from the Marioni Lab.

The materials have been contributed to by many individuals over the last several years, including:

• Abigail Edwards
• Adam Reid
• Ashley D Sawle
• Chandra Chilamakuri
• Hugo Tavares
• Jon Price
• Kamal Kishore
• Katarzyna Kania
• Roderik Kortlever
• Stephane Ballereau
• Tom Smith
• Zeynep Kalendar Atak

Apologies if we have missed anyone!