Single-cell RNA-seq Analysis

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.

Topics

(Note: session times are approximate)

Time	Topic	Links
15 min	Welcome
45 min	Introduction to single-cell technologies	Slides
15 min	Preamble: data set and workflow	Slides
1 h 30 min	Library structure, cellranger for alignment and cell calling	Slides Practical
2 h	QC and exploratory analysis	Slides Practical
2 h	Normalisation and feature selection	Slides Practical
2 h	Dimensionality reduction	Slides Practical
2 h	Batch correction and data set integration	Slides Practical
1 h 30 min	Cell clustering	Slides Practical
1 h	Identification of cluster marker genes	Slides Practical
1 h 30 min	Differential expression analysis	Slides Practical
1 h 30 min	Differential abundance analysis	Slides Practical

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.

Software

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",
                           "harmony",
                           "tidyverse",
                           "ggbeeswarm",
                           "glmGamPoi",
                           "patchwork",
                           "SparseArray",
                           "scales",
                           "bluster",
                           "cluster",
                           "DESeq2",
                           "SingleCellExperiment",
                           "scater",
                           "BiocParallel",
                           "miloR",
                           "scran",
                           "Matrix"))
  
    # for much faster FindAllMarkers()
    remotes::install_github("immunogenomics/presto")

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

Additional Resources

• Modern Statistics for Modern Biology - a fantastic resource to go deeper into some of the topics covered in this course. In particular chapter 8 of this book covers the statistical models for count data used in RNA-seq.
• Orchestrating Single-Cell Analysis with Bioconductor
• Single-cell best practices

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!