Summary and Schedule
In the last few years, the profiling of a large number of genome-wide features in individual cells has become routine. Consequently, a plethora of tools for the analysis of single-cell data has been developed, making it hard to understand the critical steps in the analysis workflow and the best methods for each objective of one’s study.
This tutorial aims to provide a solid foundation in using Bioconductor tools for single-cell RNA-seq (scRNA-seq) analysis by walking through various steps of typical workflows using example datasets.
This tutorial is based on the the online book “Orchestrating Single-Cell Analysis with Bioconductor” (OSCA), started in 2018 and continuously updated by many contributors from the Bioconductor community. Like the book, this tutorial strives to be of interest to the experimental biologists wanting to analyze their data and to the bioinformaticians approaching single-cell data.
This is a new lesson built with The Carpentries Workbench.
Prerequisites
- Familiarity with R/Bioconductor, such as the Introduction to data analysis with R and Bioconductor lesson.
- Familiarity with the biology of gene expression and scRNA-seq, such as the review article A practical guide to single-cell RNA-sequencing by Haque et.al.
Setup Instructions | Download files required for the lesson | |
Duration: 00h 00m | 1. Introduction to Bioconductor and the SingleCellExperiment class |
What is Bioconductor? How is single-cell data stored in the Bioconductor ecosystem? What is a SingleCellExperiment
object?
|
Duration: 00h 30m | 2. Exploratory data analysis and quality control |
How do I examine the quality of single-cell data? What data visualizations should I use during quality control in a single-cell analysis? How do I prepare single-cell data for analysis? |
Duration: 01h 15m | 3. Cell type annotation |
How can we identify groups of cells with similar expression
profiles? How can we identify genes that drive separation between these groups of cells? How to leverage reference datasets and known marker genes for the cell type annotation of new datasets? ::: |
Duration: 02h 00m | 4. Multi-sample analyses |
How can we integrate data from multiple batches, samples, and
studies? How can we identify differentially expressed genes between experimental conditions for each cell type? How can we identify changes in cell type abundance between experimental conditions? |
Duration: 02h 45m | 5. Working with large data |
How do we work with single-cell datasets that are too large to fit in
memory? How do we speed up single-cell analysis workflows for large datasets? How do we convert between popular single-cell data formats? |
Duration: 02h 57m | 6. Accessing data from the Human Cell Atlas (HCA) | How to obtain single-cell reference maps from the Human Cell Atlas? |
Duration: 03h 27m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
- Install R, RStudio and packages (see below).
R and RStudio
- R and RStudio are separate downloads and installations. R is a programming language and collection of software that implements that language. RStudio is a graphical integrated development environment (IDE) that makes using R much easier and more interactive. You need to install R before you install RStudio. After installing both programs, you will need to install some R libraries from within RStudio. Follow the instructions below for your operating system, and then follow the instructions to install packages.
You are running Windows
- Open RStudio, and click on “Help” > “Check for updates”. If a new version is available, quit RStudio, and download the latest version for RStudio.
- To check which version of R you are using, start RStudio and the
first thing that appears in the console indicates the version of R you
are running. Alternatively, you can type
sessionInfo()
, which will also display which version of R you are running. Go on the CRAN website and check whether a more recent version is available. If so, please download and install it. You can check here for more information on how to remove old versions from your system if you wish to do so. - Follow the steps in the instructions for everyone at the bottom of this page.
- Download R from the CRAN website.
- Run the
.exe
file that was just downloaded - Go to the RStudio download page
- Under All Installers select RStudio xxxx.yy.zz-uuu.exe - Windows 10/11 (where x, y, z, and u represent version numbers)
- Double click the file to install it
- Once it’s installed, open RStudio to make sure it works and you don’t get any error messages
- Follow the steps in the instructions for everyone at the bottom of this page.
You are running macOS
- Open RStudio, and click on “Help” > “Check for updates”. If a new version is available, quit RStudio, and download the latest version for RStudio.
- To check the version of R you are using, start RStudio and the first
thing that appears on the terminal indicates the version of R you are
running. Alternatively, you can type
sessionInfo()
, which will also display which version of R you are running. Go on the CRAN website and check whether a more recent version is available. If so, please download and install it. - Follow the steps in the instructions for everyone at the bottom of this page.
- Download R from the CRAN website.
- Select the
.pkg
file for the latest R version - Double click on the downloaded file to install R
- It is also a good idea to install XQuartz (needed by some packages)
- Go to the RStudio download page
- Under All Installers select RStudio xxxx.yy.zz-uuu.dmg - macOS 10.15+ (where x, y, z, and u represent version numbers)
- Double click the file to install RStudio
- Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.
- Follow the steps in the instructions for everyone at the bottom of this page.
You are running Linux
- Follow the instructions for your distribution from CRAN, they provide
information to get the most recent version of R for common
distributions. For most distributions, you could use your package
manager (e.g., for Debian/Ubuntu run
sudo apt-get install r-base
, and for Fedorasudo yum install R
), but we don’t recommend this approach as the versions provided by this are usually out of date. In any case, make sure you have at least R 4.2.0. - Go to the RStudio download page
- Under All Installers select the version that matches your
distribution, and install it with your preferred method (e.g., with
Debian/Ubuntu
sudo dpkg -i rstudio-xxxx.yy.zz-uuu-amd64.deb
at the terminal). - Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.
- Follow the steps in the instructions for everyone
For everyone
After installing R and RStudio, you need to install some packages that will be used during the workshop. We will also learn about package installation during the course to explain the following commands. For now, simply start RStudio by double-clicking the icon and enter these commands:
R
install.packages(c("BiocManager", "remotes"))
BiocManager::install(c("AUCell", "batchelor", "BiocNeighbors",
"BiocParallel", "BiocSingular", "BiocStyle",
"bluster", "CuratedAtlasQueryR", "dplyr",
"DropletUtils", "edgeR", "EnsDb.Mmusculus.v79",
"ggplot2", "GSEABase", "MouseGastrulationData",
"pheatmap", "scater", "scDblFinder", "scran",
"scuttle", "Seurat", "SeuratData",
"SingleCellExperiment", "SingleR",
"TENxBrainData", "zellkonverter"),
Ncpus = 4)
On the off chance your computer don’t have multiple CPU cores, remove
the Ncpus = 4
argument.
If you’ve worked with Bioconductor packages before, and the installation command offers to update packages that have newer versions, saying something like:
<...long list of packages...>
Update all/some/none? [a/s/n]:
Select n
for no. If old packages turn out to be a
problem, cross that bridge when you come to it.