Pseudobulk and related approaches for scRNA-seq analysis

Audience	Computational skills required	Duration
Biologists	Introduction to R	4-session online workshop (8 hours of trainer-led time)

Description

This repository has teaching materials for a hands-on workshop on Pseudobulk and related approaches for scRNA-seq analysis. After clustering cells as part of a single-cell RNA-seq experiment, investigators are often interested in carrying out a differential expression analysis between conditions within certain cell types. When the experimental design includes replicates with multiple conditions there is power in identifying differentially expressed genes using a pseudobulk approach. This workshop will:

Discuss various approaches for differential expression using single cell RNA-seq data
Prepare single-cell RNA-seq raw count data for pseudobulk analysis
Perform differential expression analysis on pseudobulk counts using DESeq2
Present approaches for evaluating differential proportions of cells between conditions

Working knowledge of R is required or completion of the Introduction to R workshop.

Note for Trainers:

Please note that the schedule linked below assumes that learners will spend between 3-4 hours on reading through, and completing exercises from selected lessons between classes. The online component of the workshop focuses on more exercises and discussion/Q & A.

Note

These materials were developed for a trainer-led workshop, but are also amenable to self-guided learning.

Learning Objectives

Understanding considerations for when to use different DGE algorithms on scRNA-seq data
Using FindMarkers to evaluate significantly DE genes
Aggregating single cell expression data into a pseudobulk counts matrix to run a DESeq2 workflow
Evaluating expression patterns of differentially expressed genes at the pseudobulk and single cell level
Application of methods for evaluating differential proportions of cells between conditions

Lessons

Installation Requirements

Applications

Download the most recent versions of R and RStudio for your laptop:

R (version 4.3.0 or above)
RStudio

Packages for R

Note 1: Install the packages in the order listed below.

Note 2: All the package names listed below are case sensitive!

Note 3: If you have a Mac, download and install this tool before installing your packages if using R versions 4.3.0 up to 4.4.3: https://mac.r-project.org/tools/gfortran-12.2-universal.pkg And this tool if using R versions after 4.4.3: https://mac.r-project.org/tools/gfortran-14.2-universal.pkg

Note 4: At any point (especially if you’ve used R/Bioconductor in the past), in the console R may ask you if you want to update any old packages by asking Update all/some/none? [a/s/n]:. If you see this, type “a” at the prompt and hit Enter to update any old packages. Updating packages can sometimes take quite a bit of time to run, so please account for that before you start with these installations.

Note 5: If you see a message in your console along the lines of “binary version available but the source version is later”, followed by a question, “Do you want to install from sources the package which needs compilation? y/n”, type n for no, and hit enter.

(1) Install the 10 packages listed below from CRAN using the install.packages() function.

install.packages("Seurat")
install.packages("tidyverse")
install.packages("pheatmap")
install.packages("RColorBrewer")
install.packages("cowplot")
install.packages("dplyr")
install.packages("ggalluvial")
install.packages("msigdbr")
install.packages("ggvenn")
install.packages("BiocManager")

(2) Install the 8 packages listed below from Bioconductor using the the BiocManager::install() function.

library(BiocManager)
BiocManager::install("DESeq2")
BiocManager::install("EnhancedVolcano")
BiocManager::install("SingleCellExperiment")
BiocManager::install("miloR")
BiocManager::install("clusterProfiler")
BiocManager::install("org.Mm.eg.db")
BiocManager::install("sccomp")
BiocManager::install("speckle")

Note

There are two ways to install sccomp, but both ways will require that you install cmdstanr. The instructions for installing sccomp using Bioconductor are below:

# Step 1
BiocManager::install("sccomp")

# Step 2
install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev/", getOption("repos")))

# Step 3
cmdstanr::check_cmdstan_toolchain(fix = TRUE) # Just checking system setting
cmdstanr::install_cmdstan()

If you run into issues installing sccomp via Bioconductor, you can try installing it from GitHub by using devtools:

install.packages("devtools")
library(devtools)

# Step 1
devtools::install_github("MangiolaLaboratory/sccomp")

# Step 2
install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev/", getOption("repos")))

# Step 3
cmdstanr::check_cmdstan_toolchain(fix = TRUE) # Just checking system setting
cmdstanr::install_cmdstan()

(3) Finally, please check that all the packages were installed successfully by loading them one at a time using the library() function.

library(Seurat)
library(tidyverse)
library(pheatmap)
library(RColorBrewer)
library(cowplot)
library(dplyr)
library(DESeq2)
library(EnhancedVolcano)
library(SingleCellExperiment)
library(miloR)
library(ggalluvial)
library(clusterProfiler)
library(org.Mm.eg.db)
library(msigdbr)
library(ggvenn)
library(sccomp)
library(speckle)

(4) Once all packages have been loaded, run sessionInfo().

sessionInfo()

--- sidebar: false toc: false --- # Pseudobulk and related approaches for scRNA-seq analysis | Audience | Computational skills required| Duration | :----------|:----------|:----------| | Biologists | [Introduction to R](https://hbctraining.github.io/Intro-to-R-flipped/) | 4-session online workshop (8 hours of trainer-led time)| ### Description This repository has teaching materials for a hands-on workshop on **Pseudobulk and related approaches for scRNA-seq analysis**. After clustering cells as part of a single-cell RNA-seq experiment, investigators are often interested in carrying out a differential expression analysis between conditions within certain cell types. When the experimental design includes replicates with multiple conditions there is power in identifying differentially expressed genes using a pseudobulk approach. This workshop will: - Discuss various approaches for differential expression using single cell RNA-seq data - Prepare single-cell RNA-seq raw count data for pseudobulk analysis - Perform differential expression analysis on pseudobulk counts using DESeq2 - Present approaches for evaluating differential proportions of cells between conditions Working knowledge of R is required or completion of the [Introduction to R workshop](https://hbctraining.github.io/Intro-to-R-flipped/). ::: calout-note # Note for Trainers: Please note that the schedule linked below assumes that learners will spend between 3-4 hours on reading through, and completing exercises from selected lessons between classes. The online component of the workshop focuses on more exercises and discussion/Q & A. ::: ::: callout-note These materials were developed for a trainer-led workshop, but are also amenable to self-guided learning. ::: ### Learning Objectives * Understanding considerations for when to use different DGE algorithms on scRNA-seq data * Using FindMarkers to evaluate significantly DE genes * Aggregating single cell expression data into a pseudobulk counts matrix to run a DESeq2 workflow * Evaluating expression patterns of differentially expressed genes at the pseudobulk and single cell level * Application of methods for evaluating differential proportions of cells between conditions ### Lessons * [Workshop schedule (trainer-led learning)](lessons/schedule.qmd) * [Self-learning](lessons/self-learning.qmd) ### Installation Requirements #### Applications Download the most recent versions of R and RStudio for your laptop: - [R](http://lib.stat.cmu.edu/R/CRAN/) **(version 4.3.0 or above)** - [RStudio](https://www.rstudio.com/products/rstudio/download/#download) #### Packages for R > **Note 1: Install the packages in the order listed below.** > **Note 2: All the package names listed below are case sensitive!** > **Note 3**: **If you have a Mac,** download and install this tool before installing your packages if using R versions 4.3.0 up to 4.4.3: https://mac.r-project.org/tools/gfortran-12.2-universal.pkg And this tool if using R versions after 4.4.3: https://mac.r-project.org/tools/gfortran-14.2-universal.pkg > **Note 4**: At any point (especially if you’ve used R/Bioconductor in the past), in the console **R may ask you if you want to update any old packages by asking Update all/some/none? [a/s/n]:**. If you see this, **type "a" at the prompt and hit Enter** to update any old packages. _Updating packages can sometimes take quite a bit of time to run, so please account for that before you start with these installations._ > **Note 5:** If you see a message in your console along the lines of “binary version available but the source version is later”, followed by a question, **“Do you want to install from sources the package which needs compilation? y/n”, type n for no, and hit enter**. **(1)** Install the 10 packages listed below from **CRAN** using the `install.packages()` function. ```{r} #| eval: false install.packages("Seurat") install.packages("tidyverse") install.packages("pheatmap") install.packages("RColorBrewer") install.packages("cowplot") install.packages("dplyr") install.packages("ggalluvial") install.packages("msigdbr") install.packages("ggvenn") install.packages("BiocManager") ``` **(2)** Install the 8 packages listed below from **Bioconductor** using the the `BiocManager::install()` function. ```{r} #| eval: false library(BiocManager) BiocManager::install("DESeq2") BiocManager::install("EnhancedVolcano") BiocManager::install("SingleCellExperiment") BiocManager::install("miloR") BiocManager::install("clusterProfiler") BiocManager::install("org.Mm.eg.db") BiocManager::install("sccomp") BiocManager::install("speckle") ``` ::: callout-note There are two ways to install `sccomp`, but both ways will require that you install `cmdstanr`. The instructions for installing `sccomp` using Bioconductor are below: ```{r} #| label: install_sccomp_bioc #| eval: false # Step 1 BiocManager::install("sccomp") # Step 2 install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev/", getOption("repos"))) # Step 3 cmdstanr::check_cmdstan_toolchain(fix = TRUE) # Just checking system setting cmdstanr::install_cmdstan() ``` If you run into issues installing `sccomp` via Bioconductor, you can try installing it from GitHub by using `devtools`: ```{r} #| label: install_sccomp_github #| eval: false install.packages("devtools") library(devtools) # Step 1 devtools::install_github("MangiolaLaboratory/sccomp") # Step 2 install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev/", getOption("repos"))) # Step 3 cmdstanr::check_cmdstan_toolchain(fix = TRUE) # Just checking system setting cmdstanr::install_cmdstan() ``` ::: **(3)** Finally, please check that all the packages were installed successfully by **loading them one at a time** using the `library()` function. ```{r} #| eval: false library(Seurat) library(tidyverse) library(pheatmap) library(RColorBrewer) library(cowplot) library(dplyr) library(DESeq2) library(EnhancedVolcano) library(SingleCellExperiment) library(miloR) library(ggalluvial) library(clusterProfiler) library(org.Mm.eg.db) library(msigdbr) library(ggvenn) library(sccomp) library(speckle) ``` **(4)** Once all packages have been loaded, run sessionInfo(). ```{r} #| eval: false sessionInfo() ``` ---