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Differential expression analysis of scRNAseq

Audience Computational skills required Duration
Biologists Introduction to R 3-session online workshop (~7.5 hours of trainer-led time)

Description

This repository has teaching materials for a hands-on Differential expression analysis of single-cell RNA-seq analysis workshop. 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 multi-conditions there is power in identifying differentially expressed genes using a pseudbulk 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.

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:

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 intalling your packages: https://mac.r-project.org/tools/gfortran-12.2-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 4 packages listed below from Bioconductor using the the BiocManager::install() function.

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

Please install them one-by-one as follows:

BiocManager::install("DESeq2")
BiocManager::install("EnhancedVolcano")
& so on ...

(2) Install the 7 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")

Please install them one-by-one as follows:

install.packages("Seurat")
install.packages("tidyverse")
install.packages("pheatmap")
& so on ...

(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)

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

sessionInfo()