Workshop 4: scRNA-seq analysis using Seurat

Fursham Hamid

Overview


Duration: 3 hours

Objectives

  1. Describe scRNA-seq workflow using Seurat
  2. Create and interact with Seurat Objects
  3. Perform QC on dataset and filter out poor-quality cells
  4. Normalize and scale scRNA-seq data
  5. Determine cell phases and regress out cell cycle genes
  6. Reduce data complexity using linear and non-linear dimensionality reduction
  7. Cluster cells into transcriptomically similar groups
  8. Identify differentially expressed markers
  9. Visualize scRNA-seq data using high-quality plots

Before starting the lesson

In this workshop, we will be using R programming language to process scRNA-seq data. Please ensure that you have both R and RStudio installed on your computer. Refer to the links below for instructions to download these software.

In addition, we will be using several R packages to facilitate this analysis. To download these R packages on your computer, open RStudio and copy the following block of code into the console and press Enter:

install.packages("Seurat")
install.packages("tidyverse")

During the lesson, we will be using etherpad, an online collaborative platform for our discussions and activities. You may bookmark this etherpad page before the lesson commence!


Lesson materials

In this workshop, we will be using Seurat 4.0, a powerful R package to analyze scRNA-seq data. This steps of this workflow is well-documented and we will be using these vignettes for our references:

  1. PBMC Guided tutorial
  2. Cell-Cycle Socring and Regression