Single Cell RNA Seq Analysis

Description

This workshop will focus on the analysis of single-cell RNA-seq data starting from the raw counts matrices. You'll learn about quality control of the raw data, normalization, feature selection, dimensionality reduction, clustering, finding marker genes, and batch correction. You'll analyze data using R.

The discussions are organized in three sessions:

• Session 1: Load the data, quality control, normalization, feature selection, dimensionality reduction.

• Session 2: Dimensionality reduction (continued), clustering, finding marker genes.

• Session 3: Advanced discussion on normalization, differential analysis, batch-correction, Q&A.

Note: Attendance in all three sessions is highly recommended. Each session builds upon prior sessions.

Required experience:

• Familiarity with R and RStudio (e.g., reading in files, working with lists and dataframes)
• Some exposure to RNA-seq datasets

Learning Path

   This is an advanced workshop in the RNA-Seq Analysis series. Prior experience with RNA-Seq analysis is required. See introductory and intermediate workshops in the RNA-Seq Analysis series.

Materials

Sessions 1-2:

The slides are here .

1. There is a zip file with code and data at this link.
2. Open the link, press the download button (usually on the top right corner), and select “Direct Download” if a menu pops up.
3. The zip file download should start.
4. Unzip the zip file.

Session 3

1. The slides for this session can be found here
2. The Rmarkdown file along with the support data and R code can downloaded in this folder.
3. Follow the instructions above to unzip this file.

Pre-workshop Instructions

Sessions 1-2

1. Install R and RStudio
2. Install the below packages in RStudio:

# Install tidyverse (https://www.tidyverse.org/packages/) 
install.packages("tidyverse")
library(tidyverse)

# Install Seurat (https://satijalab.org/seurat/install.html#cran)
install.packages('Seurat')
library(Seurat)

# Please make sure that you have Seurat version 4 or higher installed by running the below command
packageVersion("Seurat")

# Note: If you see a warning about installing from source, respond with ‘y’ for yes

# Install sctransform from CRAN
install.packages("sctransform")
library(sctransform)

# Install patchwork from CRAN
install.packages('patchwork')
library(patchwork)

# Install glmGamPoi from Bioconductor
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("glmGamPoi")

3. Open the “hands_on_component.Rmd” file (download here) in RStudio.
4. Press the Knit button.

If your installations were successful, RStudio will run all the steps and compile an HTML document that should look like the file named “hands_on_component_output.html” in the workshop materials. It may take some time to run through all the steps.

Session 3

I am assuming you have installed R, RStudio, Seurat and tidyverse per previous session. If not then please follow the instructions above. Then install the packages indicated below. You should be able to knit the associated R markdown file now.

1. Install the following bioconductor packages muscat, harmony and SummarizedExperiment in RStudio:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("muscat")
BiocManager::install("SummarizedExperiment")
BiocManager::install("harmony")

2. Install the following R packages

install.packages('magrittr')
install.packages('lme4')
install.packages('gdata')
install.packages('ssizeRNA')

Note: If you see a warning about installing from source, respond with ‘y’ for yes

2. Open the “Session3.Rmd” file (download here) in RStudio.
3. Press the Knit button.

If your installations were successful, RStudio will run all the steps and compile an HTML document that should look like the file named “Session3.html” in the workshop materials. It may take some time to run through all the steps.

Online Learning

You can access these materials remotely at any time and go through them at your own pace. Here's how:

1. Download the materials for Sessions 1-3 as described above. Study the slide deck for a review of current practices in scRNA-seq analysis (Jan, 2023).
2. Published benchmarks for different classes methods/analyses on single-cell RNA-seq data.