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, trajectory analysis, and network analysis of single-cell datasets. You'll analyze data using R, except for the network analysis using Cytoscape.

The discussions are organized in four 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, and batch-correction.

• Session 4: Network analysis of single-cell data, Q&A.

Note: Attendance in all four 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:

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 Rmarkdown file along with the support data and R code can downloaded in this folder.
2. Follow the instructions above to unzip this file.

Session 4

1. The slides for this session can be found here.
2. Here are notebooks you can download as well:

• R notebooks (Click button in upper-right: Code > Download Rmd):
  • Use case #1: scNetViz with public atlas data in R
  • Use case #2: scNetViz with your own data in R
• Python notebooks (Click "Open in Colab" or click Raw to download)
  • Use case #1: scNetViz with public atlas data in Python
  • Use case #2: scNetViz with your own data in Python

Pre-workshop Instructions

Sessions 1-2

1. Install R and RStudio
2. Install tidyverse in RStudio:

install.packages("tidyverse")
library(tidyverse)

3. Install Seurat in RStudio:

install.packages('Seurat')
library(Seurat)

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

4. Open the “hands_on_component.Rmd” file in RStudio.
5. 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 muscat and SummarizedExperiment in RStudio:

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

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

2. Install the following R packages

install.packages('magrittr')

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

2. Open the “Session3.Rmd” file 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.

Session 4

1. Follow the installation steps for Cytoscape and RCy3. Skip the steps for R and RStudio if you already have those installed and up-to-date.
2. Launch Cytoscape and then visit these pages to install Cytoscape apps:

• https://apps.cytoscape.org/apps/scnetviz
• https://apps.cytoscape.org/apps/stringapp
• https://apps.cytoscape.org/apps/largestsubnetwork

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 1-2 as described above. Study the slide deck for a review of current practices in scRNA-seq analysis (Nov, 2020).
2. More on materials for Sessions 1-2 to be added shortly.
3. Published benchmarks for different classes methods/analyses on single-cell RNA-seq data.