Pseudo-Bulk Analysis for Single-Cell RNA-Seq Data - Detailed Workflow Tutorial

Dive into a comprehensive tutorial on performing pseudo-bulk differential expression analysis for single-cell RNA-Seq data using R. Learn the concept of pseudo-bulk analysis, its importance, and follow a detailed workflow to execute this approach. Explore data manipulation techniques using a Seurat object, including aggregating counts to sample level, and conduct differential expression analysis with DESeq2 to identify differentially expressed genes in specific cell type clusters. Gain hands-on experience with crucial steps such as fetching data from ExperimentHub, quality control, filtering, and implementing Seurat's standard workflow. Visualize your data, make informed decisions about integrated versus non-integrated data usage, and master essential data manipulation techniques. Conclude with a thorough walkthrough of DESeq2 analysis, from creating datasets to obtaining final results.

Syllabus

Intro
WHAT is pseudo-bulk analysis?
WHY perform pseudo-bulk analysis?
onwards HOW to perform pseudo-bulk analysis?
Fetch data from ExperimentHub
QC and filtering
Seurat's standard workflow steps
Visualize data
To use integrated or nonintegrated data?
Aggregate counts to sample level
Data manipulation step 1: Transpose matrix
Data manipulation step 2: Split data frame
Data manipulation step 3: Fix row.names and transpose again
DESeq2 step 1: Get count matrix corresponding to a cell type
: Create sample level metadata i.e. colData
DESeq2 step 2: Create DESeq2 dataset from matrix
DESeq2 step 2: Run DESeq
Get results