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

This tutorial demonstrates pseudo-bulk differential expression analysis for single-cell RNA-Seq data in R. It covers data retrieval, quality control, Seurat processing, sample-level count aggregation, metadata preparation, and DESeq2 analysis of cell-type-specific expression differences.

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