This course provides a detailed tutorial on performing pseudo-bulk analysis for single-cell RNA-Seq data in R. By the end of the course, learners will understand the concept of pseudo-bulk analysis, its significance, and how to execute the analysis. The course covers manipulating data, aggregating counts to sample level, and conducting differential expression analysis using DESeq2 to identify differentially expressed genes in a specific cell type cluster. The teaching method involves a step-by-step walkthrough of the workflow. This course is intended for individuals interested in bioinformatics, genomics, and single-cell RNA-Seq data analysis.
Pseudo-Bulk Analysis for Single-Cell RNA-Seq Data - Detailed Workflow Tutorial
Overview
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
Taught by
bioinformagician
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