e-learning
Pseudobulk Analysis with Decoupler and EdgeR
Abstract
Pseudobulk analysis is a powerful technique that bridges the gap between single-cell and bulk RNA-seq data. It involves aggregating gene expression data from groups of cells within the same biological replicate, such as a mouse or patient, typically based on clustering or cell type annotations.
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- How does pseudobulk analysis help in understanding cell-type-specific gene expression changes?
- What steps are required to prepare single-cell data (e.g., clustering, annotation, and metadata addition) for pseudobulk analysis?
- How can we use pseudobulk data prepared with Decoupler to perform differential expression analysis using edgeR in Galaxy?
Learning Objectives
- Understand the principles of pseudobulk analysis in single-cell data
- Understand and generate the pseudobulk expression matrix with Decoupler
- Perform differential expression analysis using edgeR
Licence: Creative Commons Attribution 4.0 International
Keywords: Single Cell, pseudobulk, transcriptomics
Target audience: Students
Resource type: e-learning
Version: 3
Status: Active
Prerequisites:
- Clustering 3K PBMCs with Scanpy
- Introduction to Galaxy Analyses
Learning objectives:
- Understand the principles of pseudobulk analysis in single-cell data
- Understand and generate the pseudobulk expression matrix with Decoupler
- Perform differential expression analysis using edgeR
Date modified: 2025-02-24
Date published: 2025-02-12
Contributors: Björn Grüning, Diana Chiang Jurado, Pavankumar Videm, Saskia Hiltemann
Activity log
