self-learning.md

Introduction to Peak Analysis

Learning Objectives

• Understanding considerations for when to use different DGE algorithms on scRNA-seq data
• Using FindMarkers to evaluate significantly DE genes
• Aggregating single cell expression data into a pseudobulk counts matrix to run a DESeq2 workflow
• Evaluating expression patterns of differentially expressed genes at the pseudobulk and single cell level
• Application of methods for evaluating differential proportions of cells between conditions

Installations

On your desktop

1. R
2. RStudio
3. The listed R packages

Lessons

1. Introduction to scRNA-seq
2. scRNA-seq: From sequence reads to count matrix
3. scRNA-seq: From counts to clusters
4. Project setup and data exploration
5. Differential expression analysis using FindMarkers()
6. Aggregating counts by celltype using pseudobulk approach
7. DE analysis of pseudobulk data using DESeq2
8. Visualization of differentially expressed genes
9. Comparison of results from different DE approaches
10. Functional Analysis
11. Methods for Differental Abundance

Answer key

• Day 1 exercises
• Day 2 exercises

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These materials have been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.