Differential Gene Expression Analysis (bulk RNA-seq Part II)

Learning Objectives

• Explain and interpret QC on count data using Principal Component Analysis (PCA) and hierarchical clustering
• Implement DESeq2 to obtain a list of significantly different genes
• Perform functional analysis on gene lists with R-based tools

Installations

Follow the instructions linked here to download R and RStudio + Install Packages from CRAN and Bioconductor

Lessons

Part 1 (Getting Started)

1. Workflow (raw data to counts)
2. Experimental design considerations
3. Intro to DGE / setting up DGE analysis

---

Part II (QC and setting up for DESeq2)

1. RNA-seq counts distribution
2. Count normalization
3. Sample-level QC (PCA and hierarchical clustering)
4. Design formulas
5. Hypothesis testing and multiple test correction

---

Part III (DESeq2)

1. Description of steps for DESeq2
2. Wald test results
3. Summarizing results and extracting significant gene lists
4. Visualization
5. Likelihood Ratio Test results
6. Time course analysis

---

Part IV (Functional Analysis)

1. Gene annotation
2. Functional analysis - over-representation analysis
3. Functional analysis - functional class scoring / GSEA

---

Workflow Summary

---

Building on this workshop

• Single-cell RNA-seq workshop
• RMarkdown
• Ggplot2 for functional analysis

Resources

• DESeq2 vignette
• GitHub book on RNA-seq gene level analysis
• Bioconductor support site (posts tagged with deseq2)
• Functional analysis visualization

---

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.