Bulk RNAseq differential expression (DeSeq2)

DESeq2 Differential Expression Analysis

Core DESeq2 workflow for RNA-seq differential expression analysis with count data.

When to Use This Skill

Use DESeq2 when you have:

• ✅ Raw integer count data (not normalized TPM/FPKM)
• ✅ Biological replicates (≥2 per condition, ≥4 recommended)
• ✅ Need for log fold change shrinkage (ranking/visualization)
• ✅ Medium to large sample sizes (DESeq2's strength)

Don't use DESeq2 for:

• ❌ Normalized data (TPM/FPKM) → use limma-voom instead
• ❌ Very small samples (n=2-3) → consider edgeR quasi-likelihood

Quick Start (Example Data)

Test this skill with real RNA-seq data in ~2 minutes:

source("scripts/load_example_data.R")
data <- load_pasilla_data()  # Auto-installs pasilla package if needed (~2 min, ~50MB)
counts <- data$counts        # 14,599 genes × 7 samples
coldata <- data$coldata      # Metadata: treated vs untreated

# Run complete workflow
source("scripts/basic_workflow.R")  # Creates dds, res, resLFC objects + prints summary

What you get:

• Dataset: Drosophila pasilla gene RNAi knockdown (Brooks et al. 2011)
• Comparison: 3 treated vs 4 untreated samples
• Expected results: ~1,000 significant genes at padj < 0.1

For your own data: Replace data loading with your count matrix and metadata (see Inputs section).

Installation

Core packages (required):

# Set CRAN mirror first (required for installation)
options(repos = c(CRAN = "https://cloud.r-project.org"))

if (!require('BiocManager', quietly = TRUE))
    install.packages('BiocManager')
BiocManager::install(c('DESeq2', 'apeglm'))

Example data packages (optional - for testing/learning):

BiocManager::install(c('pasilla', 'airway'))  # ~70MB total, ~2-3 min

Visualization packages (required for QC plots):

# For publication-quality plots (required - generates PNG)
install.packages(c('ggplot2', 'ggprism', 'ggrepel'))

# For SVG export (optional - generates both PNG + SVG)
install.packages('svglite')