DESeq2 Differential Expression Analysis (R/Bioconductor)

DESeq2 Differential Expression Analysis (R/Bioconductor)

Overview

DESeq2 is the Bioconductor R package for differential gene expression analysis from bulk RNA-seq count data. It fits a negative binomial generalized linear model per gene, estimates dispersion parameters using empirical Bayes shrinkage across genes, and tests differential expression using Wald tests (two-group) or likelihood ratio tests (complex designs). DESeq2 is the R gold standard for RNA-seq DE analysis, with native Bioconductor integration for seamless import from Salmon (tximeta/tximport), featureCounts, or HTSeq.

When to Use

• Identifying differentially expressed genes between two experimental conditions (treated vs. control, disease vs. healthy) from bulk RNA-seq count data
• Analyzing multi-factor designs that account for batch effects or covariates (e.g., ~ batch + condition)
• Testing complex hypotheses with interaction terms (e.g., time × treatment) or reduced models using likelihood ratio tests (LRT)
• Importing Salmon pseudoalignment output via tximeta or tximport for transcript-level uncertainty propagation
• Performing LFC shrinkage with apeglm for ranked gene lists, volcano plots, and downstream pathway analysis
• Conducting time-series experiments or any design with more than two levels requiring model comparison
• Working in an R/Bioconductor ecosystem where integration with SummarizedExperiment, clusterProfiler, or EnhancedVolcano is needed
• Use pydeseq2-differential-expression instead for Python-based pipelines with the same statistical model
• Use edgeR for negative binomial DE with TMM normalization, quasi-likelihood F-tests, or TREAT testing
• Use omics-plotting SKILL after DE and for publication-quality plots of DESeq2 results
• Use gseapy-gene-enrichment SKILL after DE to interpret results at the pathway level

Prerequisites

• R packages: DESeq2 (Bioconductor), tximeta or tximport (Salmon import), apeglm (LFC shrinkage), pheatmap, ggplot2, EnhancedVolcano
• Data requirements: Raw (unnormalized) integer count matrix — gene rows × sample columns — plus a sample metadata data frame with matching column names. If using Salmon: per-sample quant.sf files and a transcript-to-gene mapping
• Environment: R ≥ 4.2, Bioconductor ≥ 3.16

# Install Bioconductor packages
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c("DESeq2", "tximeta", "tximport", "apeglm",
                        "EnhancedVolcano"))
install.packages(c("pheatmap", "ggplot2", "dplyr"))

Pre-flight Interview

Settle these with the user before writing any analysis code.