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 gseapy-gene-enrichment after DE to interpret results at the pathway level