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

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"))

Quick Start

Complete two-group comparison from a count matrix in under 20 lines.

library(DESeq2)

# Load count matrix (genes x samples) and metadata
counts <- as.matrix(read.csv("counts.csv", row.names = 1))
coldata <- read.csv("metadata.csv", row.names = 1)
coldata$condition <- factor(coldata$condition)

# Build DESeqDataSet, run full pipeline
dds <- DESeqDataSetFromMatrix(countData = counts,
                               colData   = coldata,
                               design    = ~ condition)
dds <- dds[rowSums(counts(dds)) >= 10, ]   # pre-filter
dds <- DESeq(dds)

# Extract results (treated vs. control)
res <- results(dds, contrast = c("condition", "treated", "control"),
               alpha = 0.05)
summary(res)
# LFC shrinkage for visualization
res_shrunk <- lfcShrink(dds, contrast = c("condition", "treated", "control"),
                         type = "apeglm", coef = 2)

Workflow

Step 1: Prepare Count Matrix and Sample Metadata

Build a DESeqDataSet from a gene × sample count matrix and a colData data frame. Column names of the count matrix must match row names of colData.

library(DESeq2)
library(dplyr)

# Load raw count matrix (genes as rows, samples as columns)
counts <- as.matrix(read.csv("featureCounts_matrix.csv", row.names = 1))
# Or from featureCounts tab-delimited output — skip first comment line
# fc <- read.table("featurecounts_output.txt", header = TRUE, skip = 1, row.names = 1)
# counts <- as.matrix(fc[, 7:ncol(fc)])  # columns 7+ are sample counts

# Load sample metadata
coldata <- data.frame(
    condition = factor(c("control", "control", "control",
                          "treated", "treated", "treated")),
    batch     = factor(c("A", "A", "B", "A", "B", "B")),
    row.names = colnames(counts)
)

# Build DESeqDataSet
dds <- DESeqDataSetFromMatrix(countData = counts,
                               colData   = coldata,
                               design    = ~ condition)

cat("Samples:", ncol(dds), "\n")
cat("Genes:", nrow(dds), "\n")
cat("Condition levels:", levels(coldata$condition), "\n")

Step 2: Import from Salmon via tximeta

When reads were quantified with Salmon, use tximeta to import transcript-level estimates with proper offset correction that accounts for transcript length and GC bias.

library(tximeta)

# Build a coldata with a "files" column pointing to quant.sf files
quant_dirs <- file.path("salmon_output", coldata$sample_id, "quant.sf")
coldata$files <- quant_dirs
coldata$names <- coldata$sample_id

# Import with tximeta — automatically fetches transcript metadata from Ensembl
se <- tximeta(coldata)           # SummarizedExperiment with transcript-level data

# Summarize to gene level (requires Ensembl or custom txdb)
gse <- summarizeToGene(se)       # gene-level SummarizedExperiment

# Build DESeqDataSet from the SummarizedExperiment
dds <- DESeqDataSet(gse, design = ~ condition)

cat("Gene-level SE dimensions:", dim(gse), "\n")
cat("Assay names:", assayNames(gse), "\n")
# Assay "counts" = estimated counts; "abundance" = TPM; "length" = eff. lengths

Step 3: Pre-Filtering and Quality Control

Remove genes with very low counts to improve statistical power and reduce the multiple testing burden. Explore sample quality with PCA on variance-stabilized counts.

library(ggplot2)

# Pre-filter: keep genes with at least 10 reads total
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep, ]
cat("Genes after pre-filtering:", nrow(dds), "\n")

# Variance-stabilizing transformation for QC visualization
vsd <- vst(dds, blind = TRUE)    # blind = TRUE for exploratory QC

# PCA plot
pca_data <- plotPCA(vsd, intgroup = c("condition", "batch"), returnData = TRUE)
percent_var <- round(100 * attr(pca_data, "percentVar"))

ggplot(pca_data, aes(PC1, PC2, color = condition, shape = batch)) +
    geom_point(size = 3) +
    xlab(paste0("PC1: ", percent_var[1], "% variance")) +
    ylab(paste0("PC2: ", percent_var[2], "% variance")) +
    ggtitle("PCA — Variance-Stabilized Counts") +
    theme_bw()

ggsave("pca_plot.pdf", width = 6, height = 5)
cat("Saved pca_plot.pdf\n")

Step 4: Run DESeq() — Normalization, Dispersion, and Model Fitting

DESeq() runs three sequential steps: (1) median-of-ratios size factor estimation, (2) gene-wise and shrunken dispersion estimation, (3) negative binomial GLM fitting and Wald statistics.

# Run full DESeq2 pipeline
dds <- DESeq(dds)

# Inspect size factors (library normalization)
cat("Size factors:\n")
print(sizeFactors(dds))
# Expected range: 0.3–3.0; outliers indicate library quality issues

# Dispersion plot — should show decreasing dispersion trend with mean
plotDispEsts(dds, main = "Dispersion Estimates")

# Inspect fitted model coefficients
resultsNames(dds)
# [1] "Intercept" "condition_treated_vs_control"

Step 5: Extract Results and Apply FDR Correction

Use results() to extract Wald test statistics. Specify the contrast explicitly for clarity. Independent filtering maximizes the number of detectable genes.

# Extract results for the contrast of interest
res <- results(dds,
               contrast          = c("condition", "treated", "control"),
               alpha             = 0.05,   # FDR threshold for independent filtering
               lfcThreshold      = 0,      # Test H0: |LFC| = 0 (Wald test)
               independentFilter = TRUE)   # Maximize power via adaptive filtering

# Summary: how many genes are up/down/NA
summary(res)
#