deseq2-differential-expression

name	deseq2-differential-expression
description	Bulk RNA-seq DE with R/Bioconductor DESeq2. Negative binomial GLM, empirical Bayes shrinkage, Wald/LRT tests, multi-factor designs, Salmon tximeta import, apeglm LFC shrinkage, MA/volcano/heatmap viz. R gold standard. Use pydeseq2-differential-expression for Python; use edgeR for TMM normalization.
license	LGPL-3.0

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    
               alpha  
summaryres

res_shrunk  lfcShrinkdds contrast    
                         type   coef  

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", 
    row.names  colnamescounts



dds  DESeqDataSetFromMatrixcountData  counts
                               colData    coldata
                               design      condition

cat ncoldds 
cat nrowdds 
cat levelscoldatacondition 

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"

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")

ggplotpca_data aesPC1 PC2 color  condition shape  batch 
    geom_pointsize   
    xlabpaste0 percent_var  
    ylabpaste0 percent_var  
    ggtitle 
    theme_bw

ggsave width   height  
cat

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)
# out of N with nonzero total read count:
# adjusted p-value < 0.05: ...
# LFC > 0 (up): ...
# LFC < 0 (down): ...
# outliers [Cook's distance]: ...
# low counts [below independent filtering threshold]: ...

# Convert to data frame and sort by adjusted p-value
res_df <- as.data.frame(res)
res_df <- res_df[order(res_df$padj, na.last = TRUE), ]

cat res_dfpadj   na.rm   
cat res_dfpadj    res_dflog2FoldChange   na.rm   
cat res_dfpadj    res_dflog2FoldChange   na.rm   

write.csvres_df 

Step 6: LFC Shrinkage with apeglm

Shrink log2 fold changes using the adaptive t prior (apeglm). Use shrunk LFCs for MA plots, volcano plots, and gene ranking — NOT for significance calling (padj comes from the unshrunk Wald test).

library(apeglm)

# apeglm shrinkage — specify coefficient name from resultsNames(dds)
res_shrunk <- lfcShrink(dds,
                         coef = "condition_treated_vs_control",
                         type = "apeglm",
                         res  = res)    # pass original results for speed

# Compare shrunk vs. unshrunk LFC
cat("Max |LFC| unshrunk:", max(abs(res$log2FoldChange), na.rm = TRUE), "\n")
cat("Max |LFC| shrunk:  ", max(abs(res_shrunk$log2FoldChange), na.rm = TRUE), "\n")

# Export shrunk results
res_shrunk_df <- as.data.frame(res_shrunk)
res_shrunk_df <- res_shrunk_dforderres_shrunk_dfpadj na.last   
write.csvres_shrunk_df 


sig_genes  res_shrunk_df
    res_shrunk_dfpadj 
    res_shrunk_dfpadj   
    res_shrunk_dflog2FoldChange   
cat nrowsig_genes 
write.csvsig_genes 

Step 7: Visualize — MA Plot, Volcano Plot, and Heatmap

Compute/viz separation (required): Do not combine DESeq2 computation and plotting in the same code block. Step 6 (or Step 5) saves the results CSV; this step loads that CSV and produces plots only. This way plots can be regenerated or restyled without re-running the expensive computation.

library(EnhancedVolcano)
library(pheatmap)

# --- MA Plot (base DESeq2) ---
plotMA(res_shrunk, ylim = c(-5, 5),
       main = "MA Plot (apeglm-shrunk LFC)",
       colSig = "firebrick", colNonSig = "grey60")
# Blue points are significantly DE genes; y-axis is shrunk log2FC

# --- Volcano Plot (EnhancedVolcano) ---
EnhancedVolcano(res_shrunk_df,
    lab        = rownames(res_shrunk_df),
    x          = "log2FoldChange",
    y          = "padj",
    pCutoff    = 0.05,
    FCcutoff   = 1.0,
    xlim       = c(-6, 6),
    title       
    subtitle    
    legendLabels     

ggsave width   height  


top50  headrownamessig_genesordersig_genespadj  
mat    assayvsdtop50             
mat    mat  rowMeansmat            

annotation_col  data.frame
    Condition  coldatacondition
    Batch      coldatabatch
    row.names  colnamesmat


pheatmapmat
         annotation_col   annotation_col
         cluster_rows     
         cluster_cols     
         show_rownames    
         fontsize_row     
         filename         
         width            
         height           
cat

Step 8: Multi-Factor Design and Interaction Terms

For designs with batch correction, paired samples, or interaction effects between two variables.

# --- Batch-corrected two-group comparison ---
dds_batch <- DESeqDataSetFromMatrix(countData = counts,
                                     colData   = coldata,
                                     design    = ~ batch + condition)
dds_batch <- dds_batch[rowSums(counts(dds_batch)) >= 10, ]
dds_batch <- DESeq(dds_batch)

res_batch <- results(dds_batch,
                      contrast = c("condition", "treated", "control"),
                      alpha    = 0.05)
cat("Significant (batch-corrected):", sum(res_batch$padj < 0.05, na.rm = TRUE), 



coldatagenotype  factorcoldatagenotype  

dds_int  DESeqDataSetFromMatrix
    countData  counts colData  coldata
    design   genotype  condition  genotypecondition

dds_int  DESeqdds_int
resultsNamesdds_int



res_int  resultsdds_int name   alpha  
cat res_intpadj   na.rm   



dds_lrt  DESeqdds_int test   reduced   genotype  condition
res_lrt   resultsdds_lrt
cat
    res_lrtpadj   na.rm   

Key Parameters

Parameter	Function	Default	Range / Options	Effect
design	DESeqDataSetFromMatrix()	(required)	~ var or ~ cov + var	Specifies the GLM formula; put batch/covariates before variable of interest
contrast	results()	NULL	c(var, level1, level2) or coefficient name	Defines the comparison; always specify explicitly for clarity
alpha	results()	0.1	0.01–0.10	FDR threshold used for independent filtering; does not change padj values
lfcThreshold	results()	0	0–2	Test H0:
independentFilter	results()	TRUE	TRUE/FALSE	Adaptive mean-count filtering to maximize the number of rejectible hypotheses
type	lfcShrink()	"apeglm"	"apeglm", "ashr", "normal"	Shrinkage prior; apeglm is fastest and best calibrated
coef	lfcShrink()	(required)	coefficient name from resultsNames()	Which model coefficient to shrink; must match resultsNames() output
test	DESeq()	"Wald"	"Wald", "LRT"	Wald: two-group comparisons; LRT: model comparison for complex designs

Key Concepts

Negative Binomial Model and Dispersion

DESeq2 models each gene's count as Negative Binomial(mean = µ, dispersion = α), where mean µ depends on the experimental design through a log-linear model. The key innovation is dispersion shrinkage: gene-wise dispersion estimates are shrunk toward a fitted trend across all genes using an empirical Bayes approach, borrowing information across genes to stabilize estimates from small sample sizes.

Size Factor Normalization

DESeq2 uses the median-of-ratios method: for each sample, compute the ratio of each gene's count to the geometric mean of that gene across all samples, then take the median ratio across all genes as the size factor. This is robust to differentially expressed genes and does not require any assumptions about the fraction of DE genes.

Wald Test vs. Likelihood Ratio Test

• Wald test (default): tests a single coefficient β = 0; fast and appropriate for two-group comparisons and specific contrasts in multi-factor designs
• LRT: compares the full model to a reduced model by the likelihood ratio; appropriate for testing whether any level of a multi-level factor (e.g., 5 time points) has an effect, or for testing interaction terms as a group

Cook's Distance Outlier Detection

DESeq2 computes Cook's distance per gene per sample to flag potential expression outliers. Genes where any sample has a Cook's distance above a threshold are automatically replaced with NA in the results. With refit = TRUE (default), outlier samples are excluded and the model is refit for that gene.

Common Recipes

Recipe: Multiple Contrasts from One Fitted Model

Efficient when comparing multiple treatment groups — fit DESeq() once, extract multiple contrasts.

# Fit once
dds_multi <- DESeqDataSetFromMatrix(counts, coldata, design = ~ condition)
dds_multi <- dds_multi[rowSums(counts(dds_multi)) >= 10, ]
dds_multi <- DESeq(dds_multi)

# Extract contrasts — all vs. "control"
contrasts_list <- list(
    A_vs_ctrl = c("condition", "treatment_A", "control"),
    B_vs_ctrl = c("condition", "treatment_B", "control"),
    C_vs_ctrl = c("condition", "treatment_C", "control")
)

results_list <- lapplycontrasts_list nm 
    res  resultsdds_multi contrast  contrasts_listnm alpha  
    res_shrunk  lfcShrinkdds_multi contrast  contrasts_listnm
                             type   res  res
    write.csvas.data.frameres_shrunk paste0 nm 
    catnm  respadj   na.rm   
    as.data.frameres_shrunk

results_list  contrasts_list

Recipe: Paired Sample Design

Eliminate within-subject variability by including subject as a blocking factor.

# Paired design: each subject has pre- and post-treatment samples
coldata$subject   <- factor(c("S1","S1","S2","S2","S3","S3"))
coldata$timepoint <- factor(c("pre","post","pre","post","pre","post"))

dds_paired <- DESeqDataSetFromMatrix(counts, coldata,
                                      design = ~ subject + timepoint)
dds_paired <- dds_paired[rowSums(counts(dds_paired)) >= 10, ]
dds_paired <- DESeq(dds_paired

res_paired  resultsdds_paired
                       contrast    
                       alpha     
cat res_pairedpadj   na.rm   

Recipe: Rank Genes for GSEA Using Shrunk LFC × -log10(pvalue)

Create a pre-ranked gene list for input to GSEA or gseapy's prerank function.

# Build ranking metric: signed -log10(pvalue) with LFC direction
res_rank <- as.data.frame(res_shrunk)
res_rank <- res_rank[!is.na(res_rank$pvalue) & !is.na(res_rank$log2FoldChange), ]
res_rank$rank_metric <- sign(res_rank$log2FoldChange) *
                         (-log10(res_rank$pvalue + 1e-300))
res_rank <- res_rank[order(res_rank$rank_metric, decreasing = TRUE), ]

# Export as two-column TSv for fgsea or gseapy prerank
write.table(data.frame(gene  = rownames(res_rank),
                        score = res_rank$rank_metric
            
            sep      row.names   col.names  
cat nrowres_rank 

Recipe: Save and Reload Fitted DESeq2 Object

Avoid re-running the expensive DESeq() step in subsequent sessions.

# Save fitted DDS object
saveRDS(dds, "dds_fitted.rds")
cat("Saved fitted DESeqDataSet to dds_fitted.rds\n")

# Reload in a new session
dds_loaded <- readRDS("dds_fitted.rds")
# Continue with results(), lfcShrink(), etc.
res_reloaded <- results(dds_loaded, contrast = c("condition", "treated", "control"))

Expected Outputs

File	Description
deseq2_all_results.csv	Full results table: baseMean, log2FoldChange, lfcSE, stat, pvalue, padj for all genes
deseq2_shrunk_results.csv	Results with apeglm-shrunk LFC; use for ranking and visualization
deseq2_significant.csv	Filtered results: padj < 0.05 and
pca_plot.pdf	PCA of variance-stabilized counts; used to check batch structure and outliers
dispersion_plot.pdf	Gene-wise vs. fitted dispersions; should show tight cloud around trend
volcano_plot.pdf	Volcano plot with significance and LFC thresholds labeled
heatmap_top50.pdf	Hierarchically clustered heatmap of top 50 DE genes
gsea_ranked_list.rnk	Pre-ranked gene list for pathway enrichment (fgsea/gseapy)
dds_fitted.rds	Serialized DESeqDataSet for checkpoint/resume

Troubleshooting

Problem	Cause	Solution
Error: design contains one or more variables with all samples having the same value	A design factor has only one level	Check table(coldata$condition); ensure all factor levels are present
Model matrix not full rank	Confounded design (e.g., batch perfectly correlated with condition)	Run table(coldata$batch, coldata$condition); drop the confounded variable or collect more samples
All or most padj = NA	Genes flagged by independent filtering (low mean count) or Cook's outliers	Check summary(res) for count of filtered genes; relax alpha or verify data quality
No significant genes despite strong biology	Under-powered experiment or high within-group variability	Verify n ≥ 3 per group; check PCA for outliers; inspect p-value histogram (should show spike near 0)
Error in lfcShrink: coefficient not found	coef name does not match resultsNames(dds)	Run resultsNames(dds) and copy the exact coefficient string into coef =
apeglm fails with convergence warning	Extreme LFC estimates (sparse data or complete separation)	Switch to type = "ashr" which is more robust to these cases
Very large size factors (> 5)	Extremely different library sizes, or normalized counts accidentally used	Check colSums(counts(dds)); ensure input is raw integer counts from aligner/counter
Dispersion plot shows outlier cloud far from trend	Noisy or failed libraries; incorrect metadata grouping	Examine PCA; remove outlier samples; check that design formula matches biological groups
Volcano plot: no labeled points	EnhancedVolcano defaults label too few genes	Adjust pCutoff and FCcutoff; or set selectLab = rownames(sig_genes)[1:20]

References

• DESeq2 Bioconductor page — Package home, vignette, and reference manual
• DESeq2 vignette — Comprehensive worked example covering all design types
• Love et al. (2014) Genome Biology — Original DESeq2 paper: moderated estimation of fold change and dispersion for RNA-seq data
• Zhu et al. (2019) Bioinformatics — apeglm LFC shrinkage for DESeq2
• tximeta Bioconductor page — Salmon import with transcript-level metadata
• EnhancedVolcano Bioconductor page — Publication-quality volcano plots for DE results