Analyze RNA-seq count data with DESeq2 in R to find genes whose expression differs between conditions. Define experimental factors, account for batch effects, moderate noisy fold-change estimates, and export significant genes and normalized counts.
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Perform differential expression analysis using DESeq2 in R/Bioconductor. Use for analyzing RNA-seq count data, creating DESeqDataSet objects, running the DESeq workflow, and extracting results with log fold change shrinkage. Use when performing DE analysis with DESeq2.
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Reference examples tested with: DESeq2 1.42+, Salmon 1.10+, edgeR 4.0+, scanpy 1.10+
Before using code patterns, verify installed versions match. If versions differ:
packageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Differential expression analysis using DESeq2 for RNA-seq count data.
library(DESeq2)
library(apeglm) # For lfcShrink with type='apeglm'
if (!require('BiocManager', quietly = TRUE))
install.packages('BiocManager')
BiocManager::install('DESeq2')
BiocManager::install('apeglm')
Goal: Construct a DESeqDataSet object from various input formats for DE analysis.
Approach: Wrap count data and sample metadata into the DESeq2 container, specifying the experimental design formula.
"Load my RNA-seq counts into DESeq2" → Create a DESeqDataSet from a count matrix, SummarizedExperiment, or tximport object with sample metadata and a design formula.
# counts: matrix with genes as rows, samples as columns
# coldata: data frame with sample metadata (rownames must match colnames of counts)
dds <- DESeqDataSetFromMatrix(countData = counts,
colData = coldata,
design = ~ condition)
library(SummarizedExperiment)
dds <- DESeqDataSet(se, design = ~ condition)
library(tximport)
txi <- tximport(files, type = 'salmon', tx2gene = tx2gene)
dds <- DESeqDataSetFromTximport(txi, colData = coldata, design = ~ condition)
Goal: Run the complete DESeq2 pipeline from raw counts to shrunken log fold change estimates.
Approach: Create dataset, pre-filter low-count genes, set reference level, run size factor estimation + dispersion estimation + Wald test, then apply LFC shrinkage.
"Find differentially expressed genes between treated and control" → Test for significant expression changes between conditions using negative binomial models with empirical Bayes shrinkage.
# Create DESeqDataSet
dds <- DESeqDataSetFromMatrix(countData = counts,
colData = coldata,
design = ~ condition)
# Pre-filter low count genes (recommended)
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
# Set reference level for condition
dds$condition <- relevel(dds$condition, ref = 'control')
# Run DESeq2 pipeline (estimateSizeFactors, estimateDispersions, nbinomWaldTest)
dds <- DESeq(dds)
# Get results
res <- results(dds)
# Apply log fold change shrinkage (recommended for visualization/ranking)
resLFC <- lfcShrink(dds, coef = 'condition_treated_vs_control', type = 'apeglm')
Goal: Specify the experimental design to model biological and nuisance variables.
Approach: Build R formula objects that encode condition, batch, and interaction terms for the GLM.
# Simple two-group comparison
design = ~ condition
# Controlling for batch effects
design = ~ batch + condition
# Interaction model
design = ~ genotype + treatment + genotype:treatment
# Multi-factor without interaction
design = ~ genotype + treatment
Goal: Extract results for specific pairwise or complex comparisons from a fitted DESeq2 model.
Approach: Use coefficient names or contrast vectors to define which groups to compare.
# See available coefficients
resultsNames(dds)
# Results by coefficient name
res <- results(dds, name = 'condition_treated_vs_control')
# Results by contrast (compare specific levels)
res <- results(dds, contrast = c('condition', 'treated', 'control'))
# Contrast with list format (for complex designs)
res <- results(dds, contrast = list('conditionB', 'conditionA'))
Goal: Reduce noisy fold change estimates for low-count genes to improve ranking and visualization.
Approach: Apply empirical Bayes shrinkage (apeglm, ashr, or normal) to moderate log fold changes toward zero.
# apeglm method (default, recommended)
resLFC <- lfcShrink(dds, coef = 'condition_treated_vs_control', type = 'apeglm')
# ashr method (alternative)
resLFC <- lfcShrink(dds, coef = 'condition_treated_vs_control', type = 'ashr')
# normal method (original, less recommended)
resLFC <- lfcShrink(dds, coef = 'condition_treated_vs_control', type = 'normal')
Goal: Control the stringency of differential expression calls using adjusted p-value and fold change cutoffs.
Approach: Set alpha for multiple testing correction and optionally apply a minimum log fold change threshold.
# Default: padj < 0.1
res <- results(dds)
# Custom alpha threshold
res <- results(dds, alpha = 0.05)
# With log fold change threshold
res <- results(dds, lfcThreshold = 1) # |log2FC| > 1
Goal: Retrieve, filter, and sort DE results for downstream use.
Approach: Extract results as a data frame, subset by significance, and order by p-value or fold change.
# Summary of results
summary(res)
# Get significant genes
sig <- subset(res, padj < 0.05)
# Order by adjusted p-value
resOrdered <- res[order(res$padj),]
# Order by log fold change
resOrdered <- res[order(abs(res$log2FoldChange), decreasing = TRUE),]
# Convert to data frame
res_df <- as.data.frame(res)
| Column | Description |
|---|---|
baseMean | Mean of normalized counts across all samples |
log2FoldChange | Log2 fold change (treatment vs control) |
lfcSE | Standard error of log2 fold change |
stat | Wald statistic |
pvalue | Raw p-value |
padj | Adjusted p-value (Benjamini-Hochberg) |
Goal: Obtain normalized expression values suitable for visualization and cross-sample comparison.
Approach: Extract size-factor-normalized counts or apply variance-stabilizing / rlog transformations.
# Get normalized counts
normalized_counts <- counts(dds, normalized = TRUE)
# Get size factors
sizeFactors(dds)
# Variance stabilizing transformation (for visualization)
vsd <- vst(dds, blind = FALSE)
# Regularized log transformation (alternative, slower)
rld <- rlog(dds, blind = FALSE)
Goal: Account for batch or other nuisance variables while testing the effect of interest.
Approach: Include batch as a covariate in the design formula so DESeq2 adjusts for it during testing.
# Design with batch correction
dds <- DESeqDataSetFromMatrix(countData = counts,
colData = coldata,
design = ~ batch + condition)
dds <- DESeq(dds)
# Extract condition effect (controlling for batch)
res <- results(dds, name = 'condition_treated_vs_control')
Goal: Identify genes whose response to treatment differs between genotypes (or other factor combinations).
Approach: Fit a model with interaction terms and test the interaction coefficient for significance.
# Interaction between genotype and treatment
dds <- DESeqDataSetFromMatrix(countData = counts,
colData = coldata,
design = ~ genotype + treatment + genotype:treatment)
dds <- DESeq(dds)
# Test interaction term
res_interaction <- results(dds, name = 'genotypeKO.treatmentdrug')
# Or use contrast for difference of differences
res_interaction <- results(dds, contrast = list(
c('genotypeKO.treatmentdrug'),
c()
))
Goal: Test whether a factor (e.g., condition) explains significant variance compared to a reduced model.
Approach: Compare full and reduced GLMs using a likelihood ratio test instead of Wald tests.
# Compare full vs reduced model
dds <- DESeq(dds, test = 'LRT', reduced = ~ batch)
# Results from LRT
res <- results(dds)
Goal: Remove uninformative genes to reduce multiple testing burden and improve statistical power.
Approach: Apply count-based filters requiring minimum expression across a threshold number of samples.
# Remove genes with low counts
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
# Keep genes with at least n counts in at least k samples
keep <- rowSums(counts(dds) >= 10) >= 3
dds <- dds[keep,]
# Filter by expression level
keep <- rowMeans(counts(dds, normalized = TRUE)) >= 10
dds <- dds[keep,]
# Update design formula
design(dds) <- ~ batch + condition
dds <- DESeq(dds)
# Subset samples
dds_subset <- dds[, dds$group == 'A']
# Subset genes
dds_genes <- dds[rownames(dds) %in% gene_list,]
Goal: Save DE results and normalized counts to files for sharing or downstream tools.
Approach: Convert results to data frames and write as CSV files.
# Write to CSV
write.csv(as.data.frame(resOrdered), file = 'deseq2_results.csv')
# Write normalized counts
write.csv(as.data.frame(normalized_counts), file = 'normalized_counts.csv')
| Error | Cause | Solution |
|---|---|---|
| "design matrix not full rank" | Confounded variables or missing levels | Check coldata for confounding |
| "counts matrix should be integers" | Non-integer counts (e.g., from tximport) | Use DESeqDataSetFromTximport() |
| "all samples have 0 counts" | Gene filtering issue | Check count matrix format |
| "factor levels not in colData" | Typo in design formula | Verify column names in coldata |
| Feature | Status | Alternative |
|---|---|---|
| No-replicate designs | Removed (v1.22) | Require biological replicates |
betaPrior = TRUE | Deprecated | Use lfcShrink() instead |
rlog() for large datasets | Not recommended | Use vst() for >100 samples |
# 1. Create DESeqDataSet
dds <- DESeqDataSetFromMatrix(counts, coldata, design = ~ condition)
# 2. Pre-filter
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
# 3. Set reference level
dds$condition <- relevel(dds$condition, ref = 'control')
# 4. Run DESeq2
dds <- DESeq(dds)
# 5. Get results with shrinkage
res <- lfcShrink(dds, coef = resultsNames(dds)[2], type = 'apeglm')
# 6. Filter significant genes
sig_genes <- subset(res, padj < 0.05 & abs(log2FoldChange) > 1)
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