bio-de-deseq2-basics

name	bio-de-deseq2-basics
description	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.
tool_type	r
primary_tool	DESeq2

DESeq2 Basics

Differential expression analysis using DESeq2 for RNA-seq count data.

Required Libraries

library(DESeq2)
library(apeglm)  # For lfcShrink with type='apeglm'

Installation

if (!require('BiocManager', quietly = TRUE))
    install.packages('BiocManager')
BiocManager::install('DESeq2')
BiocManager::install('apeglm')

Creating DESeqDataSet

From Count Matrix

# 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)

From SummarizedExperiment

library(SummarizedExperiment)
dds <- DESeqDataSet(se, design = ~ condition)

From tximport (Salmon/Kallisto)

library(tximport)
txi <- tximport(files, type = 'salmon', tx2gene = tx2gene)
dds <- DESeqDataSetFromTximport(txi, colData = coldata, design = ~ condition)

Standard DESeq2 Workflow

# 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')

Design Formulas

# 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

Specifying Contrasts

# 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'))

Log Fold Change Shrinkage

# 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')

Setting Significance Thresholds

# 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

Accessing DESeq2 Results

# 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)

Result Columns

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)

Normalization and Counts

# 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)

Multi-Factor Designs

# 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')

Interaction Models

# 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()
))

Likelihood Ratio Test

# Compare full vs reduced model
dds <- DESeq(dds, test = 'LRT', reduced = ~ batch)

# Results from LRT
res <- results(dds)

Pre-Filtering Strategies

# 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,]

Working with Existing Objects

# 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,]

Exporting Results

# 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')

Common Errors

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

Deprecated Features

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

Quick Reference: Workflow Steps

# 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 & abslog2FoldChange  

Related Skills

• edger-basics - Alternative DE analysis with edgeR
• de-visualization - MA plots, volcano plots, heatmaps
• de-results - Extract and export significant genes