edgeR Basics

Differential expression analysis using edgeR's quasi-likelihood framework for RNA-seq count data.

Required Libraries

library(edgeR)
library(limma)  # For design matrices and voom

Installation

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

Creating DGEList Object

# From count matrix
# counts: matrix with genes as rows, samples as columns
# group: factor indicating sample groups
y <- DGEList(counts = counts, group = group)

# With gene annotation
y <- DGEList(counts = counts, group = group, genes = gene_info)

# Check structure
y

Standard edgeR Workflow (Quasi-Likelihood)

# Create DGEList
y <- DGEList(counts = counts, group = group)

# Filter low-expression genes
keep <- filterByExpr(y, group = group)
y <- y[keep, , keep.lib.sizes = FALSE]

# Normalize (TMM by default)
y <- calcNormFactors(y)

# Create design matrix
design <- model.matrix(~ group)

# Estimate dispersion (optional in edgeR v4+ but improves BCV plots)
y <- estimateDisp(y, design)

# Fit quasi-likelihood model
fit <- glmQLFit(y, design)

# Perform quasi-likelihood F-test
qlf <- glmQLFTest(fit, coef = 2)

# View top genes
topTags(qlf)

Filtering Low-Expression Genes

# Automatic filtering (recommended)
keep <- filterByExpr(y, group = group)
y <- y[keep, , keep.lib.sizes = FALSE]

# Manual filtering: CPM threshold
keep <- rowSums(cpm(y) > 1) >= 2  # At least 2 samples with CPM > 1
y <- y[keep, , keep.lib.sizes = FALSE]

# Filter by minimum counts
keep <- rowSums(y$counts >= 10) >= 3  # At least 3 samples with 10+ counts
y <- y[keep, , keep.lib.sizes = FALSE]

Normalization Methods

# TMM normalization (default, recommended)
y <- calcNormFactors(y, method = 'TMM')

# Alternative methods
y <- calcNormFactors(y, method = 'RLE')      # Relative Log Expression
y <- calcNormFactors(y, method = 'upperquartile')
y <- calcNormFactors(y, method = 'none')     # No normalization

# View normalization factors
y$samples$norm.factors

Design Matrices

# Simple two-group comparison
design <- model.matrix(~ group)

# With batch correction
design <- model.matrix(~ batch + group)

# Interaction model
design <- model.matrix(~ genotype + treatment + genotype:treatment)

# No intercept (for direct group comparisons)
design <- model.matrix(~ 0 + group)
colnames(design) <- levels(group)

Dispersion Estimation

# Estimate all dispersions
y <- estimateDisp(y, design)

# Or estimate separately
y <- estimateGLMCommonDisp(y, design)
y <- estimateGLMTrendedDisp(y, design)
y <- estimateGLMTagwiseDisp(y, design)

# View dispersions
y$common.dispersion
y$trended.dispersion
y$tagwise.dispersion

# Plot BCV (biological coefficient of variation)
plotBCV(y)

Quasi-Likelihood Testing

# Fit QL model
fit <- glmQLFit(y, design)

# Test specific coefficient
qlf <- glmQLFTest(fit, coef = 2)

# Test with contrast
contrast <- makeContrasts(groupB - groupA, levels = design)
qlf <- glmQLFTest(fit, contrast = contrast)

# Test multiple coefficients (ANOVA-like)
qlf <- glmQLFTest(fit, coef = 2:3)

Making Contrasts

# Design without intercept
design <- model.matrix(~ 0 + group)
colnames(design) <- levels(group)
y <- estimateDisp(y, design)
fit <- glmQLFit(y, design)

# Pairwise comparisons
contrast <- makeContrasts(
    TreatedVsControl = treated - control,
    DrugAVsControl = drugA - control,
    DrugBVsControl = drugB - control,
    DrugAVsDrugB = drugA - drugB,
    levels = design
)

# Test each contrast
qlf_treated <- glmQLFTest(fit, contrast = contrast[, 'TreatedVsControl'])
qlf_drugA <- glmQLFTest(fit, contrast = contrast[, 'DrugAVsControl'])

Accessing Results

# Top differentially expressed genes
topTags(qlf, n = 20)

# All results as data frame
results <- topTags(qlf, n = Inf)$table

# Summary of DE genes at different thresholds
summary(decideTests(qlf))

# Get DE genes with specific cutoffs
de_genes <- topTags(qlf, n = Inf, p.value = 0.05)$table

Result Columns

logFC

logCPM

F

PValue

FDR

Alternative: Exact Test (Classic edgeR)

# For simple two-group comparison only
y <- DGEList(counts = counts, group = group)
y <- calcNormFactors(y)
y <- estimateDisp(y)

# Exact test
et <- exactTest(y)
topTags(et)

Alternative: glmLRT (Likelihood Ratio Test)

# Fit GLM
fit <- glmFit(y, design)

# Likelihood ratio test
lrt <- glmLRT(fit, coef = 2)
topTags(lrt)

Treat Test (Log Fold Change Threshold)

# Test for |logFC| > threshold
tr <- glmTreat(fit, coef = 2, lfc = log2(1.5))  # |FC| > 1.5
topTags(tr)

Multi-Factor Designs

# Design with batch correction
design <- model.matrix(~ batch + condition, data = sample_info)
y <- estimateDisp(y, design)
fit <- glmQLFit(y, design)

# Test condition effect (controlling for batch)
# Condition coefficient is typically the last
qlf <- glmQLFTest(fit, coef = ncol(design))

Getting Normalized Counts

# Counts per million (CPM)
cpm_values <- cpm(y)

# Log2 CPM
log_cpm <- cpm(y, log = TRUE)

# RPM (reads per million, same as CPM)
rpm_values <- cpm(y)

# With prior count for log transformation
log_cpm <- cpm(y, log = TRUE, prior.count = 2)

Exporting Results

# Get all results
all_results <- topTags(qlf, n = Inf)$table

# Add gene IDs as column
all_results$gene_id <- rownames(all_results)

# Write to file
write.csv(all_results, file = 'edger_results.csv', row.names = FALSE)

# Export normalized counts
write.csv(cpm(y), file = 'cpm_values.csv')

Common Errors

Deprecated/Changed Functions

decidetestsDGE()

decideTests()

glmFit()

glmLRT()

glmQLFit()

glmQLFTest()

estimateDisp()

glmQLFit()

mglmLS()

mglmSimple()

mglmLevenberg()

mglmOneWay()

Note:

calcNormFactors()

normLibSizes()

Quick Reference: Workflow Steps

# 1. Create DGEList
y <- DGEList(counts = counts, group = group)

# 2. Filter low-expression genes
keep <- filterByExpr(y, group = group)
y <- y[keep, , keep.lib.sizes = FALSE]

# 3. Normalize
y <- calcNormFactors(y)

# 4. Create design matrix
design <- model.matrix(~ group)

# 5. Estimate dispersion (optional in v4+)
y <- estimateDisp(y, design)

# 6. Fit quasi-likelihood model
fit <- glmQLFit(y, design)

# 7. Test for DE
qlf <- glmQLFTest(fit, coef = 2)

# 8. Get results
topTags(qlf, n = 20)

Choosing edgeR vs DESeq2

Related Skills

• deseq2-basics - Alternative DE analysis with DESeq2
• de-visualization - MA plots, volcano plots, heatmaps
• de-results - Extract and export significant genes