| name | bioconductor-edger |
| description | Estimates differential gene expression for short read sequence count using methods appropriate for count data. If you have paired data you may also want to consider Tophat/Cufflinks. Input must be raw count data for each sequence arranged i |
| when_to_use | Use when: Bulk RNA-seq differential expression; ChIP-seq read count differential binding; ATAC-seq differential accessibility; Multi-group comparisons with contrasts; Complex experimental designs (factorial, nested, batch) |
| user-invocable | false |
edgeR — Comprehensive Skill Guide
Domain: RNA-seq / ChIP-seq / ATAC-seq
Bioconductor: edgeR
Paper: Robinson MD, McCarthy DJ, Smyth GK (2010). Bioinformatics, 26:139-140.
Empirical analysis of digital gene expression data. Handles small sample sizes well; supports a wide range of experimental designs via generalized linear models.
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 4.10.1 · Bioconductor: 3.23 · R: ≥ 4.6
- Depends: limma
- Imports: locfit
- Install:
BiocManager::install("edgeR")
When to Use
- Bulk RNA-seq differential expression
- ChIP-seq read count differential binding
- ATAC-seq differential accessibility
- Multi-group comparisons with contrasts
- Complex experimental designs (factorial, nested, batch)
Alternatives: DESeq2, limma-voom, NOISeq
Do NOT Use When
- Pre-normalized data (TPM, FPKM, RPKM) — requires raw counts.
- Continuous phenotype association — use linear regression or limma.
- Single-cell RNA-seq — use Seurat, scran, or muscat for pseudobulk.
- Proteomics/metabolomics unless data genuinely follows negative binomial.
Data Requirements
| Requirement | Details |
|---|
| Min Replicates | ≥2 per group; n=2 is supported but power is very low |
| Input Format | Raw integer count matrix (genes × samples) |
| Must Not Use | Normalized values; edgeR normalizes internally via TMM |
| Coverage | ≥5M aligned reads per sample typical for bulk RNA-seq |
Installation
if (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("edgeR")
library(edgeR)
Workflows
Standard GLM Workflow (Quasi-Likelihood)
Recommended for most RNA-seq experiments
Setup DGEList
library(edgeR)
d <- DGEList(counts=counts, group=group)
Filter and normalise
keep <- filterByExpr(d)
d <- d[keep,]
d <- calcNormFactors(d)
Design matrix and dispersion
design <- model.matrix(~group)
d <- estimateDisp(d, design, robust=TRUE)
plotBCV(d)
Fit and test
fit <- glmQLFit(d, design, robust=TRUE)
qlf <- glmQLFTest(fit, coef=2)
plotQLDisp(fit)
top <- topTags(qlf, n=Inf)$table
sig <- top[top$FDR < 0.05,]
Multi-group Contrasts
Pairwise comparisons between multiple groups
Build contrasts
group <- factor(c("A","A","B","B","C","C"))
design <- model.matrix(~0+group)
colnames(design) <- levels(group)
d <- estimateDisp(d, design)
fit <- glmQLFit(d, design)
contr <- makeContrasts(BvsA=B-A, CvsA=C-A, BvsC=B-C, levels=design)
res_BvsA <- glmQLFTest(fit, contrast=contr[,"BvsA"]
topTagsres_BvsA
Key Functions & Parameters
DGEList()
Container for count data
| Parameter | Description |
|---|
counts | matrix of raw counts (genes × samples) |
group | factor of sample groups |
lib.size | optional: library sizes (computed automatically if absent) |
norm.factors | optional: normalisation factors |
filterByExpr()
Automatic count filter — removes lowly expressed genes
| Parameter | Description |
|---|
y | DGEList |
group | factor (uses y$samples$group if absent) |
min.count | minimum count per sample (default 10) |
min.total.count | minimum total count (default 15) |
calcNormFactors()
Compute TMM normalisation factors
| Parameter | Description |
|---|
object | DGEList |
method | 'TMM' (default) | 'TMMwspll' | 'RLE' | 'upperquartile' | 'none' |
estimateDisp()
Estimate common and tagwise dispersions (BCV)
| Parameter | Description |
|---|
y | DGEList |
design | model matrix from model.matrix() |
robust | logical; robust estimation (default FALSE) |
trend.method | 'locfit' | 'none' | 'movingave' |
glmQLFit()
Fit quasi-likelihood negative binomial GLM (preferred for small n)
| Parameter | Description |
|---|
y | DGEList |
design | model matrix |
robust | logical; robustify prior df estimation |
glmQLFTest()
Quasi-likelihood F-test for coefficient(s)
| Parameter | Description |
|---|
glmfit | DGEGLM from glmQLFit |
coef | column index/name of design matrix to test |
contrast | contrast vector/matrix instead of coef |
topTags()
Extract top DE genes from test result
| Parameter | Description |
|---|
object | DGELRT or DGEExact |
n | number of genes (default 10; use Inf for all) |
adjust.method | 'BH' (default) | 'BY' | 'holm' | 'none' |
sort.by | 'PValue' | 'logFC' | 'none' |
Scientific Assumptions
The method assumes the following about your data and experiment:
- Counts are negative binomial distributed.
- Dispersion is shared across genes with similar expression (empirical Bayes shrinkage).
- Most genes are NOT differentially expressed (used in TMM normalization).
- The quasi-likelihood F-test (QLF) accounts for additional gene-level variation not captured by NB.
Result Interpretation
logFC: log2 fold-change; positive = higher in first group of contrast.PValue / FDR: BH-adjusted FDR; standard cutoff FDR < 0.05.logCPM: average log2-CPM (counts per million) — low logCPM → low-count gene, LFC less reliable.F statistic (QLF): larger F = more evidence for DE; compare across genes, not absolute value.- Use
plotBCV() to inspect biological coefficient of variation — unusually high BCV indicates outlier samples.
- Use
plotSmear() as an MA plot equivalent to check for systematic bias.
Best Practices
- Use
filterByExpr() for automatic gene filtering — it accounts for library size.
- TMM normalisation (
calcNormFactors) is the default and works well for most datasets.
- Use
glmQLFit + glmQLFTest for small sample sizes (n<10 per group).
- Use
glmFit + glmLRT for large datasets — slightly more powerful but less robust.
- Always provide a design matrix (
model.matrix()) for multi-factor experiments.
- For pairwise contrasts use
makeContrasts() — do not subset samples post-hoc.
- Plot
plotBCV(d) and plotQLDisp(fit) to check dispersion estimates.
- For ChIP-seq: use csaw windows instead of gene-level counts.
When to Choose This vs Alternatives
| Alternative | Prefer this tool when | Prefer alternative when |
|---|
DESeq2 | You need GLM quasi-likelihood tests; complex multifactor designs; very small n (n=2). | You prefer apeglm LFC shrinkage or want IHW for multiple testing. |
limma-voom | You have large datasets or complex contrasts that benefit from linear model flexibility. | You prefer moderated t-statistics and eBayes; microarray or large n (≥5/group). |
Benchmark Evidence
Key studies that have validated or benchmarked this tool:
-
Robinson MD, McCarthy DJ, Smyth GK (2010). Bioinformatics 26:139. PMID:19910308
Original edgeR paper; empirical Bayes dispersion estimation outperforms Poisson models.
-
Law CW et al. (2014). Genome Biol 15:R29. PMID:24926665
edgeR-QLF and limma-voom show best performance for RNA-seq DE across sample sizes.
Common Errors & Troubleshooting
Error in estimateDisp: no residual df
Cause: Saturated model — number of coefficients equals number of samples
Fix: Reduce model complexity or add replicates
dispersion values all negative
Cause: Very few samples; BCV estimate is unstable
Fix: Use estimateGLMCommonDisp with method='deviance'
From Community Q&A (Biostars)
Additional Notes from Official Documentation
Extracted from the edgeR Bioconductor vignette(s)
What is it?
edgeR is a package for differential analyses of read count data from sequencing technologies
such as RNA-seq, ChIP-seq, ATAC-seq, BS-seq and CUT&RUN.
It has particularly strong capabilities for expression analyses of RNA-seq data, including gene expression, transcript expression and tests for differential splicing.
edgeR implements novel statistical methods based on the negative binomial distribution
as a model for count variability, including empirical Bayes methods, exact tests, and generalized linear models.
The package is especially suitable for analysing designed experiments with multiple
experimental factors but possibly small numbers of replicates.
It has unique abilities to model transcript specific variation even in small samples,
a capability essential for prioritizing genes or
How to get help
The edgeR Userâs Guide is available by
or alternatively from the edgeR landing page .
Documentation for specific functions is available through the usual R help system, e.g., ?glmFit .
Further questions about the package should be directed to the Bioconductor support site .
> library(edgeR)
> edgeRUsersGuide()
Further reading
Chen Y, Chen L, Lun ATL, Baldoni PL, Smyth GK (2024). edgeR 4.0: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets. bioRxiv doi: 10.1101/2024.01.21.576131 .
Chen, Y, Pal, B, Visvader, JE, Smyth, GK (2017). Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR. F1000Research 6, 2055. doi:10.12688/f1000research.13196.2
Chen Y, Lun ATL, Smyth GK (2016). From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research 5, 1438. doi:10.12688/f1000research.8987.2
McCarthy, DJ, Chen, Y, Smyth, GK (2012). Differential expression analysis of multifactor RNA-Seq experiment
Resources
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