edgeR (Empirical Analysis of Digital Gene Expression)

Overview

edgeR is an R/Bioconductor package for differential gene expression analysis of RNA-seq count data. It models read counts using a negative binomial distribution, estimating dispersion parameters empirically across genes using empirical Bayes shrinkage. edgeR supports the exact test (for simple two-group comparisons) and generalized linear models (glmLRT, glmQLFTest) for complex experimental designs. It is one of the two most widely used differential expression tools in cancer genomics (alongside DESeq2).

Used by

Used to perform differential gene expression analysis across transcriptome clusters in 28 metastatic neuroendocrine neoplasms (pog570_bcgsc_2020) using RSEM-quantified RNA-seq data; identified gene sets enriched in MYC-target programs in Cluster B NENs (high-grade/poorly differentiated), supporting master-regulator inference by VIPER PMID:24326773.
Used for differential gene expression analysis of RNA-seq data from AALE chr_3p-deleted vs. wild-type cells; identified 64% of 3p genes significantly down-regulated at early passage (FDR < 0.05) PMID:29622463

Notes

edgeR requires raw count data (not TPM or RPKM); RSEM expected counts should be rounded to integers before input.
TMM normalization (trimmed mean of M-values) is the default normalization method in edgeR.
For small sample sizes (n < 5 per group), the quasi-likelihood F-test (glmQLFTest) is preferred for more robust dispersion estimation.

Sources

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