edgeR: Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE.

Package overview edgeR Vignette

Vignettes Man pages API and functions Files

Package details
Author	Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth
Bioconductor views	AlternativeSplicing BatchEffect Bayesian BiomedicalInformatics CellBiology ChIPSeq Clustering Coverage DNAMethylation DifferentialExpression DifferentialMethylation DifferentialSplicing Epigenetics FunctionalGenomics GeneExpression GeneSetEnrichment Genetics ImmunoOncology MultipleComparison Normalization Pathways QualityControl RNASeq Regression SAGE Sequencing SystemsBiology TimeCourse Transcription Transcriptomics
Maintainer	Yunshun Chen <yuchen@wehi.edu.au>, Gordon Smyth <smyth@wehi.edu.au>, Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>, Mark Robinson <mark.robinson@imls.uzh.ch>
License	GPL (>=2)
Version	3.32.1
URL	http://bioinf.wehi.edu.au/edgeR https://bioconductor.org/packages/edgeR
Package repository	View on Bioconductor
Installation	Install the latest version of this package by entering the following in R: `if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("edgeR")`