Bioconductor-mirror/edgeR: Empirical Analysis of Digital Gene Expression Data in R
Version 3.19.3

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 counts, including ChIP-seq, SAGE and CAGE.

Getting started

Package details

AuthorYunshun Chen <[email protected]>, Aaron Lun <[email protected]>, Davis McCarthy <[email protected]>, Xiaobei Zhou <[email protected]>, Mark Robinson <[email protected]>, Gordon Smyth <[email protected]>
Bioconductor views AlternativeSplicing BatchEffect Bayesian ChIPSeq Clustering Coverage DifferentialExpression DifferentialSplicing GeneExpression GeneSetEnrichment Genetics MultipleComparison Normalization QualityControl RNASeq Regression SAGE Sequencing TimeCourse Transcription
MaintainerYunshun Chen <[email protected]>, Aaron Lun <[email protected]>, Mark Robinson <[email protected]>, Davis McCarthy <[email protected]>, Gordon Smyth <[email protected]>
LicenseGPL (>=2)
Version3.19.3
URL http://bioinf.wehi.edu.au/edgeR
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("Bioconductor-mirror/edgeR")
Bioconductor-mirror/edgeR documentation built on July 1, 2017, 6:38 a.m.