edgeR

Empirical analysis of digital gene expression data in R

Bioconductor version: 2.9

Differential expression analysis of RNA-seq and digital gene expression profiles with biological replication. Uses empirical Bayes estimation and exact tests based on the negative binomial distribution. Also useful for differential signal analysis with other types of genome-scale count data.

Author: Mark Robinson <mrobinson at wehi.edu.au>, Davis McCarthy <dmccarthy at wehi.edu.au>, Yunshun Chen <yuchen at wehi.edu.au>, Gordon Smyth <smyth at wehi.edu.au>

Maintainer: Mark Robinson <mrobinson at wehi.edu.au>, Davis McCarthy <dmccarthy at wehi.edu.au>, Yunshun Chen <yuchen at wehi.edu.au>, Gordon Smyth <smyth at wehi.edu.au>

To install this package, start R and enter:

    source("http://bioconductor.org/biocLite.R")
    biocLite("edgeR")

To cite this package in a publication, start R and enter:

    citation("edgeR")

Documentation

PDF R Script edgeR Vignette
PDF edgeRUsersGuide.pdf
PDF   Reference Manual
Text   NEWS

Details

biocViews Bioinformatics, DifferentialExpression, SAGE, HighThroughputSequencing, RNAseq
Depends R (>= 2.3.0), methods, limma
Imports
Suggests MASS, statmod, splines
System Requirements
License LGPL (>= 2)
URL
Depends On Me
Imports Me ArrayExpressHTS, DiffBind, Repitools, rnaSeqMap, tweeDEseq
Suggests Me baySeq, EDASeq, GenomicRanges, goseq, leeBamViews, oneChannelGUI, pasilla, Repitools
Version 2.4.6
Since Bioconductor 2.3 (R-2.8)

Package Downloads

Package Source edgeR_2.4.6.tar.gz
Windows Binary edgeR_2.4.6.zip (32- & 64-bit)
MacOS 10.5 (Leopard) binary edgeR_2.4.6.tgz
Package Downloads Report Download Stats

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