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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