edgeR: Empirical Analysis of Digital Gene Expression Data in R
Version 3.18.1

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.

Package details

AuthorYunshun Chen <yuchen@wehi.edu.au>, Aaron Lun <alun@wehi.edu.au>, Davis McCarthy <dmccarthy@wehi.edu.au>, Xiaobei Zhou <xiaobei.zhou@uzh.ch>, Mark Robinson <mark.robinson@imls.uzh.ch>, Gordon Smyth <smyth@wehi.edu.au>
Bioconductor views AlternativeSplicing BatchEffect Bayesian ChIPSeq Clustering Coverage DifferentialExpression DifferentialSplicing GeneExpression GeneSetEnrichment Genetics MultipleComparison Normalization QualityControl RNASeq Regression SAGE Sequencing TimeCourse Transcription
MaintainerYunshun Chen <yuchen@wehi.edu.au>, Aaron Lun <alun@wehi.edu.au>, Mark Robinson <mark.robinson@imls.uzh.ch>, Davis McCarthy <dmccarthy@wehi.edu.au>, Gordon Smyth <smyth@wehi.edu.au>
LicenseGPL (>=2)
Version3.18.1
URL http://bioinf.wehi.edu.au/edgeR
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("edgeR")

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edgeR documentation built on May 31, 2017, 11:02 a.m.