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 counts, including ChIP-seq, Bisulfite-seq, SAGE and CAGE.

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 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
MaintainerYunshun Chen <[email protected]>, Aaron Lun <[email protected]>, Mark Robinson <[email protected]>, Davis McCarthy <[email protected]>, Gordon Smyth <[email protected]>
LicenseGPL (>=2)
Version3.24.1
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 Dec. 4, 2018, 6:01 p.m.