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 ChIPSeq Clustering Coverage DNAMethylation DifferentialExpression DifferentialMethylation DifferentialSplicing GeneExpression GeneSetEnrichment Genetics MultipleComparison Normalization Pathways 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)
URL http://bioinf.wehi.edu.au/edgeR
Package repositoryView on Bioconductor
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edgeR documentation built on Nov. 1, 2018, 4:13 a.m.