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Empirical analysis of digital gene expression data in R

Bioconductor version: 3.0

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: Yunshun Chen <yuchen at>, Davis McCarthy <dmccarthy at>, Aaron Lun <alun at>, Xiaobei Zhou <xiaobei.zhou at>, Mark Robinson <mark.robinson at>, Gordon Smyth <smyth at>

Maintainer: Yunshun Chen <yuchen at>, Aaron Lun <alun at>, Mark Robinson <mark.robinson at>, Davis McCarthy <dmccarthy at>, Gordon Smyth <smyth at>

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biocViews AlternativeSplicing, BatchEffect, Bayesian, ChIPSeq, Clustering, Coverage, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneSetEnrichment, Genetics, MultipleComparison, Normalization, QualityControl, RNASeq, Regression, SAGE, Sequencing, Software, TimeCourse, Transcription
Version 3.8.6
In Bioconductor since BioC 2.3 (R-2.8)
License GPL (>=2)
Depends R (>= 2.15.0), limma
Imports methods
Suggests MASS, statmod, splines, locfit, KernSmooth
Depends On Me DBChIP, EDDA, manta, methylMnM, MLSeq, RUVSeq, TCC, tRanslatome
Imports Me ampliQueso, ArrayExpressHTS, compcodeR, csaw, DEGreport, DiffBind, DSS, easyRNASeq, EDDA, erccdashboard, HTSFilter, MEDIPS, metaseqR, msmsTests, Repitools, ReportingTools, rnaSeqMap, systemPipeR, ToPASeq, tweeDEseq
Suggests Me baySeq, BitSeq, ClassifyR, clonotypeR, cqn, EDASeq, gage, GenomicAlignments, GenomicRanges, goseq, groHMM, GSAR, GSVA, leeBamViews, missMethyl, oneChannelGUI, pasilla, SSPA
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