edgeR : Empirical Analysis of Digital Gene Expression Data in R

Package: edgeR
Version: 3.14.0
Date: 2016-04-19
Title: Empirical Analysis of Digital Gene Expression Data in R
Description: 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.
Author: Yunshun 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>
Maintainer: Yunshun 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>
License: GPL (>=2)
Depends: R (>= 2.15.0), limma
Imports: graphics, stats, utils, methods
Suggests: MASS, statmod, splines, locfit, KernSmooth
URL: http://bioinf.wehi.edu.au/edgeR
biocViews: GeneExpression, Transcription, AlternativeSplicing,
Coverage, DifferentialExpression, DifferentialSplicing,
GeneSetEnrichment, Genetics, Bayesian, Clustering, Regression,
TimeCourse, SAGE, Sequencing, ChIPSeq, RNASeq, BatchEffect,
MultipleComparison, Normalization, QualityControl
NeedsCompilation: yes
Packaged: 2016-05-04 03:09:43 UTC; biocbuild