Package: edgeR
Version: 4.1.2
Date: 2023-11-19
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 read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE.
Author: Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Lizhong Chen, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth
Maintainer: Yunshun Chen <>, Gordon Smyth <>, Aaron Lun <>, Mark Robinson <>
License: GPL (>=2)
Depends: R (>= 3.6.0), limma (>= 3.41.5)
Imports: methods, graphics, stats, utils, locfit, Rcpp
Suggests: jsonlite, readr, rhdf5, splines, knitr, AnnotationDbi, Biobase, BiocStyle, SummarizedExperiment,, Matrix, SeuratObject
LinkingTo: Rcpp
VignetteBuilder: knitr
biocViews: GeneExpression, Transcription, AlternativeSplicing,
        Coverage, DifferentialExpression, DifferentialSplicing,
        DifferentialMethylation, GeneSetEnrichment, Pathways,
        Genetics, DNAMethylation, Bayesian, Clustering, ChIPSeq,
        Regression, TimeCourse, Sequencing, RNASeq, BatchEffect, 
        SAGE, Normalization, QualityControl, MultipleComparison,
        BiomedicalInformatics, CellBiology, FunctionalGenomics, 
        Epigenetics, Genetics, ImmunoOncology, SystemsBiology, 
        Transcriptomics, SingleCell
NeedsCompilation: yes