edgeR
This is the development version of edgeR; for the stable release version, see edgeR.
Empirical Analysis of Digital Gene Expression Data in R
Bioconductor version: Development (3.24)
Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.
Author: Yunshun Chen, Lizhong Chen, Aaron TL Lun, Davis J McCarthy, Pedro Baldoni, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth
Maintainer: Yunshun Chen <yuchen at wehi.edu.au>, Gordon Smyth <smyth at wehi.edu.au>, Aaron Lun <infinite.monkeys.with.keyboards at gmail.com>, Mark Robinson <mark.robinson at imls.uzh.ch>
citation("edgeR")):
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M (2015). "Orchestrating high-throughput genomic analysis with Bioconductor." Nature Methods, 12(2), 115–121. doi:10.1038/nmeth.3252.
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J (2004). "Bioconductor: open software development for computational biology and bioinformatics." Genome Biology, 5(10), R80. doi:10.1186/gb-2004-5-10-r80.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## The following initializes the development version of Bioconductor
BiocManager::install(version = "devel")
BiocManager::install("edgeR")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("edgeR")| A brief introduction to edgeR | HTML | R Script |
| edgeR User's Guide | ||
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | AlternativeSplicing, BatchEffect, Bayesian, BiomedicalInformatics, CellBiology, ChIPSeq, Clustering, Coverage, DNAMethylation, DifferentialExpression, DifferentialMethylation, DifferentialSplicing, Epigenetics, FunctionalGenomics, GeneExpression, GeneSetEnrichment, Genetics, ImmunoOncology, MultipleComparison, Normalization, Pathways, Proteomics, QualityControl, RNASeq, Regression, SAGE, Sequencing, SingleCell, Software, SystemsBiology, TimeCourse, Transcription, Transcriptomics |
| Version | 4.11.4 |
| In Bioconductor since | BioC 2.3 (R-2.8) (18 years) |
| License | GPL (>=2) |
| Depends | R (>= 3.6.0), limma(>= 3.63.6) |
| Imports | methods, graphics, stats, utils, locfit |
| System Requirements | |
| URL | https://bioinf.wehi.edu.au/edgeR/ https://bioconductor.org/packages/edgeR |
See More
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | edgeR_4.11.4.tar.gz |
| Windows Binary (x86_64) | edgeR_4.11.4.zip |
| macOS Binary (big-sur-x86_64) | edgeR_4.11.4.tgz |
| macOS Binary (sonoma-arm64) | edgeR_4.11.4.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/edgeR |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/edgeR |
| Bioc Package Browser | https://code.bioconductor.org/browse/edgeR/ |
| Package Short Url | https://bioconductor.org/packages/edgeR/ |
| Package Downloads Report | Download Stats |