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>
citation("edgeR")):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. edgeR: Empirical Analysis of Digital Gene Expression Data in R. doi:10.18129/B9.bioc.edgeR, R package version 4.99.6, https://bioconductor.org/packages/edgeR.
Generated from the package metadata; it may differ from the package's own citation.
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")| edgeR User's Guide | ||
| A brief introduction to edgeR | HTML | R Script |
| 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 | 4.99.6 |
| 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_4.99.6.tar.gz |
| Windows Binary (x86_64) | edgeR_4.99.6.zip |
| macOS Binary (big-sur-x86_64) | edgeR_4.99.6.tgz |
| macOS Binary (sonoma-arm64) | edgeR_4.99.6.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/edgeR |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/edgeR |
| Package Short Url | https://bioconductor.org/packages/edgeR/ |
| Package Downloads Report | Download Stats |