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\name{edgeR-package}
\docType{package}
\alias{edgeR}
\alias{edgeR-package}
\title{Empirical analysis of digital gene expression data in R}
\description{
edgeR is a package for the analysis of digital gene expression data arising from RNA sequencing technologies such as SAGE, CAGE, Tag-seq or RNA-seq, with emphasis on testing for differential expression.
It can also be used for other sequencing technologies from which read counts are produced, such as ChIP-seq, Hi-C or CRISPR.
Particular strengths of the package include the ability to estimate biological variation between replicate libraries, and to conduct exact tests of significance which are suitable for small counts.
The package is able to make use of even minimal numbers of replicates.
The supplied counts are assumed to be those of genes in a RNA-seq experiment.
However, counts can be supplied for any genomic feature of interest, e.g., tags, transcripts, exons, or even arbitrary intervals of the genome.
An extensive User's Guide is available, and can be opened by typing \code{edgeRUsersGuide()} at the R prompt.
Detailed help pages are also provided for each individual function.
The edgeR package implements original statistical methodology described in the publications below.
}
\author{
Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Lizhong Chen, Pedro Baldoni, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth
}
\references{
Chen Y, Chen L, Lun ATL, Baldoni PL, Smyth GK (2025).
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets.
\emph{Nucleic Acids Research} 53(2), gkaf018.
\doi{10.1093/nar/gkaf018}
Baldoni PL, Chen L, Smyth GK (2024).
Faster and more accurate assessment of differential transcript expression with Gibbs sampling and edgeR v4.
\emph{NAR Genomics and Bioinformatics} 6(4), lqae151.
\doi{10.1093/nargab/lqae151}
Baldoni PL#, Chen Y#, Hediyeh-zadeh S, Liao Y, Dong X, Ritchie ME, Shi W, Smyth GK (2024).
Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR.
\emph{Nucleic Acids Research} 52(3), e13.
\doi{10.1093/nar/gkad1167}
Chen Y, Pal B, Visvader JE, Smyth GK (2017).
Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR.
\emph{F1000Research} 6, 2055.
Lun, AT, Smyth GK (2017).
No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data.
\emph{Statistical Applications in Genetics and Molecular Biology} 16(2), 83-93.
Chen Y, Lun ATL, Smyth GK (2016).
From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline.
\emph{F1000Research} 5, 1438.
Lun ATL, Chen Y, Smyth GK (2016).
It's DE-licious: a recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR.
\emph{Methods in Molecular Biology} 1418, 391-416.
\url{https://gksmyth.github.io/pubs/QLedgeRPreprint.pdf}
Dai Z, Sheridan JM, Gearing LJ, Moore DL, Su S, Wormald S, Wilcox S, O'Connor L, Dickins RA, Blewitt ME, Ritchie ME (2014).
edgeR: a versatile tool for the analysis of shRNA-seq and CRISPR-Cas9 genetic screens.
\emph{F1000Research} 3, 95.
Chen Y, Lun ATL, Smyth GK (2014).
Differential expression analysis of complex RNA-seq experiments using edgeR.
In: \emph{Statistical Analysis of Next Generation Sequence Data},
Somnath Datta and Daniel S Nettleton (eds), Springer, New York, pages 51-74.
\url{https://gksmyth.github.io/pubs/edgeRChapterPreprint.pdf}
Zhou X, Lindsay H, Robinson MD (2014).
Robustly detecting differential expression in RNA sequencing data using observation weights.
\emph{Nucleic Acids Research} 42, e91.
Anders S, McCarthy DJ, Chen Y, Okoniewski M, Smyth GK, Huber W, Robinson MD (2013).
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor.
\emph{Nature Protocols} 8, 1765-1786.
McCarthy DJ#, Chen Y#, Smyth GK (2012).
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.
\emph{Nucleic Acids Research} 40, 4288-4297.
Robinson MD, Oshlack A (2010).
A scaling normalization method for differential expression analysis of RNA-seq data.
\emph{Genome Biology}, 11, R25.
Robinson MD, McCarthy DJ, Smyth GK (2010). edgeR: a Bioconductor
package for differential expression analysis of digital gene
expression data. \emph{Bioinformatics} 26, 139-140
Robinson MD, Smyth GK (2008).
Small-sample estimation of negative binomial dispersion, with applications to SAGE data.
\emph{Biostatistics}, 9, 321-332
Robinson MD, Smyth GK (2007).
Moderated statistical tests for assessing differences in tag abundance.
\emph{Bioinformatics} 23, 2881-2887
)}
\concept{Documentation}
|
