This package is considered a duplicate. The official version of this package is found at:https://bioc.r-universe.dev/DESeq2

Package: DESeq2 1.51.7

Michael Love

DESeq2: Differential gene expression analysis based on the negative binomial distribution

Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

Authors:Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Anqi Zhu [ctb], Nikolaos Ignatiadis [ctb], Raphael Rossellini [ctb], Kwame Forbes [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut, ctb], RADIANT EU FP7 [fnd], NIH NHGRI [fnd], CZI [fnd]

DESeq2_1.51.7.tar.gz
DESeq2_1.51.7.zip(r-4.7)DESeq2_1.51.7.zip(r-4.6)DESeq2_1.51.7.zip(r-4.5)
DESeq2_1.51.7.tgz(r-4.6-x86_64)DESeq2_1.51.7.tgz(r-4.6-arm64)DESeq2_1.51.7.tgz(r-4.5-x86_64)DESeq2_1.51.7.tgz(r-4.5-arm64)
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manual.pdf |manual.html✨
card.svg |card.png
DESeq2/json (API)
NEWS

# Install 'DESeq2' in R:

install.packages('DESeq2', repos = c('https://huber-group-embl.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/thelovelab/deseq2/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On BioConductor:DESeq2-1.53.0(bioc 3.24)DESeq2-1.52.0(bioc 3.23)

sequencing rnaseq chipseq geneexpression transcription normalization differentialexpression bayesian regression principalcomponent clustering immunooncology openblas cpp

14.00 score 459 stars 123 packages 25k scripts 57 exports 45 dependencies

Last updated from:15f2ec90e8. Checks:11 NOTE, 2 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	NOTE	457
linux-devel-x86_64	NOTE	498
source / vignettes	OK	461
linux-release-arm64	NOTE	664
linux-release-x86_64	NOTE	496
macos-release-arm64	NOTE	450
macos-release-x86_64	NOTE	838
macos-oldrel-arm64	NOTE	419
macos-oldrel-x86_64	NOTE	782
windows-devel	NOTE	564
windows-release	NOTE	521
windows-oldrel	NOTE	451
wasm-release	OK	230

Exports:collapseReplicates counts counts<-DESeq DESeqDataSet DESeqDataSetFromHTSeqCount DESeqDataSetFromMatrix DESeqDataSetFromTximport DESeqResults DESeqTransform design design<-dispersionFunction dispersionFunction<-dispersions dispersions<-estimateBetaPriorVar estimateDispersions estimateDispersionsFit estimateDispersionsGeneEst estimateDispersionsMAP estimateDispersionsPriorVar estimateMLEForBetaPriorVar estimateSizeFactors estimateSizeFactorsForMatrix fpkm fpm getVarianceStabilizedData lfcShrink makeExampleDESeqDataSet nbinomLRT nbinomWaldTest normalizationFactors normalizationFactors<-normalizeGeneLength normTransform plotCounts plotDispEsts plotMA plotPCA plotSparsity priorInfo priorInfo<-removeResults replaceOutliers replaceOutliersWithTrimmedMean results resultsNames rlog rlogTransformation show sizeFactors sizeFactors<-summary unmix varianceStabilizingTransformation vst

Dependencies:abind BH Biobase BiocGenerics BiocParallel cli codetools cpp11 DelayedArray farver formatR futile.logger futile.options generics GenomicRanges ggplot2 glue gtable IRanges isoband labeling lambda.r lattice lifecycle locfit Matrix MatrixGenerics matrixStats R6 RColorBrewer Rcpp RcppArmadillo rlang S4Arrays S4Vectors S7 scales Seqinfo snow SparseArray SummarizedExperiment vctrs viridisLite withr XVector

Analyzing RNA-seq data with DESeq2

Michael I. Love, Simon Anders, and Wolfgang Huber

Rendered fromDESeq2.Rmdusingknitr::rmarkdownon May 19 2026.

Last update: 2025-11-18
Started: 2016-11-18

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
DESeq2 package for differential analysis of count data	DESeq2-package DESeq2
Extract a matrix of model coefficients/standard errors	coef.DESeqDataSet
Collapse technical replicates in a RangedSummarizedExperiment or DESeqDataSet	collapseReplicates
Accessors for the 'counts' slot of a DESeqDataSet object.	counts,DESeqDataSet-method counts<-,DESeqDataSet,matrix-method
Differential expression analysis based on the Negative Binomial (a.k.a. Gamma-Poisson) distribution	DESeq
DESeqDataSet object and constructors	DESeqDataSet DESeqDataSet-class DESeqDataSetFromHTSeqCount DESeqDataSetFromMatrix DESeqDataSetFromTximport
DESeqResults object and constructor	DESeqResults DESeqResults-class
DESeqTransform object and constructor	DESeqTransform DESeqTransform-class
Accessors for the 'design' slot of a DESeqDataSet object.	design,DESeqDataSet-method design<-,DESeqDataSet,formula-method design<-,DESeqDataSet,matrix-method
Accessors for the 'dispersionFunction' slot of a DESeqDataSet object.	dispersionFunction dispersionFunction,DESeqDataSet-method dispersionFunction<- dispersionFunction<-,DESeqDataSet,function-method
Accessor functions for the dispersion estimates in a DESeqDataSet object.	dispersions dispersions,DESeqDataSet-method dispersions<- dispersions<-,DESeqDataSet,numeric-method
Steps for estimating the beta prior variance	estimateBetaPriorVar estimateMLEForBetaPriorVar
Estimate the dispersions for a DESeqDataSet	estimateDispersions,DESeqDataSet-method
Low-level functions to fit dispersion estimates	estimateDispersionsFit estimateDispersionsGeneEst estimateDispersionsMAP estimateDispersionsPriorVar
Estimate the size factors for a 'DESeqDataSet'	estimateSizeFactors,DESeqDataSet-method
Low-level function to estimate size factors with robust regression.	estimateSizeFactorsForMatrix
FPKM: fragments per kilobase per million mapped fragments	fpkm
FPM: fragments per million mapped fragments	fpm
Shrink log2 fold changes	lfcShrink
Make a simulated DESeqDataSet	makeExampleDESeqDataSet
Likelihood ratio test (chi-squared test) for GLMs	nbinomLRT
Wald test for the GLM coefficients	nbinomWaldTest
Accessor functions for the normalization factors in a DESeqDataSet object.	normalizationFactors normalizationFactors,DESeqDataSet-method normalizationFactors<- normalizationFactors<-,DESeqDataSet,matrix-method
Normalize for gene length	normalizeGeneLength
Normalized counts transformation	normTransform
Plot of normalized counts for a single gene	plotCounts
Plot dispersion estimates	plotDispEsts plotDispEsts,DESeqDataSet-method
MA-plot from base means and log fold changes	plotMA plotMA,DESeqDataSet-method plotMA,DESeqResults-method
Sample PCA plot for transformed data	plotPCA plotPCA,DESeqTransform-method
Sparsity plot	plotSparsity
Accessors for the 'priorInfo' slot of a DESeqResults object.	priorInfo priorInfo,DESeqResults-method priorInfo<- priorInfo<-,DESeqResults,list-method
Replace outliers with trimmed mean	replaceOutliers replaceOutliersWithTrimmedMean
Extract results from a DESeq analysis	removeResults results resultsNames
Apply a 'regularized log' transformation	rlog rlogTransformation
Show method for DESeqResults objects	show,DESeqResults-method
Accessor functions for the 'sizeFactors' information in a DESeqDataSet object.	sizeFactors,DESeqDataSet-method sizeFactors<-,DESeqDataSet,numeric-method
Summarize DESeq results	summary,DESeqResults-method
Unmix samples using loss in a variance stabilized space	unmix
Apply a variance stabilizing transformation (VST) to the count data	getVarianceStabilizedData varianceStabilizingTransformation
Quickly estimate dispersion trend and apply a variance stabilizing transformation	vst