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DESeq2 (version 1.10.0)

Differential gene expression analysis based on the negative binomial distribution

Description

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.

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Version

Version

1.10.0

License

LGPL (>= 3)

Maintainer

Michael Love

Last Published

February 15th, 2017

Functions in DESeq2 (1.10.0)

DESeqResults-class

DESeqResults object and constructor
estimateBetaPriorVar

Steps for estimating the beta prior variance
dispersions

Accessor functions for the dispersion estimates in a DESeqDataSet object.
plotSparsity

Sparsity plot
DESeqTransform-class

DESeqTransform object and constructor
plotPCA

Sample PCA plot for transformed data
collapseReplicates

Collapse technical replicates in a RangedSummarizedExperiment or DESeqDataSet
coef

Extract a matrix of model coefficients/standard errors
estimateDispersionsGeneEst

Low-level functions to fit dispersion estimates
estimateSizeFactors

Estimate the size factors for a DESeqDataSet
replaceOutliers

Replace outliers with trimmed mean
results

Extract results from a DESeq analysis
sizeFactors

Accessor functions for the 'sizeFactors' information in a DESeqDataSet object.
summary

Summarize DESeq results
DESeq2-package

DESeq2 package for differential analysis of count data
DESeqDataSet-class

DESeqDataSet object and constructors
nbinomLRT

Likelihood ratio test (chi-squared test) for GLMs
nbinomWaldTest

Wald test for the GLM coefficients
normalizationFactors

Accessor functions for the normalization factors in a DESeqDataSet object.
normalizeGeneLength

Normalize for gene length
design

Accessors for the 'design' slot of a DESeqDataSet object.
dispersionFunction

Accessors for the 'dispersionFunction' slot of a DESeqDataSet object.
plotCounts

Plot of normalized counts for a single gene on log scale
normTransform

Normalized counts transformation
estimateSizeFactorsForMatrix

Low-level function to estimate size factors with robust regression.
counts

Accessors for the 'counts' slot of a DESeqDataSet object.
fpkm

FPKM: fragments per kilobase per million mapped fragments
varianceStabilizingTransformation

Apply a variance stabilizing transformation (VST) to the count data
DESeq

Differential expression analysis based on the Negative Binomial (a.k.a. Gamma-Poisson) distribution
makeExampleDESeqDataSet

Make a simulated DESeqDataSet
fpm

FPM: fragments per million mapped fragments
plotDispEsts

Plot dispersion estimates
plotMA

MA-plot from base means and log fold changes
rlog

Apply a 'regularized log' transformation
show

Show method for DESeqResults objects