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DESeq (version 1.24.0)

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

1.24.0

License

GPL (>= 3)

Maintainer

Simon Anders

Last Published

February 15th, 2017

Functions in DESeq (1.24.0)

getBaseMeansAndVariances

Perform row-wise estimates of base-level means and variances for count data.
fpkm

FPKM: fragments per kilobase per million mapped fragments
rlog

Apply a 'regularized log' transformation
results

Extract results from a DESeq analysis
conditions

Accessor functions for the 'conditions' information in a CountDataSet object.
makeExampleCountDataSet

make a simple example CountDataSet with random data
estimateBetaPriorVar

Steps for estimating the beta prior variance
fitNbinomGLMs

Fit a generalized linear model (GLM) for each gene.
scvPlot

REMOVED
plotPCA

Sample PCA plot from variance-stabilized data
dispTable

Accessor function for the dispTable information in a CountDataSet
nbkd.sf

GLM family for a negative binomial with known dispersion and log link with size factors
residualsEcdfPlot

REMOVED
counts

Accessors for the 'counts' slot of a CountDataSet object.
dispersions

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

Make a simulated DESeqDataSet
normalizationFactors

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

Makes a so-called "MA-plot"
plotDispEsts

Plot dispersion estimates and fitted values
estimateSizeFactors

Estimate the size factors for a CountDataSet
fitInfo

Accessor function for the fitInfo objects in a CountDataSet
DESeq

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

DESeqResults object and constructor
estimateVarianceFunctions

REMOVED
fpm

FPM: fragments per million mapped fragments
DESeqDataSet-class

DESeqDataSet object and constructors
estimateDispersionsGeneEst

Low-level functions to fit dispersion estimates
plotCounts

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

Collapse technical replicates in a RangedSummarizedExperiment or DESeqDataSet
normalizeGeneLength

Normalize for gene length
fitNbinomGLMsForMatrix

Fit negative binomial GLMs to a count matrix.
varianceFitDiagnostics

REMOVED
sizeFactors

Accessor functions for the 'sizeFactors' information in a CountDataSet object.
getVarianceStabilizedData

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

Create a CountDataSet object
DESeq2-package

DESeq2 package for differential analysis of count data
nbinomWaldTest

Wald test for the GLM coefficients
nbinomTestForMatrices

Perform row-wise tests for differences between the base means of two count matrices.
nbinomLRT

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

Quickly estimate dispersion trend and apply a variance stabilizing transformation
coef

Extract a matrix of model coefficients/standard errors
estimateDispersions

Estimate and fit dispersions for a CountDataSet.
plotSparsity

Sparsity plot
show

Show method for DESeqResults objects
dispersionFunction

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

Summarize DESeq results
CountDataSet-class

Class "CountDataSet" -- a container for count data from HTS experiments
nbinomGLMTest

Perform chi-squared tests comparing two sets of GLM fits
estimateSizeFactorsForMatrix

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

Normalized counts transformation
design

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

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

Adjust an SCV value for the bias arising when it is calculated from unbiased estimates of mean and variance.
nbinomTest

Test for differences between the base means for two conditions
newCountDataSetFromHTSeqCount

Create a new CountDataSet from count files generated with htseq-count
replaceOutliers

Replace outliers with trimmed mean
DESeqTransform-class

DESeqTransform object and constructor