Perform row-wise estimates of base-level means and variances for count data.
FPKM: fragments per kilobase per million mapped fragments
Apply a 'regularized log' transformation
Extract results from a DESeq analysis
Accessor functions for the 'conditions' information in a
CountDataSet object.
make a simple example CountDataSet with random data
Steps for estimating the beta prior variance
Fit a generalized linear model (GLM) for each gene.
REMOVED
Sample PCA plot from variance-stabilized data
Accessor function for the dispTable information in a CountDataSet
GLM family for a negative binomial with known dispersion and log link with size factors
REMOVED
Accessors for the 'counts' slot of a CountDataSet object.
Accessor functions for the dispersion estimates in a DESeqDataSet
object.
Make a simulated DESeqDataSet
Accessor functions for the normalization factors in a DESeqDataSet
object.
Makes a so-called "MA-plot"
Plot dispersion estimates and fitted values
Estimate the size factors for a CountDataSet
Accessor function for the fitInfo objects in a CountDataSet
Differential expression analysis based on the Negative Binomial (a.k.a. Gamma-Poisson) distribution
DESeqResults object and constructor
estimateVarianceFunctions
REMOVED
FPM: fragments per million mapped fragments
DESeqDataSet object and constructors
estimateDispersionsGeneEst
Low-level functions to fit dispersion estimates
Plot of normalized counts for a single gene on log scale
Collapse technical replicates in a RangedSummarizedExperiment or DESeqDataSet
Normalize for gene length
Fit negative binomial GLMs to a count matrix.
REMOVED
Accessor functions for the 'sizeFactors' information in a
CountDataSet object.
getVarianceStabilizedData
Apply a variance stabilizing transformation (VST) to the count data
Create a CountDataSet object
DESeq2 package for differential analysis of count data
Wald test for the GLM coefficients
Perform row-wise tests for differences between the base means of two count matrices.
Likelihood ratio test (chi-squared test) for GLMs
Quickly estimate dispersion trend and apply a variance stabilizing transformation
Extract a matrix of model coefficients/standard errors
Estimate and fit dispersions for a CountDataSet.
Sparsity plot
Show method for DESeqResults objects
Accessors for the 'dispersionFunction' slot of a DESeqDataSet object.
Summarize DESeq results
Class "CountDataSet" -- a container for count data from HTS experiments
Perform chi-squared tests comparing two sets of GLM fits
estimateSizeFactorsForMatrix
Low-level function to estimate size factors with robust regression.
Normalized counts transformation
Accessors for the 'design' slot of a DESeqDataSet object.
varianceStabilizingTransformation
Apply a variance stabilizing transformation (VST) to the count data
Adjust an SCV value for the bias arising when it is calculated from
unbiased estimates of mean and variance.
Test for differences between the base means for two conditions
newCountDataSetFromHTSeqCount
Create a new CountDataSet from count files generated with htseq-count
Replace outliers with trimmed mean
DESeqTransform object and constructor