DESeq2 package for differential analysis of count data
Normalized counts transformation
Steps for estimating the beta prior variance
Sample PCA plot for transformed data
Plot of normalized counts for a single gene on log scale
FPM: fragments per million mapped fragments
Replace outliers with trimmed mean
estimateDispersionsGeneEst
Low-level functions to fit dispersion estimates
Sparsity plot
DESeqTransform object and constructor
Make a simulated DESeqDataSet
Likelihood ratio test (chi-squared test) for GLMs
Accessor functions for the dispersion estimates in a DESeqDataSet
object.
Accessors for the 'counts' slot of a DESeqDataSet object.
Estimate the size factors for a DESeqDataSet Summarize DESeq results
Collapse technical replicates in a RangedSummarizedExperiment or DESeqDataSet
DESeqResults object and constructor
Normalize for gene length
Estimate the dispersions for a DESeqDataSet
Plot dispersion estimates
Extract results from a DESeq analysis
Show method for DESeqResults objects
Accessor functions for the 'sizeFactors' information in a DESeqDataSet
object.
estimateSizeFactorsForMatrix
Low-level function to estimate size factors with robust regression.
Accessors for the 'design' slot of a DESeqDataSet object.
Differential expression analysis based on the Negative Binomial (a.k.a. Gamma-Poisson) distribution
FPKM: fragments per kilobase per million mapped fragments
Apply a 'regularized log' transformation
Accessor functions for the normalization factors in a DESeqDataSet
object.
Quickly estimate dispersion trend and apply a variance stabilizing transformation
Extract a matrix of model coefficients/standard errors
DESeqDataSet object and constructors
Accessors for the 'dispersionFunction' slot of a DESeqDataSet object.
MA-plot from base means and log fold changes
Wald test for the GLM coefficients
varianceStabilizingTransformation
Apply a variance stabilizing transformation (VST) to the count data