getVarianceStabilizedData {DESeq} | R Documentation

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

### Description

This function calculates a variance stabilizing transformation (VST) from the fitted dispersion-mean relation(s) and then transforms the count data (normalized by division by the size factor), yielding a matrix of values which are now approximately homoskedastic. This is useful as input to statistical analyses requiring homoskedasticity.

### Usage

    varianceStabilizingTransformation(cds)
    getVarianceStabilizedData(cds)

### Arguments

`cds` | a `CountDataSet` which also contains the fitted dispersion-mean relation

### Details

For each sample (i.e., column of `counts(cds)`), the full variance function is calculated from the raw variance (by scaling according to the size factor and adding the shot noise). The function requires a blind estimate of the variance function, i.e., one ignoring conditions. Usually, this is achieved by calling `estimateDispersions` with `method="blind"` before calling it. A typical workflow is shown in Section Variance stabilizing transformation in the package vignette.

If `estimateDispersions` was called with `fitType="parametric"`, a closed-form expression for the variance stabilizing transformation is used on the normalized count data. The expression can be found in the file ‘vst.pdf’ which is distributed with the vignette.

If `estimateDispersions` was called with `fitType="locfit"`, the reciprocal of the square root of the variance of the normalized counts, as derived from the dispersion fit, is then numerically integrated, and the integral (approximated by a spline function) is evaluated for each count value in the column, yielding a transformed value.

In both cases, the transformation is scaled such that for large counts, it becomes asymptotically (for large values) equal to the logarithm to base 2.

Limitations: In order to preserve normalization, the same transformation has to be used for all samples. This results in the variance stabilizition to be only approximate. The more the size factors differ, the more residual dependence of the variance on the mean you will find in the transformed data. As shown in the vignette, you can use the function `meanSdPlot` from the package vsn to see whether this is a problem for your data.

### Value

For `varianceStabilizingTransformation`, an `ExpressionSet`.

For `getVarianceStabilizedData`, a `matrix` of the same dimension as the count data, containing the transformed values.

### Author(s)

Simon Anders <sanders@fs.tum.de>

### Examples

    cds <- makeExampleCountDataSet()
    cds <- estimateSizeFactors( cds )
    cds <- estimateDispersions( cds, method="blind" )
    vsd <- getVarianceStabilizedData( cds )
    colsA <- conditions(cds) == "A"
    plot( rank( rowMeans( vsd[,colsA] ) ), genefilter::rowVars( vsd[,colsA] ) )

[Package DESeq version 1.36.0 Index]