Variance Stabilizing Transformation

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Description

Stabilizing the expression variance based on the bead level expression variance and mean relations

Usage

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vst(u, std, nSupport = min(length(u), 500), backgroundStd=NULL, fitMethod = c('linear', 'quadratic'), lowCutoff = 1/3, ifPlot = FALSE)

Arguments

u

mean expression of the beads with same sequence

std

expression standard deviation of the beads with same sequence

nSupport

the number of down-sampling to speed processing

backgroundStd

pre-estimated background standard deviation level

fitMethod

methods of fitting the relations between expression variance and mean relations

lowCutoff

cutoff ratio to determine the low expression range. Do not change this until you now what you are doing.

ifPlot

plot intermediate results or not

Details

The variance-stabilizing transformation (VST) takes the advantage of larger number of technical replicates available on the Illumina microarray. It models the mean-variance relationship of the within-array technical replicates at the bead level of Illumina microarray. An arcsinh transform is then applied to stabilize the variance. See reference for more details.

For the methods of fitting the relations between expression variance and mean relations, the 'linear' method is more robust and provides detailed parameters for inverseVST.

Value

Return the transformed (variance stabilized) expression values.

Author(s)

Pan Du, Simon Lin

References

Lin, S.M., Du, P., Kibbe, W.A., "Model-based Variance-stabilizing Transformation for Illumina Mi-croarray Data", submitted

See Also

lumiT, inverseVST

Examples

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## load example data
data(example.lumi)

## get the gene expression mean for one chip
u <- exprs(example.lumi)[,1]
## get the gene standard deviation for one chip
std <- se.exprs(example.lumi)[,1]

## do variance stabilizing transform
transformedU <- vst(u, std)

## do variance stabilizing transform with plotting intermediate result 
transformedU <- vst(u, std, ifPlot=TRUE)

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