normalize.AffyBatch.vsn {vsn} | R Documentation

## Wrapper for vsn to be used as a normalization method in the package affy

### Description

Wrapper for `vsn` to be used as a normalization method in the package affy

### Usage

    normalize.AffyBatch.vsn(abatch, subsample=20000, niter = 4, strata, ...)

### Arguments

`abatch` | An object of type `AffyBatch`.
`subsample` | The number of probes to be sampled for the fit of the transformation parameters.
`niter` | Parameter passed on to `vsn`.
`strata` | Integer vector of length nrow(y). See `vsn` for details.
`...` | Further parameters for `vsn`.

### Details

Please refer to the "details" and "references" sections of the man page for `vsn` for more details about this method.

Important note: after calling `vsn`, the function `normalize.AffyBatch.vsn` exponentiates the data. This is done in order to make the behavior of this function similar to the other normalization methods in affy. There, it is assumed that in subsequent analysis steps (e.g. in `medianpolish`), the logarithm to base 2 needs to be taken.

### Value

An object of class `AffyBatch`.

### Author(s)

Wolfgang Huber mailto:w.huber@dkfz.de

### See Also

`vsn`

### Examples

    ### The example has been commented out because it takes a long time.
    ### Please also refer to the vignette.

    ## library(affy)
    ## library(affydata)
    ## data(Dilution)

    ### let affy know about vsn
    ## normalize.AffyBatch.methods <- c(normalize.AffyBatch.methods, "vsn")

    ## es1 = expresso(Dilution[1:2],
    ##        bg.correct       = FALSE,   ## bg correction is done by vsn
    ##        normalize.method = "vsn",
    ##        pmcorrect.method = "pmonly",
    ##        summary.method   = "medianpolish")

    ## es2 = expresso(Dilution[1:2],
    ##        bgcorrect.method = "rma",
    ##        normalize.method = "quantiles",
    ##        pmcorrect.method = "pmonly",
    ##        summary.method   = "medianpolish")

    ## par(mfrow=c(2,2), pch=".")

    ### extract expression values
    ## x1 = exprs(es1)
    ## x2 = exprs(es2)

    ### scatter plot
    ## plot(x1, main="vsn: chip 3 vs 4")
    ## plot(x2, main="rma: chip 3 vs 4")

    ### rank(mean) - difference plot
    ## ylim = c(-0.7, 0.7)
    ## plot(rank(rowSums(x1)), diff(t(x1)), ylim=ylim, main="rank(mean) vs differences")
    ## abline(h=0, col="red")

    ## plot(rank(rowSums(x2)), diff(t(x2)), ylim=ylim, main="rank(mean) vs differences")
    ## abline(h=0, col="red")

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