vsnh {vsn} | R Documentation

## A function that transforms a matrix of microarray intensities.

### Description

A function that transforms a matrix of microarray intensities. This function works in conjunction with `vsn`. `vsn` and `vsnh` have been superseded by `vsn2` and the `predict` method for `vsn` objects. The functions `vsn` and `vsnh` remain in the package for backward compatibility, but for new projects, please use `vsn2` and `predict`.

### Usage

    vsnh(y, p, strata)

### Arguments

`y` | A numeric matrix containing intensity values from an array experiment. It may contain NA values.
`p` | An array with the transformation parameters. If `strata` is specified, it must be a 3d array, dim(p)[1] must be greater than or equal to the maximum of `strata`, dim(p)[2] must be ncol(y), and dim(p)[3] must be 2. If `strata` is missing, then the first dimension may be omitted. NA values are not allowed. See Details.
`strata` | Integer vector of length nrow(y). See `vsn` for details.

### Details

The transformation is:

vsnh(y, p, s)[k, i] = asinh( p[s[k], i, 1] + p[s[k], i, 2] * y[k, i] ) - log(2*p[s[1], 1, 2])

where k=1:nrow(y) counts over the probes, i=1:ncol(y) counts over the samples, p[s[k], i, 1] is the calibration offset for stratum s[k] in sample i, p[s[k], i, 2] is the calibration factor for stratum s[k] in sample i, and s[k] is the stratum of the the k-th probe.

The constant offset - log(2*p[s[1], 1, 2]) is there to make sure that for large y, vsnh(y) for the first stratum on the first chip is approximately the same as log(y). This has no effect on the generalized log-ratios (glog-ratios), which are differences between transformed intensities, but some users are more comfortable with the absolute values that are obtained this way, since they are more comparable to the log scale.

### Value

A numeric matrix of the same size as y, with the transformed data.

### Author(s)

Wolfgang Huber http://www.ebi.ac.uk/huber

### References

Variance stabilization applied to microarray data calibration and to the quantification of differential expression, Wolfgang Huber, Anja von Heydebreck, Holger Sueltmann, Annemarie Poustka, Martin Vingron; Bioinformatics (2002) 18 Suppl.1 S96-S104.

Parameter estimation for the calibration and variance stabilization of microarray data, Wolfgang Huber, Anja von Heydebreck, Holger Sueltmann, Annemarie Poustka, and Martin Vingron; Statistical Applications in Genetics and Molecular Biology (2003) Vol. 2 No. 1, Article 3. http://www.bepress.com/sagmb/vol2/iss1/art3.

### See Also

`vsn`

### Examples

    data(kidney)
    y      = exprs(kidney)
    p      = array(c(-0.2, -0.1, 0.1, 0.2, 0.0026, 0.0028, 0.0030, 0.0032), dim=c(2,2,2))
    strata = sample(1:2, nrow(y), replace=TRUE)
    res1   = vsnh(exprs(kidney), p, strata)

    res2   = asinh(p[strata,,1] + p[strata,,2] * y) - log(2*p[strata,1,2])

    stopifnot(max(abs(res1 - res2)) < 1e-10)

[Package vsn version 3.8.0 Index]