normalize.AffyBatch.vsn {vsn} | R Documentation

## Wrapper for vsn to be used as a normalization method with expresso

### Description

Wrapper for `vsn2` to be used as a normalization method with the expresso function of the package affy. The expresso function is deprecated, consider using `justvsn` instead. The normalize.AffyBatch.vsn can still be useful on its own, as it provides some additional control of the normalization process (fitting on subsets, alternate transform parameters).

### Usage

      normalize.AffyBatch.vsn(
         abatch,
         reference,
         strata = NULL,
         subsample = if (nrow(exprs(abatch))>30000L) 30000L else 0L,
         subset,
         log2scale = TRUE,
         log2asymp=FALSE,
         ...)

### Arguments

`abatch` | An object of type `AffyBatch`.
`reference` | Optional, a 'vsn' object from a previous fit. If this argument is specified, the data in 'x' are normalized "towards" an existing set of reference arrays whose parameters are stored in the object 'reference'. If this argument is not specified, then the data in 'x' are normalized "among themselves". See `vsn2` for details.
`strata` | The 'strata' functionality is not supported, the parameter is ignored.
`subsample` | Is passed on to `vsn2`.
`subset` | This allows the specification of a subset of expression measurements to be used for the vsn fit. The transformation with the parameters of this fit is then, however, applied to the whole dataset. This is useful for excluding expression measurements that are known to be differentially expressed or control probes that may not match the vsn model, thus avoiding that they influence the normalization process. This operates at the level of probesets, not probes. Both 'subset' and 'subsample' can be used together.
`log2scale` | If TRUE, this will perform a global affine transform on the data to put them on a similar scale as the original non-transformed data. Many users prefer this. Fold-change estimates are not affected by this transform. In some situations, however, it may be helpful to turn this off, e.g., when comparing independently normalized subsets of the data.
`log2asymp` | If TRUE, this will perform a global affine transform on the data to make the generalized log (asinh) transform be asymptotically identical to a log base 2 transform. Some people find this helpful. Only one of 'log2scale' or 'log2asymp' can be set to TRUE. Fold-change estimates are not affected by this transform.
`...` | Further parameters for `vsn2`.

### Details

Please refer to the Details and References sections of the man page for `vsn2` for more details about this method.

Important note: after calling `vsn2`, the function `normalize.AffyBatch.vsn` exponentiates the data (base 2). This is done in order to make the behavior of this function similar to the other normalization methods in affy. That packages uses the convention of taking the logarithm to base in subsequent analysis steps (e.g. in `medpolish`).

### Value

An object of class `AffyBatch`. The `vsn` object returned, which can be used as `reference` for subsequent fits, is provided by `description(abatch)@preprocessing$vsnReference`.

### Author(s)

D. P. Kreil http://bioinf.boku.ac.at/, Wolfgang Huber

### See Also

`vsn2`

### Examples

    ## Please see vignette.

[Package vsn version 3.56.0 Index]