vsn2trsf: Apply the vsn transformation to data
In vsn: Variance stabilization and calibration for microarray data

Description Usage Arguments Value Author(s) Examples

Apply the vsn transformation to data.

1 2	## S4 method for signature 'vsn' predict(object, newdata, strata=object@strata, log2scale=TRUE, useDataInFit=FALSE)

`object`	An object of class `vsn` that contains transformation parameters and strata information, typically this is the result of a previous call to `vsn2`.
`newdata`	Object of class `ExpressionSet`, `NChannelSet`, `AffyBatch` (from the `affy` package), `RGList` (from the `limma` package), `matrix` or `numeric`, with the data to which the fit is to be applied to.
`strata`	Optional, a `factor` or `integer` that aligns with the rows of `newdata`; see the `strata` argument of `vsn2`.
`log2scale`	If `TRUE`, the data are returned on the glog scale to base 2, and an overall offset c is added (see Value section of the `vsn2` manual page). If `FALSE`, the data are returned on the glog scale to base e, and no offset is added.
`useDataInFit`	If `TRUE`, then no transformation is attempted and the data stored in `object` is transferred appropriately into resulting object, which otherwise preserves the class and metadata of `newdata`. This option exists to increase performance in constructs like fit = vsn2(x, ...) nx = predict(fit, newdata=x) and is used, for example, in the `justvsn` function.

An object typically of the same class as newdata. There are two exceptions: if newdata is an RGList, the return value is an NChannelSet, and if newdata is numeric, the return value is a matrix with 1 column.

Wolfgang Huber

data("kidney")

## nb: for random subsampling, the 'subsample' argument of vsn
##   provides an easier way to do this
fit = vsn2(kidney[sample(nrow(kidney), 500), ])
tn = predict(fit, newdata=exprs(kidney))