vsn2 {vsn} | R Documentation |
vsn2
fits the vsn model to the data matrix
in an ExpressionSet
and
returns a vsn
object with the fit parameters and
the transformed data matrix.
The data matrix contains, typically, feature intensity readings from a
microarray. There are also vsn2
methods for numeric matrices and vectors.
predict
applies a fitted model
to data and returns an ExpressionSet
object.
justvsn
is a simple wrapper that
takes and returns an ExpressionSet
.
These are the main functions of this package. An overview is given in the vignette
Introduction to vsn.
## S4 method for signature 'ExpressionSet': vsn2(x, reference, strata, ...) vsnMatrix(x, reference, strata, lts.quantile = 0.9, subsample = 0L, verbose = interactive(), returnData = TRUE, pstart, cvg.niter = 5L, cvg.eps = 5e-3) ## S4 method for signature 'matrix': vsn2(x, reference, strata, ...) ## S4 method for signature 'numeric': vsn2(x, reference, strata, ...)
x |
An object containing the data to which the model is to be
fitted. Methods exists for ExpressionSet ,
matrix and numeric . |
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 Details for a more precise explanation. |
strata |
Optional, a factor whose length is nrow(x) . Can
be used for stratified normalization (i.e. separate offsets a and
factors b for each level of strata ). If missing, all
rows of x are assumed to come from one stratum. |
lts.quantile |
Numeric of length 1. The quantile that is used for the resistant least trimmed sum of squares regression. Allowed values are between 0.5 and 1. A value of 1 corresponds to ordinary least sum of squares regression. |
subsample |
Integer of length 1. If specified, the model parameters are
estimated from a subsample of the data of size subsample
only, yet the fitted transformation is
then applied to all data. For large datasets, this can substantially
reduce the CPU time and memory consumption at a negligible loss of precision. |
verbose |
Logical. If TRUE, some messages are printed. |
returnData |
Logical. If TRUE, the transformed data are returned
in a slot of the resulting vsn object.
The option to set this option to FALSE allows saving of memory
if the data are not needed. |
pstart |
Optional, array. Can be used to specify start values
for the iterative parameter estimation algorithm. See
vsn2trsf for a description of the layout of the array. |
cvg.niter |
Integer. The number of iterations to be used in the least trimmed sum of squares regression. |
cvg.eps |
Numeric. A convergence treshold. |
... |
Arguments that get passed on to vsnMatrix . |
If the reference
argument is not specified, then the model
parameters $μ_k$ and $σ$ are fit from the data in x
.
This is the mode of operation described in the 2002 Bioinformatics
paper and that was the only option in versions 1.X of this package.
If reference
is specified, the model parameters $μ_k$ and $σ$ are taken from
it. This allows for 'incremental' normalization. See the vignette
Likelihood Calculations for vsn.
An object of class vsn
.
The transformed data are on a glog scale to base 2. More precisely,
the transformed data are subject to the transformation
asinh(a+bx)/log(2)+c, where
$mbox{asinh}(x)/log(2)=log_2(x+sqrt{x^2+1})$ is also called the 'glog', and
the constant c is an overall constant offset that is computed such that for
large x the transformation approximately corresponds to the
$log_2$ function. The offset c is inconsequential for all differential expression
calculations, but many users like to see the data in a range that they
are familiar with.
Wolfgang Huber http://www.ebi.ac.uk/huber
data("kidney") fit = vsn2(kidney) ## fit nkid = predict(fit, newdata=kidney) ## apply fit plot(exprs(nkid), pch=".") abline(a=0, b=1, col="red")