class:vsnInput

vsnInput

vsnInput-class

[,vsnInput-method

dim,vsnInput-method

nrow,vsnInput-method

ncol,vsnInput-method

show,vsnInput-method


  Class to contain input data and parameters for vsn functions


classes

The package implements a method for normalising microarray intensities,
both between colours within array, and between arrays. The method uses a
robust variant of the maximum-likelihood estimator for the stochastic model of
microarray data described in the references (see vignette).
The model incorporates data calibration (a.k.a. normalization), a model for
the dependence of the variance on the mean intensity, and a
variance stabilizing data transformation. Differences between
transformed intensities are analogous to "normalized
log-ratios". However, in contrast to the latter, their
variance is independent of the mean, and they are usually more
sensitive and specific in detecting differential
transcription.

Wolfgang Huber

Variance stabilization and calibration for microarray data

vsnInput function

Creating Objects

<dl>
  <dt><code>x</code>:</dt><dd>A numeric matrix with the input data.</dd> <dt><code>reference</code>:</dt><dd>An object of <code>vsn</code>,
    typically this would have been
    obtained from a previous fit to a set of reference arrays (data).</dd> <dt><code>strata</code>:</dt><dd>A factor of length 0 or n. If its length is
    n, then its levels correspond to different normalization strata
    (see <code><a rd-options="" href="/link/vsn2?package=vsn&version=3.40.0" data-mini-rdoc="vsn::vsn2">vsn2</a></code>).</dd> <dt><code>ordered</code>:</dt><dd>Logical scalar; are the rows reordered so that
    the strata are contiguous.</dd> <dt><code>lts.quantile</code>:</dt><dd>Numeric scalar, see<code><a rd-options="" href="/link/vsn2?package=vsn&version=3.40.0" data-mini-rdoc="vsn::vsn2">vsn2</a></code>.</dd> <dt><code>subsample</code>:</dt><dd>Integer scalar, see<code><a rd-options="" href="/link/vsn2?package=vsn&version=3.40.0" data-mini-rdoc="vsn::vsn2">vsn2</a></code>.</dd> <dt><code>verbose</code>:</dt><dd>Logical scalar, see<code><a rd-options="" href="/link/vsn2?package=vsn&version=3.40.0" data-mini-rdoc="vsn::vsn2">vsn2</a></code>.</dd> <dt><code>calib</code></dt><dd>Character of length 1, see manual page of <code><a rd-options="" href="/link/vsn2?package=vsn&version=3.40.0" data-mini-rdoc="vsn::vsn2">vsn2</a></code>.</dd> <dt><code>pstart</code>:</dt><dd>A 3D array of size (number of strata) x (number of
    columns of the data matrix) x 2. It contains the start parameters.</dd> <dt><code>optimpar</code>:</dt><dd>List with parameters for the numerical
    optimiser <code>L-BFGS-B</code>; see the manual page of <code><a rd-options="" href="/link/vsn2?package=vsn&version=3.40.0" data-mini-rdoc="vsn::vsn2">vsn2</a></code>.</dd> </dl> 

Slots

<dl>
    <dt><code>[</code></dt><dd>Subset</dd> <dt><code>dim</code></dt><dd>Get dimensions of data matrix.</dd> <dt><code>nrow</code></dt><dd>Get number of rows of data matrix.</dd> <dt><code>ncol</code></dt><dd>Get number of columns of data matrix.</dd> <dt><code>show</code></dt><dd>Print a summary of the object</dd> </dl>

vsnInput: Class to contain input data and parameters for vsn functions

Description

Arguments

Creating Objects

Slots

Methods

See Also