normalizeQuantile.AffymetrixCelSet: Normalizes samples to have the same empirical distribution

In aroma.affymetrix: Analysis of Large Affymetrix Microarray Data Sets

Normalizes samples to have the same empirical distribution

Description

Normalizes samples to have the same empirical distribution.

Usage

## S3 method for class 'AffymetrixCelSet'
normalizeQuantile(this, path=NULL, name="normQuantile", subsetToUpdate=NULL,
  typesToUpdate=NULL, xTarget=NULL, subsetToAvg=subsetToUpdate, typesToAvg=typesToUpdate,
  ..., verbose=FALSE)

Arguments

path	The path where to save the normalized data files. If NULL, a default name is used.
name	The name of the normalized data set, which will also be part of the default path.
subsetToUpdate	The probes to be updated. If NULL, all probes are updated.
typesToUpdate	Types of probes to be updated.
xTarget	A numeric vector. The empirical distribution to which all arrays should be normalized to.
subsetToAvg	The probes to calculate average empirical distribution over. If a single numeric in (0,1), then this fraction of all probes will be used. If NULL, all probes are considered.
typesToAvg	Types of probes to be used when calculating the average empirical distribution. If "pm" and "mm" only perfect-match and mismatch probes are used, respectively. If "pmmm" both types are used.
...	Additional arguments passed to normalizeQuantile().
verbose	See Verbose.

Value

Returns a double vector.

Author(s)

Henrik Bengtsson

See Also

normalizeQuantileRank.numeric For more information see AffymetrixCelSet.

aroma.affymetrix documentation built on Aug. 21, 2025, 5:30 p.m.