| QuantileNormalization {aroma.affymetrix} | R Documentation |
Package: aroma.affymetrix
Class QuantileNormalization
Object
~~|
~~+--AromaTransform
~~~~~~~|
~~~~~~~+--Transform
~~~~~~~~~~~~|
~~~~~~~~~~~~+--ProbeLevelTransform
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--QuantileNormalization
Directly known subclasses:
DChipQuantileNormalization
public static class QuantileNormalization
extends ProbeLevelTransform
This class represents a normalization function that transforms the probe-level signals towards the same empirical distribution.
QuantileNormalization(..., subsetToUpdate=NULL, typesToUpdate=NULL, targetDistribution=NULL, subsetToAvg=subsetToUpdate, typesToAvg=typesToUpdate)
... |
Arguments passed to the constructor of
ProbeLevelTransform. |
subsetToUpdate |
The probes to be updated.
If NULL, all probes are updated. |
typesToUpdate |
Types of probes to be updated. |
targetDistribution |
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.
|
Methods:
clearCache | - | |
process | Normalizes the data set. |
Methods inherited from Transform:
getOutputDataSet, getOutputDataSetOLD20090509, getOutputFiles
Methods inherited from AromaTransform:
getExpectedOutputFiles, getExpectedOutputFullnames, getFullName, getInputDataSet, getName, getOutputDataSet, getOutputDataSet0, getOutputFiles, getPath, getTags, isDone, process, setTags
Methods inherited from Object:
asThis, $, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clone, detach, equals, extend, finalize, gc, getEnvironment, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, registerFinalizer, save
Henrik Bengtsson (http://www.braju.com/R/)
## Not run:
for (zzz in 0) {
# Setup verbose output
verbose <- Arguments$getVerbose(-2)
timestampOn(verbose)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Define an example dataset
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Find any dataset
path <- NULL
if (is.null(path))
break
ds <- AffymetrixCelSet$fromFiles(path)
print(ds)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Normalization
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
norm <- QuantileNormalization(ds, subsetToAvg=1/3)
dsQN <- process(norm, verbose=verbose)
print(dsQN)
} # for (zzz in 0)
rm(zzz)
## End(Not run)