
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)
ProbeLevelTransform
.NULL
, all probes are updated."pm"
and "mm"
only perfect-match and mismatch
probes are used, respectively. If "pmmm"
both types are used.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, getChecksum, $, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clearLookupCache, clone, detach, equals, extend, finalize, gc, getEnvironment, getFieldModifier, getFieldModifiers, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, registerFinalizer, savefor (zzz in 0) {
# Setup verbose output
verbose <- Arguments$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)
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