QuantileNormalization {aroma.affymetrix} | R Documentation |
Package: aroma.affymetrix
Class QuantileNormalization
Object
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AromaTransform
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Transform
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ProbeLevelTransform
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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.
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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)