RmaBackgroundCorrection: The RmaBackgroundCorrection class

Description Usage Arguments Details Fields and Methods Jitter noise Author(s)


Package: aroma.affymetrix
Class RmaBackgroundCorrection


Directly known subclasses:

public static class RmaBackgroundCorrection
extends BackgroundCorrection

This class represents the RMA background adjustment function.


RmaBackgroundCorrection(..., addJitter=FALSE, jitterSd=0.2, seed=6022007)



Arguments passed to the constructor of BackgroundCorrection.


If TRUE, Zero-mean gaussian noise is added to the signals before being background corrected.


Standard deviation of the jitter noise added.


An (optional) integer specifying a temporary random seed to be used for generating the (optional) jitter. The random seed is set to its original state when done. If NULL, it is not set.


Internally bg.adjust is used to background correct the probe signals. The default is to background correct PM signals only.

Fields and Methods


process -

Methods inherited from BackgroundCorrection:
getParameters, process

Methods inherited from ProbeLevelTransform:

Methods inherited from Transform:
getOutputDataSet, getOutputFiles

Methods inherited from AromaTransform:
as.character, findFilesTodo, getAsteriskTags, getExpectedOutputFiles, getExpectedOutputFullnames, getFullName, getInputDataSet, getName, getOutputDataSet, getOutputDataSet0, getOutputFiles, getPath, getRootPath, getTags, isDone, process, setTags

Methods inherited from ParametersInterface:
getParameterSets, getParameters, getParametersAsString

Methods inherited from Object:
$, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clearLookupCache, clone, detach, equals, extend, finalize, getEnvironment, getFieldModifier, getFieldModifiers, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, save, asThis

Jitter noise

The fitting algorithm of the RMA background correction model may not converge if there too many small and discrete signals. To overcome this problem, a small amount of noise may be added to the signals before fitting the model. This is an ad hoc solution that seems to work. However, add Gaussian noise may generate non-positive signals.


Ken Simpson, Henrik Bengtsson

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