LimmaBackgroundCorrection {aroma.affymetrix}R Documentation

The LimmaBackgroundCorrection class


Package: aroma.affymetrix
Class LimmaBackgroundCorrection


Directly known subclasses:

public static class LimmaBackgroundCorrection
extends BackgroundCorrection

This class represents the various "background" correction methods implemented in the limma package.


LimmaBackgroundCorrection(..., args=NULL, addJitter=FALSE, jitterSd=0.2, seed=6022007)



Arguments passed to the constructor of BackgroundCorrection.


A list of additional arguments passed to the correction algorithm.


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.


By default, only PM signals are background corrected and MMs are left unchanged.

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, names, objectSize, print, save, asThis

Jitter noise

The fitting algorithm of the normal+exponential 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, adding Gaussian noise may generate non-positive signals.


Henrik Bengtsson. Adopted from RmaBackgroundCorrection by Ken Simpson.

See Also

Internally, backgroundCorrect is used.

[Package aroma.affymetrix version 3.2.2 Index]