Package: aroma.affymetrix
Class LimmaBackgroundCorrection
Object
~~|
~~+--ParametersInterface
~~~~~~~|
~~~~~~~+--AromaTransform
~~~~~~~~~~~~|
~~~~~~~~~~~~+--Transform
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--ProbeLevelTransform
~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~+--BackgroundCorrection
~~~~~~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~~~~~~+--LimmaBackgroundCorrection
Directly known subclasses:
NormExpBackgroundCorrection
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.
Methods:
process | - |
Methods inherited from BackgroundCorrection:
getParameters, process
Methods inherited from ProbeLevelTransform:
getRootPath
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
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.
By default, only PM signals are background corrected and MMs are left unchanged.
Internally, backgroundCorrect is used.