LimmaBackgroundCorrection {aroma.affymetrix} | R Documentation |
The LimmaBackgroundCorrection class
Description
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.
Usage
LimmaBackgroundCorrection(..., args=NULL, addJitter=FALSE, jitterSd=0.2, seed=6022007)
Arguments
... |
Arguments passed to the constructor of
|
args |
A |
addJitter |
If |
jitterSd |
Standard deviation of the jitter noise added. |
seed |
An (optional) |
Details
By default, only PM signals are background corrected and MMs are left unchanged.
Fields and Methods
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
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.
Author(s)
Henrik Bengtsson. Adopted from RmaBackgroundCorrection by Ken Simpson.
See Also
Internally, backgroundCorrect
is used.