RmaBackgroundCorrection {aroma.affymetrix} | R Documentation |
The RmaBackgroundCorrection class
Description
Package: aroma.affymetrix
Class RmaBackgroundCorrection
Object
~~|
~~+--
ParametersInterface
~~~~~~~|
~~~~~~~+--
AromaTransform
~~~~~~~~~~~~|
~~~~~~~~~~~~+--
Transform
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--
ProbeLevelTransform
~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~+--
BackgroundCorrection
~~~~~~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~~~~~~+--
RmaBackgroundCorrection
Directly known subclasses:
public static class RmaBackgroundCorrection
extends BackgroundCorrection
This class represents the RMA background adjustment function.
Usage
RmaBackgroundCorrection(..., addJitter=FALSE, jitterSd=0.2, seed=6022007)
Arguments
... |
Arguments passed to the constructor of
|
addJitter |
If |
jitterSd |
Standard deviation of the jitter noise added. |
seed |
An (optional) |
Details
Internally bg.adjust
is used to background correct the
probe signals. The default is to background correct PM signals only.
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 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.
Author(s)
Ken Simpson, Henrik Bengtsson