LinearModelProbeSequenceNormalization {aroma.affymetrix}R Documentation

The LinearModelProbeSequenceNormalization class


Package: aroma.affymetrix
Class LinearModelProbeSequenceNormalization


Directly known subclasses:

public abstract static class LinearModelProbeSequenceNormalization
extends AbstractProbeSequenceNormalization

This abstract class represents a normalization method that corrects for systematic effects in the probe intensities due to probe-sequence dependent effects that can be modeled using a linear model.





Arguments passed to the constructor of AbstractProbeSequenceNormalization.

Fields and Methods

No methods defined.

Methods inherited from AbstractProbeSequenceNormalization:
fitOne, getAromaCellSequenceFile, getParameters, getTargetFile, indexOfMissingSequences, predictOne, process

Methods inherited from ProbeLevelTransform3:
getAsteriskTags, getCellsTo, getCellsToFit, getCellsToUpdate, getParameters, getUnitsTo, getUnitsToFit, getUnitsToUpdate, writeSignals

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


This class requires that an aroma probe sequence file is available for the chip type.

Memory usage

The model fitting methods of this class are bounded in memory. This is done by first building up the normal equations incrementally in chunks of cells. The generation of normal equations is otherwise the step that consumes the most memory. When the normal equations are available, the solve() method is used to solve the equations. Note that this algorithm is still exact.


Henrik Bengtsson

[Package aroma.affymetrix version 3.2.1 Index]