LinearModelProbeSequenceNormalization | R Documentation |
Package: aroma.affymetrix
Class LinearModelProbeSequenceNormalization
Object
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ParametersInterface
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AromaTransform
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Transform
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ProbeLevelTransform
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ProbeLevelTransform3
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AbstractProbeSequenceNormalization
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LinearModelProbeSequenceNormalization
Directly known subclasses:
BasePositionNormalization
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.
LinearModelProbeSequenceNormalization(...)
... |
Arguments passed to the constructor of
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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:
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
This class requires that an aroma probe sequence file is available for the chip type.
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
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