RmaPlm {aroma.affymetrix} | R Documentation |
Package: aroma.affymetrix
Class RmaPlm
Object
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~~+--
Model
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UnitModel
~~~~~~~~~~~~|
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MultiArrayUnitModel
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~~~~~~~~~~~~~~~~~+--
ProbeLevelModel
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RmaPlm
Directly known subclasses:
ExonRmaPlm, HetLogAddCnPlm, HetLogAddPlm, HetLogAddSnpPlm, RmaCnPlm, RmaSnpPlm
public static class RmaPlm
extends ProbeLevelModel
This class represents the log-additive model part of the Robust Multichip Analysis (RMA) method described in Irizarry et al (2003).
RmaPlm(..., flavor=c("affyPLM", "affyPLMold", "oligo"))
... |
Arguments passed to ProbeLevelModel . |
flavor |
A character string specifying what model fitting algorithm
to be used. This makes it possible to get identical estimates as other
packages. |
Methods:
getAsteriskTags | - |
Methods inherited from ProbeLevelModel:
calculateResidualSet, calculateWeights, fit, getAsteriskTags, getCalculateResidualsFunction, getChipEffectSet, getProbeAffinityFile, getResidualSet, getWeightsSet
Methods inherited from MultiArrayUnitModel:
getListOfPriors, setListOfPriors, validate
Methods inherited from UnitModel:
findUnitsTodo, getAsteriskTags, getFitSingleCellUnitFunction
Methods inherited from Model:
fit, getAlias, getAsteriskTags, getDataSet, getFullName, getName, getPath, getRootPath, getTags, setAlias, setTags
Methods inherited from Object:
asThis, $, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clone, detach, equals, extend, finalize, gc, getEnvironment, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, registerFinalizer, save
For a single unit group, the log-additive model of RMA is:
log_2(y_{ik}) = β_i + α_k + varepsilon_{ik}
where β_i are the chip effects for arrays i=1,...,I, and α_k are the probe affinities for probes k=1,...,K. The varepsilon_{ik} are zero-mean noise with equal variance. The model is constrained such that sum_k{α_k} = 0.
Note that all PLM classes must return parameters on the intensity scale. For this class that means that theta_i = 2^β_i and phi_k = 2^α_k are returned.
There are a few differ algorithms available for fitting the same
probe-level model. The default and recommended method
(flavor="affyPLM"
) uses the implementation in the
preprocessCore package which fits the model parameters robustly
using an M-estimator (the method used to be in affyPLM).
Alternatively, other model-fitting algorithms are available.
The algorithm (flavor="oligo"
) used by the oligo package,
which originates from the affy packages, fits the model using
median polish, which is a non-robust estimator. Note that this algorithm
does not constraint the probe-effect parameters to multiply to one on
the intensity scale. Since the internal function does not return these
estimates, we can neither rescale them.
Henrik Bengtsson (http://www.braju.com/R/)
Irizarry et al. Summaries of Affymetrix GeneChip probe level data.
NAR, 2003, 31, e15.