Package: aroma.affymetrix
Class RmaPlm
Object
~~|
~~+--ParametersInterface
~~~~~~~|
~~~~~~~+--Model
~~~~~~~~~~~~|
~~~~~~~~~~~~+--UnitModel
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--MultiArrayUnitModel
~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~+--ProbeLevelModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~~~~~~+--RmaPlm
Directly known subclasses:
ExonRmaPlm, HetLogAddCnPlm, HetLogAddPlm, HetLogAddSnpPlm, RmaCnPlm, RmaSnpPlm
public abstract 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", "oligo"))
Arguments passed to ProbeLevelModel.
A character string specifying what model fitting algorithm
to be used. This makes it possible to get identical estimates as other
packages.
Methods:
No methods defined.
Methods inherited from ProbeLevelModel:
calculateResidualSet, calculateWeights, fit, getAsteriskTags, getCalculateResidualsFunction, getChipEffectSet, getProbeAffinityFile, getResidualSet, getRootPath, getWeightsSet
Methods inherited from MultiArrayUnitModel:
getListOfPriors, setListOfPriors, validate
Methods inherited from UnitModel:
findUnitsTodo, getAsteriskTags, getFitSingleCellUnitFunction, getParameters
Methods inherited from Model:
as.character, fit, getAlias, getAsteriskTags, getDataSet, getFullName, getName, getPath, getRootPath, getTags, setAlias, 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
For a single unit group, the log-additive model of RMA is:
$$log_2(y_{ik}) = \beta_i + \alpha_k + \varepsilon_{ik}$$
where \(\beta_i\) are the chip effects for arrays \(i=1,...,I\), and \(\alpha_k\) 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 \(\sum_k{\alpha_k} = 0\).
Note that all PLM classes must return parameters on the intensity scale. For this class that means that \(\theta_i = 2^\beta_i\) and \(\phi_k = 2^\alpha_k\) 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, Ken Simpson
Irizarry et al. Summaries of Affymetrix GeneChip probe level data.
NAR, 2003, 31, e15.