RmaPlm {aroma.affymetrix}R Documentation

The RmaPlm class

Description

Package: aroma.affymetrix
Class RmaPlm

Object
~~|
~~+--Model
~~~~~~~|
~~~~~~~+--UnitModel
~~~~~~~~~~~~|
~~~~~~~~~~~~+--MultiArrayUnitModel
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--ProbeLevelModel
~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~+--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).

Usage

RmaPlm(..., flavor=c("affyPLM", "affyPLMold", "oligo"))

Arguments

... 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.

Fields and Methods

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

Model

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.

Different flavors of model fitting

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.

Author(s)

Henrik Bengtsson (http://www.braju.com/R/)

References

Irizarry et al. Summaries of Affymetrix GeneChip probe level data. NAR, 2003, 31, e15.


[Package aroma.affymetrix version 1.2.0 Index]