aroma.affymetrix (version 1.6.0)

ProbeLevelModel: The ProbeLevelModel class

Description

Package: aroma.affymetrix Class ProbeLevelModel Object ~~| ~~+--Model ~~~~~~~| ~~~~~~~+--UnitModel ~~~~~~~~~~~~| ~~~~~~~~~~~~+--MultiArrayUnitModel ~~~~~~~~~~~~~~~~~| ~~~~~~~~~~~~~~~~~+--ProbeLevelModel Directly known subclasses: AffineCnPlm, AffinePlm, AffineSnpPlm, AvgCnPlm, AvgPlm, AvgSnpPlm, ExonRmaPlm, HetLogAddCnPlm, HetLogAddPlm, HetLogAddSnpPlm, MbeiCnPlm, MbeiPlm, MbeiSnpPlm, RmaCnPlm, RmaPlm, RmaSnpPlm public static class ProbeLevelModel extends MultiArrayUnitModel This abstract class represents a probe-level model (PLM) as defined by the affyPLM package: "A [...] PLM is a model that is fit to probe-intensity data. More specifically, it is where we fit a model with probe level and chip level parameters on a probeset by probeset basis", where the more general case for a probeset is a unit group in Affymetrix CDF terms.

Usage

ProbeLevelModel(..., standardize=TRUE)

Arguments

...

Arguments passed to MultiArrayUnitModel.

standardize

If TRUE, chip-effect and probe-affinity estimates are rescaled such that the product of the probe affinities is one.

Fields and Methods

Methods: rll{ calculateResidualSet - calculateWeights - fit Estimates the model parameters. getChipEffectSet Gets the set of chip effects for this model. getProbeAffinityFile Gets the probe affinities for this model. 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

Details

In order to minimize the risk for mistakes, but also to be able compare results from different PLMs, all PLM subclasses must meet the following criteria:

1. All parameter estimates must be (stored and returned) on the intensity scale, e.g. log-additive models such asRmaPlmhave to transform the parameters on the log-scale to the intensity scale.
2. The probe-affinity estimates$\phi_k$for a unit group must be constrained such that$\prod_k \phi_k = 1$, or equivalently if$\phi_k > 0$,$\sum_k \log(\phi_k) = 0$.

Note that the above probe-affinity constraint guarantees that the estimated chip effects across models are on the same scale.

See Also

For more details on probe-level models, please see the affyPLM package.