AffyBatch object and returns the result in an ExpressionSet object. The methods supported by NormiR for the background correction are provided by
the affy or limma packages, depending on whether the input AffyBatch
object has been created with ReadAffy or ReadExi/createAB,
respectively.
By default, it applies the spike-in probe-based method for the second step of normalization. In case the spike-in probe-based method cannot be applied, a median normalization is executed instead. Several options allow however to force the execution of the spike-in probe-based normalization and to fine-tune the resulting correction functions.
The next step of PM correction is enabled only when numerical values are available
for the MM probes of the input AffyBatch object. In this case the methods proposed
by NormiR are provided by the affy package.
The methods supported by NormiR for the last step of summarization are also
provided by the affy package. They do not depend on how the input AffyBatch
object has been created.
NormiR(abatch, # background correction bg.correct=TRUE, bgcorrect.method='auto', bgcorrect.param=list(), # normalize normalize=TRUE, normalize.method='spikein', normalize.param=list(), # pm correction (enabled only when MM-values are available) pmcorrect.method='pmonly', pmcorrect.param=list(), # expression values summary.method='medianpolish', summary.param=list(), summary.subset=NULL, # misc. verbose=FALSE, ...)AffyBatch object.
TRUE: the background correction step will be performed.
NormiR.bgcorrect.methods(abatch) indicates which methods can be used, depending
on the raw data contained in the abatch object. The auto option
corresponds to the default choice of applying rma for single-channel arrays
and normexp for dual-channel arrays.
R list containing the parameters required by the selected background correction method,
as specified in the documentation of the original functions bg.correct of the
affy package or backgroundCorrect of the limma package. As an illustration
the parameters of the normexp method of the limma package are given below.
normexp method, matching exactly
the argument normexp.method of the backgroundCorrect function.
offset of the backgroundCorrect function.
TRUE: the normalization step will be performed.
spikein method is used. Running NormiR.normalize.methods(abatch) indicates
which other methods can be chosen, depending on the raw data contained in the
abatch object.
R list of the arguments that are used to control the spikein normalization. Running
NormiR.spikein.args() provides a complete list of all the tunable parameters supported by
NormiR and explained below.
display is used. Figures are shown to the screen. Using file
generates the figures in PDF format in the working directory.
span argument of the R loess function
mean. The method used for the high-intensity extrapolation of
the normalization correction function.
FALSE. If TRUE, it forces the normalization correction functions to
have zero values at the lower end of the probe intensity range.
TRUE; some details are provided on the console.
NormiR uses the
probes annotated as "spike-in" by Exiqon or Affymetrix.
AffyBatch object. Running NormiR.pmcorrect.methods(abatch) indicates
which other methods can be chosen instead of the default one pmonly.
R list of optional parameters for the selected pmcorrect.method, as specified
in the documentation of the original function pmcorrect function of the affy package.
NormiR.summary.methods() indicates which other methods can be chosen instead
of the default one medianpolish.
R list of optional parameters for the selected summary.method, as specified
in the documentation of the original AffyBatch method computeExprSet contained in
the affy package.
R list of probe set identifiers. When set to its default NULL value, the
summarized expression values are computed for all probe sets available on the array.
TRUE. The details of the function execution are displayed on the console.
ExpressionSet object containing the normalized expression data.
bg.correct.miR,
NormiR.bgcorrect.methods,
norm.miR,
NormiR.normalize.methods,
NormiR.spikein.args,
NormiR.pmcorrect.methods,
summarize.miR,
NormiR.summary.methods.
data(galenv)
data(GSE20122)
eset.spike <- NormiR(GSE20122,
bg.correct=FALSE,
normalize.method='spikein',
summary.method='medianpolish')
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