setNormalizationMethod-methods: Prepare data.
In rkuiper/geneClassifiers: Application of gene classifiers

Description Usage Arguments Details Value See Also Examples

setNormalizationMethod is to be called prior to running a classifier.

1	setNormalizationMethod(expressionSet, method, ...)

`expressionSet`	An object of class `ExpressionSet` containing the gene expression data.
`method`	A character string indicating the normalization that was applied to the data. Possible values are given by `getNormalizationMethods()`.
`...`	see details.

The FixedExpressionData class forms together with the ClassifierParameters class the basis for input to the runClassifier function. The data inside the FixedExpressionData-class has to be stored as it is right after normalization. This function may require some additional arguments:

isLog2Transformed = TRUE Use this argument if the data already underwent a log2transformation, as is common e.g. in case of MAS5.0 normalization.
targetValue = value This is a MAS5.0 specific argument. It is the sample intensity mean when the lowest and highest 2% of intensities are discarded. If only part of the original expression set is given to this function, then this argument is required.

An object of class FixedExpressionData

Other workflow functions: getNormalizationMethod, runClassifier, showClassifierList

data(exampleMAS5)
myData <- setNormalizationMethod(exampleMAS5, "MAS5.0",targetValue=500)
results <- runClassifier('EMC92', myData)
getScores( results )
getClassifications( results )