setNormalizationMethod {geneClassifiers} | R Documentation

## Prepare data.

### Description

`setNormalizationMethod` is to be called prior to running a classifier.

### Usage

    setNormalizationMethod(expressionSet, method, ...)

### Arguments

`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.

### 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.

### Value

An object of class `FixedExpressionData`

### See Also

Other workflow functions: `getNormalizationMethod`, `runClassifier`, `showClassifierList`

### Examples

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

[Package geneClassifiers version 1.6.0 Index]