runClassifier {geneClassifiers} | R Documentation

## Perform classification.

### Description

`runClassifier` performs classification by applying a classifier to gene expression data.

### Usage

    runClassifier(classifierParameters, fixedExpressionData, ...)

      ## S4 method for signature 'character,FixedExpressionData'
    runClassifier(classifierParameters,
      fixedExpressionData, ...)

      ## S4 method for signature 'ClassifierParameters,FixedExpressionData'
    runClassifier(classifierParameters,
      fixedExpressionData, ...)

### Arguments

`classifierParameters` | Either a text value indicating a classifier name (see `showClassifierList`), or an object of class ClassifierParameters as returned by the `getClassifier` function.
`fixedExpressionData` | The data to be classified in the form of a `FixedExpressionData` object as returned by the `setNormalizationMethod` function.
`...` | see details

### Details

A list of possible classifiers is obtained by `showClassifierList`. The data to be classified is first to be processed by the setNormalizationMethod function. By default the data is assumed to contain many (n>=25) samples with corresponding probe-sets needed for classification. If one of these conditions is not met, a classifier outcome might be seriously affected. By default an error is given. Although strongly discouraged, it is possible to circumvent the security checks. If not all required probe-sets are included in the input set, you can explicitly pass the parameter `allow.reweighted = TRUE` to the `runClassifier` function in order to determine the classifier outcome using less probe-sets (e.g. possible if the missing probe-sets are known to have minimal contribution).See `vignette("MissingCovariates")` for more information. If the input data has a small number of samples, the default batch correction becomes ineffective. If you are aware of the possible negative effects you can force to not use batch correction by passing the parameter `do.batchcorrection=FALSE`.

### Value

The classification results as an object of class `ClassifierResults`.

### See Also

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

### Examples

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

[Package geneClassifiers version 1.16.0 Index]