NSCselectionInterface {ClassifyR} | R Documentation

## Interface for `pamr.listgenes` Function from `pamr` CRAN Package

### Description

Restructures variables from ClassifyR framework to be compatible with `pamr.listgenes` definition.

### Usage

      ## S4 method for signature 'matrix'
    NSCselectionInterface(measurements, classes, ...)
      ## S4 method for signature 'DataFrame'
    NSCselectionInterface(measurements, classes, datasetName,
                       trained, ..., selectionName = "Shrunken Centroids", verbose = 3)
      ## S4 method for signature 'MultiAssayExperiment'
    NSCselectionInterface(measurements, targets = names(measurements), ...)

### Arguments

`measurements` | Either a `matrix`, `DataFrame` or `MultiAssayExperiment` containing the training data. For a `matrix`, the rows are features, and the columns are samples.
`classes` | Either a vector of class labels of class `factor` of the same length as the number of samples in `measurements` or if the measurements are of class `DataFrame` a character vector of length 1 containing the column name in `measurement` is also permitted. Not used if `measurements` is a `MultiAssayExperiment` object.
`targets` | If `measurements` is a `MultiAssayExperiment`, the names of the data tables to be used. `"clinical"` is also a valid value and specifies that numeric variables from the clinical data table will be used.
`datasetName` | A name for the data set used. Stored in the result.
`trained` | The output of `NSCtrainInterface`, which is identical to the output of `pamr.listgenes`.
`...` | Variables not used by the `matrix` nor the `MultiAssayExperiment` method which are passed into and used by the `DataFrame` method or extra arguments passed to `pamr.listgenes`.
`selectionName` | A name to identify this selection method by. Stored in the result.
`verbose` | Default: 3. A number between 0 and 3 for the amount of progress messages to give. This function only prints progress messages if the value is 3.

### Details

This function is an interface between the ClassifyR framework and `pamr.listgenes`.

The set of features chosen is the obtained by considering the range of thresholds provided to `NSCtrainInterface` and using the threshold that obtains the lowest cross-validation error rate on the training set.

### Value

An object of class `SelectResult`. The `rankedFeatures` slot will be empty.

### Author(s)

Dario Strbenac

### See Also

`pamr.listgenes` for the function that was interfaced to.

### Examples

      if(require(pamr))
      {
        # Genes 76 to 100 have differential expression.
        genesMatrix <- sapply(1:25, function(geneColumn) c(rnorm(100, 9, 1)))
        genesMatrix <- cbind(genesMatrix, sapply(1:25, function(geneColumn)
                                     c(rnorm(75, 9, 1), rnorm(25, 14, 1))))
        rownames(genesMatrix) <- paste("Gene", 1:nrow(genesMatrix))
        classes <- factor(rep(c("Poor", "Good"), each = 25))

        trained <- NSCtrainInterface(genesMatrix, classes)
        selected <- NSCselectionInterface(genesMatrix, classes, "Example", trained)

        selected@chosenFeatures
      }

[Package ClassifyR version 2.4.4 Index]