differentMeansSelection {ClassifyR} | R Documentation

## Selection of Differentially Abundant Features

### Description

Uses an ordinary t-test if the data set has two classes or one-way ANOVA if the data set has three or more classes to select differentially expressed features.

### Usage

      ## S4 method for signature 'matrix'
    differentMeansSelection(measurements, classes, ...)
      ## S4 method for signature 'DataFrame'
    differentMeansSelection(measurements, classes, datasetName,
                   trainParams, predictParams, resubstituteParams,
                   selectionName = "Difference in Means", verbose = 3)
      ## S4 method for signature 'MultiAssayExperiment'
    differentMeansSelection(measurements, targets = NULL, ...)

### 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` | A vector of class labels of class `factor` of the same length as the number of samples in `measurements`. Not used if `measurements` is a `MultiAssayExperiment` object.
`targets` | Names of data tables to be combined into a single table and used in the analysis.
`...` | Variables not used by the `matrix` nor the `MultiAssayExperiment` method which are passed into and used by the `DataFrame` method.
`datasetName` | A name for the data set used. Stored in the result.
`trainParams` | A container of class `TrainParams` describing the classifier to use for training.
`predictParams` | A container of class `PredictParams` describing how prediction is to be done.
`resubstituteParams` | An object of class `ResubstituteParams` describing the performance measure to consider and the numbers of top features to try for resubstitution classification.
`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 selection method looks for changes in means and uses `rowttests` to rank the features if there are two classes or `rowFtests` if there are three or more classes. The choice of features is based on the best resubstitution performance.

### Value

An object of class `SelectResult` or a list of such objects, if the classifier which was used for determining the specified performance metric made a number of prediction varieties.

### Author(s)

Dario Strbenac

### Examples

      #if(require(sparsediscrim))
      #{
        # Genes 76 to 100 have differential expression.
        genesMatrix <- sapply(1:25, function(sample) c(rnorm(100, 9, 2)))
        genesMatrix <- cbind(genesMatrix, sapply(1:25, function(sample)
                                     c(rnorm(75, 9, 2), rnorm(25, 14, 2))))
        classes <- factor(rep(c("Poor", "Good"), each = 25))
        colnames(genesMatrix) <- paste("Sample", 1:ncol(genesMatrix))
        rownames(genesMatrix) <- paste("Gene", 1:nrow(genesMatrix))

        resubstituteParams <- ResubstituteParams(nFeatures = seq(10, 100, 10),
                                performanceType = "balanced error", better = "lower")
        selected <- differentMeansSelection(genesMatrix, classes, "Example",
                                   trainParams = TrainParams(), predictParams = PredictParams(),
                                   resubstituteParams = resubstituteParams)

        selected@chosenFeatures
      #}

[Package ClassifyR version 2.10.0 Index]