ClassifyR (version 1.2.4)

limmaSelection: Selection of Differentially Expressed Features

Description

Uses a moderated t-test with empirical Bayes shrinkage to select differentially expressed features.

Usage

"limmaSelection"(expression, classes, ...) "limmaSelection"(expression, trainParams, predictParams, resubstituteParams, ..., verbose = 3)

Arguments

expression

Either a matrix or ExpressionSet containing the training data. For a matrix, the rows are features, and the columns are samples.

classes

A vector of class labels.

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.

...

For the matrix method, variables passed to the ExpressionSet method. For the ExpressionSet method, extra parameters that are passed to TrainParams

verbose

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.

Value

A list of length 2. The first element has the features ranked from most important to least important. The second element has the features that were selected to be used for classification.

Details

This selection method looks for differential expression. It uses a moderated t-test.

References

Limma: linear models for microarray data, Gordon Smyth, 2005, In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, Springer, New York, pages 397-420.

Examples

  if(require(sparsediscrim))
  {
    # Samples in one class with differential expression to other class.
    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))))
    classes <- factor(rep(c("Poor", "Good"), each = 25))
    
    limmaSelection(genesMatrix, classes,
                    trainParams = TrainParams(), predictParams = PredictParams(),
                    resubstituteParams = ResubstituteParams(nFeatures = seq(10, 100, 10), performanceType = "balanced", better = "lower"))
  }