ClassifyR (version 1.6.2)

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, datasetName, trainParams, predictParams, resubstituteParams, ..., selectionName = "Moderated t-test", 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.

datasetName

A name for the dataset 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.

...

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

selectionName

A name to identify this selection method by. Stored in the result.

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

An object of class SelectResult or a list of such objects, if the classifier which was used for determining resubstitution error rate made a number of prediction varieties.

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))
  {
    # 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))
    
    limmaSelection(genesMatrix, classes, "Example",
                    trainParams = TrainParams(), predictParams = PredictParams(),
                    resubstituteParams = ResubstituteParams(nFeatures = seq(10, 100, 10), performanceType = "balanced", better = "lower"))
  }