ClassifyR (version 1.6.2)

edgeRselection: Feature Selection Based on Differential Expression for RNA-seq

Description

Performs a differential expression analysis between classes and chooses the features which have best resubstitution performance.

Usage

"edgeRselection"(expression, classes, ...) "edgeRselection"(expression, datasetName, normFactorsOptions = NULL, dispOptions = NULL, fitOptions = NULL, trainParams, predictParams, resubstituteParams, selectionName = "edgeR LRT", verbose = 3)

Arguments

expression

Either a matrix or ExpressionSet containing the expression values.

classes

A vector of class labels.

...

Unused variables from the matrix method passed to the ExpressionSet method.

datasetName

A name for the dataset used. Stored in the result.

normFactorsOptions

A named list of any options to be passed to calcNormFactors.

dispOptions

A named list of any options to be passed to estimateDisp.

fitOptions

A named list of any options to be passed to glmFit.

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

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

The differential expression analysis follows the standard edgeR steps of estimating library size normalisation factors, calculating dispersion, in this case robustly, and then fitting a generalised linear model followed by a likelihood ratio test.

References

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data, Mark D. Robinson, Davis McCarthy, and Gordon Smyth, 2010, Bioinformatics, Volume 26 Issue 1, bioinformatics.oxfordjournals.org/content/26/1/139.

Examples

  if(require(parathyroidSE) && require(sparsediscrim) && require(PoiClaClu))
  {
    data(parathyroidGenesSE)
    expression <- assays(parathyroidGenesSE)[[1]]
    DPN <- which(colData(parathyroidGenesSE)[, "treatment"] == "DPN")
    control <- which(colData(parathyroidGenesSE)[, "treatment"] == "Control")
    expression <- expression[, c(control, DPN)]
    classes <- rep(c("Contol", "DPN"), c(length(control), length(DPN)))
    expression <- expression[rowSums(expression > 1000) > 8, ] # Make small dataset.
    edgeRselection(expression, classes, "DPN Treatment",
                   trainParams = TrainParams(classifyInterface, TRUE, TRUE),
                   predictParams = PredictParams(function(){}, TRUE, getClasses = function(result) result[["ytehat"]]),
                   resubstituteParams = ResubstituteParams(nFeatures = seq(10, 100, 10),
                                        performanceType = "balanced", better = "lower"))
  }