pairsDifferencesSelection: Selection of Pairs of Features that are Different Between...
In ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

Description Usage Arguments Details Value Author(s) References See Also Examples

Ranks pre-specified pairs of features by the largest difference of the sum of measurement differences over all samples within a class and chooses the pairs of features which have the best resubstitution performance.

  ## S4 method for signature 'matrix'
pairsDifferencesSelection(measurements, classes, featurePairs = NULL, ...)
  ## S4 method for signature 'DataFrame'
pairsDifferencesSelection(measurements, classes, featurePairs = NULL,
                  datasetName, trainParams, predictParams, resubstituteParams,
                  selectionName = "Pairs Differences", verbose = 3)
  ## S4 method for signature 'MultiAssayExperiment'
pairsDifferencesSelection(measurements, target = names(measurements)[1], featurePairs = NULL, ...)

`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.
`featurePairs`	An S4 object of type `Pairs` containing feature identifiers to calculate the sum of differences within each class for.
`target`	If `measurements` is a `MultiAssayExperiment`, the name of the data table to be used.
`...`	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.

Instead of considering whether one feature in a pair of features is consistently lower or higher than the other in the pair, this method takes the sum of differences across all samples within a class, to prevent ties in the ranking of pairs of features.

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.

Dario Strbenac

Simple decision rules for classifying human cancers from gene expression profiles, Aik C Tan, Daniel Q Naiman, Lei Xu, Raimond L. Winslow and Donald Geman, 2005, Bioinformatics, Volume 21 Issue 20, https://academic.oup.com/bioinformatics/article/21/20/3896/203010.

kTSPclassifier for a classifier which makes use of the pairs of selected features in classification.

  featurePairs <- Pairs(c('A', 'A'), c('B', 'C'))
                           
  # Difference in differences for features A and C between classes.                                           
  measurements <- matrix(c(9.9, 10.5, 10.1, 10.9, 11.0, 6.6, 7.7, 7.0, 8.1, 6.5,
                           8.5, 10.5, 12.5, 10.5, 9.5, 8.5, 10.5, 12.5, 10.5, 9.5,
                           6.6, 7.7, 7.0, 8.1, 6.5, 11.2, 11.0, 11.1, 11.4, 12.0,
                           8.1, 10.6, 7.4, 7.1, 10.4, 6.1, 7.3, 2.7, 11.0, 9.1,
                           round(rnorm(60, 8, 1), 1)), ncol = 10, byrow = TRUE)
  classes <- factor(rep(c("Good", "Poor"), each = 5))
                         
  rownames(measurements) <- LETTERS[1:10]
  colnames(measurements) <- names(classes) <- paste("Patient", 1:10)

  # The features are pairs and there are only two in this example.
  resubstituteParams <- ResubstituteParams(nFeatures = 1:2,
                                performanceType = "balanced error", better = "lower")
  
  predictParams <- PredictParams(NULL)
  pairsDifferencesSelection(measurements, classes, featurePairs = featurePairs,
                            datasetName = "Example",
                            trainParams = TrainParams(kTSPclassifier),
                            predictParams = predictParams,
                            resubstituteParams = resubstituteParams)