DMDselection: Selection of Differential Distributions with Differences in...

In ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

Description

Ranks features by largest Differences in Means/Medians and Deviations and chooses the features which have best resubstitution performance.

Usage

  ## S4 method for signature 'matrix'
DMDselection(measurements, classes, ...)
  ## S4 method for signature 'DataFrame'
DMDselection(measurements, classes, datasetName, differences = c("both", "location", "scale"),
             trainParams, predictParams, resubstituteParams, ...,
             selectionName = "Differences of Medians and Deviations",
             verbose = 3)
  ## S4 method for signature 'MultiAssayExperiment'
DMDselection(measurements, targets = names(measurements), ...)

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.
targets	If measurements is a MultiAssayExperiment, the names of the data tables to be used. "clinical" is also a valid value and specifies that numeric variables from the clinical data table will be used.
...	Variables not used by the matrix nor the MultiAssayExperiment method which are passed into and used by the DataFrame method or parameters for getLocationsAndScales, such as location, scale.
datasetName	Default: "Differences of Medians and Deviations". A name for the data set used. Stored in the result.
differences	Default: "both". Either "both", "location", or "scale". The type of differences to consider. If both are considered then the absolute difference in location and the absolute difference in scale are summed.
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

DMD is defined as sum(|location i - location j|) + sum(|scale i - scale j|), i not j, i < j. The subscripts denote the class for which the parameter is calculated for.

Data tables which consist entirely of non-numeric data cannot be analysed. If measurements is an object of class MultiAssayExperiment, the factor of sample classes must be stored in the DataFrame accessible by the colData function with column name "class".

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

  # First 20 features have bimodal distribution for Poor class.
  # Other 80 features have normal distribution for both classes.
  genesMatrix <- sapply(1:25, function(sample)
                              {
                                randomMeans <- sample(c(8, 12), 20, replace = TRUE)
                                c(rnorm(20, randomMeans, 1), rnorm(80, 10, 1))
                              }
                       )
  genesMatrix <- cbind(genesMatrix, sapply(1:25, function(sample) rnorm(100, 10, 1)))
  classes <- factor(rep(c("Poor", "Good"), each = 25))
  
  resubstituteParams <- ResubstituteParams(nFeatures = seq(10, 100, 10),
                                           performanceType = "balanced error",
                                           better = "lower")
  DMDselection(genesMatrix, classes, datasetName = "Example",
               trainParams = TrainParams(naiveBayesKernel),
               predictParams = PredictParams(NULL),
               resubstituteParams = resubstituteParams)

ClassifyR documentation built on Nov. 8, 2020, 6:53 p.m.