featureSetSummary: Transform a Table of Feature Abundances into a Table of...

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

Description

Represents a feature set by the mean or median feature measurement of a feature set for all features belonging to a feature set.

Usage

  ## S4 method for signature 'matrix'
featureSetSummary(measurements, location = c("median", "mean"),
                   featureSets, minimumOverlapPercent = 80, verbose = 3)
  ## S4 method for signature 'DataFrame'
featureSetSummary(measurements, location = c("median", "mean"),
                                          featureSets, minimumOverlapPercent = 80, verbose = 3)
  ## S4 method for signature 'MultiAssayExperiment'
featureSetSummary(measurements, target = NULL, location = c("median", "mean"),
                   featureSets, minimumOverlapPercent = 80, verbose = 3)

Arguments

measurements	Either a matrix or DataFrame containing the training data. For a matrix, the rows are features, and the columns are samples.
target	If the input is a MultiAssayExperiment, this specifies which data set will be transformed. Can either be an integer or a character string specifying the name of the table. Must have length 1.
location	Default: The median. The type of location to summarise a set of features belonging to a feature set by.
featureSets	An object of type FeatureSetCollection which defines the feature sets.
minimumOverlapPercent	The minimum percentage of overlapping features between the data set and a feature set defined in featureSets for that feature set to not be discarded from the anaylsis.
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

This feature transformation method is unusual because the mean or median feature of a feature set for one sample may be different to another sample, whereas most other feature transformation methods do not result in different features being compared between samples during classification.

Value

The same class of variable as the input variable measurements is, with the individual features summarised to feature sets. The number of samples remains unchanged, so only one dimension of measurements is altered.

Author(s)

Dario Strbenac

References

Network-based biomarkers enhance classical approaches to prognostic gene expression signatures, Rebecca L Barter, Sarah-Jane Schramm, Graham J Mann and Yee Hwa Yang, 2014, BMC Systems Biology, Volume 8 Supplement 4 Article S5, https://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S4-S5.

Examples

  sets <- list(Adhesion = c("Gene 1", "Gene 2", "Gene 3"),
               `Cell Cycle` = c("Gene 8", "Gene 9", "Gene 10"))
  featureSets <- FeatureSetCollection(sets)
  
  # Adhesion genes have a median gene difference between classes.
  genesMatrix <- matrix(c(rnorm(5, 9, 0.3), rnorm(5, 7, 0.3), rnorm(5, 8, 0.3),
                        rnorm(5, 6, 0.3), rnorm(10, 7, 0.3), rnorm(70, 5, 0.1)),
                        ncol = 10, byrow = TRUE)
  rownames(genesMatrix) <- paste("Gene", 1:10)
  colnames(genesMatrix) <- paste("Patient", 1:10)
  classes <- factor(rep(c("Poor", "Good"), each = 5)) # But not used for transformation.
  
  featureSetSummary(genesMatrix, featureSets = featureSets)

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