featureSetSummary {ClassifyR} | R Documentation

## Transform a Table of Feature Abundances into a Table of Feature Set Abundances.

### 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)

[Package ClassifyR version 2.8.0 Index]