interactorDifferences {ClassifyR} | R Documentation

## Convert Individual Features into Differences Between Binary Interactors Based on Known Sub-networks

### Description

This conversion is useful for creating a meta-feature table for classifier training and prediction based on sub-networks that were selected based on their differential correlation between classes.

### Usage

      ## S4 method for signature 'matrix'
    interactorDifferences(measurements, ...)
      ## S4 method for signature 'DataFrame'
    interactorDifferences(measurements, networkSets = NULL, absolute = FALSE, verbose = 3)
      ## S4 method for signature 'MultiAssayExperiment'
    interactorDifferences(measurements, target = NULL, ...)

### Arguments

`measurements` | Either a `matrix`, `DataFrame` or `MultiAssayExperiment` containing the training data. For a `matrix`, the rows are features, and the columns are samples.
`networkSets` | A object of type `FeatureSetCollection`. The `sets` slot must contain a list of two-column matrices with each row corresponding to a binary interaction. Such sub-networks may be determined by a community detection algorithm.
`absolute` | If TRUE, then the absolute values of the differences are returned.
`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.
`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

The pairs of features known to interact with each other are specified by `networkSets`.

### Value

An object of class `DataFrame` with one column for each interactor pair difference and one row for each sample. Additionally, `mcols(resultTable)` prodvides a `DataFrame` with a column named "original" containing the name of the sub-network each meta-feature belongs to.

### Author(s)

Dario Strbenac

### References

Dynamic modularity in protein interaction networks predicts breast cancer outcome, Ian W Taylor, Rune Linding, David Warde-Farley, Yongmei Liu, Catia Pesquita, Daniel Faria, Shelley Bull, Tony Pawson, Quaid Morris and Jeffrey L Wrana, 2009, Nature Biotechnology, Volume 27 Issue 2, https://www.nature.com/articles/nbt.1522.

### Examples

      networksList <- list(`A Hub` = matrix(c('A', 'A', 'A', 'B', 'C', 'D'), ncol = 2),
                           `G Hub` = matrix(c('G', 'G', 'G', 'H', 'I', 'J'), ncol = 2))
      netSets <- FeatureSetCollection(networksList)

      # Differential correlation for sub-network with hub A.
      measurements <- matrix(c(5.7, 10.1, 6.9, 7.7, 8.8, 9.1, 11.2, 6.4, 7.0, 5.5,
                               5.6, 9.6, 7.0, 8.4, 10.8, 12.2, 8.1, 5.7, 5.4, 12.1,
                               4.5, 9.0, 6.9, 7.0, 7.3, 6.9, 7.8, 7.9, 5.7, 8.7,
                               8.1, 10.6, 7.4, 7.15, 10.4, 6.1, 7.3, 2.7, 11.0, 9.1,
                               round(rnorm(60, 8, 1), 1)), ncol = 10, byrow = TRUE)

      rownames(measurements) <- LETTERS[1:10]
      colnames(measurements) <- paste("Patient", 1:10)

      interactorDifferences(measurements, netSets)

[Package ClassifyR version 2.8.0 Index]