interactorDifferences {ClassifyR} | R Documentation |
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.
## 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, ...)
measurements |
Either a |
networkSets |
A object of type |
absolute |
If TRUE, then the absolute values of the differences are returned. |
target |
If |
... |
Variables not used by the |
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. |
The pairs of features known to interact with each other are specified by networkSets
.
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.
Dario Strbenac
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.
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)