R/cophenetic-correlation.R

In acabassi/klic: Kernel Learning Integrative Clustering

#' Cophenetic correlation coefficient
#'
#' Compute the cophenetic correlation coefficient of a kernel matrix, which is
#' a measure of how faithfully hierarchical clustering would preserve the
#' pairwise distances between the original data points.
#' @param kernelMatrix kernel matrix.
#' @return This functions returns the cophenetic correlation coefficient of the
#' kernel matrix provided as input.
#' @author Alessandra Cabassi \email{alessandra.cabassi@mrc-bsu.cam.ac.uk}
#' @references Cabassi, A. and Kirk, P. D. W. (2019). Multiple kernel learning
#' for integrative consensus clustering of genomic datasets. arXiv preprint.
#' arXiv:1904.07701.
#' @references Sokal, R.R. and Rohlf, F.J., 1962. The comparison of dendrograms
#' by objective methods. Taxon, 11(2), pp.33-40.
#' @examples
#' # Load kernel matrix
#' consensus_matrix <- as.matrix(read.csv(system.file('extdata',
#' 'consensus_matrix1.csv', package = 'klic'), row.names = 1))
#'
#' # Compute cophenetic correlation
#' coph_corr_coeff <- copheneticCorrelation(consensus_matrix)
#' cat(coph_corr_coeff)
#' @export

copheneticCorrelation = function(kernelMatrix) {

    # Convert the similarities into distances
    KMasDist <- stats::as.dist(1 - kernelMatrix)

    # Perform hierarchical clustering on the distances derived from the kernel
    # matrix
    hclustered <- stats::hclust(KMasDist)

    # Compute the cophenetic distances of the hierarchical clustering
    cophDist <- stats::cophenetic(hclustered)

    # Compute the correlation between the cophenetic distances and the actual
    # distances
    cophCor <- stats::cor(KMasDist, cophDist)

}