R/expand-moc.R

In acabassi/coca: Cluster-of-Clusters Analysis

#' Expand matrix of clusters
#'
#' Expand matrix of cluster labels into matrix of clusters
#'
#' @param clLabels Matrix of cluster labels of size N x M.
#' @param datasetNames Vector of cluster names of length M. Default is NULL.
#' @return The output is a list containing:
#' \item{moc}{the matrix of clusters.}
#' \item{datasetIndicator}{a vector containing the dataset indicator.}
#' \item{datasetNames}{an expanded vector of dataset names for the moc.}
#' @author Alessandra Cabassi \email{alessandra.cabassi@mrc-bsu.cam.ac.uk}
#'
#' @examples
#' # Load data
#' data <- list()
#' data[[1]] <- as.matrix(read.csv(system.file("extdata", "dataset1.csv",
#' package = "coca"), row.names = 1))
#' data[[2]] <- as.matrix(read.csv(system.file("extdata", "dataset2.csv",
#' package = "coca"), row.names = 1))
#' data[[3]] <- as.matrix(read.csv(system.file("extdata", "dataset3.csv",
#' package = "coca"), row.names = 1))
#'
#' # Build matrix of clusters
#' outputBuildMOC <- buildMOC(data, M = 3, K = 6, distances = "cor")
#'
#' # Extract matrix of clusters
#' clLabels <- outputBuildMOC$clLabels
#'
#' # Impute missing values
#' outputFillMOC <- fillMOC(clLabels, data = data)
#'
#' # Replace matrix of cluster labels with new (full) one
#' clLabels <- outputFillMOC$fullClLabels
#'
#' # Expand matrix of cluster labels into matrix of clusters
#' outputExpandMOC <- expandMOC(clLabels)
#' clLabels <- outputExpandMOC$clLabels
#' @export

expandMOC <- function(clLabels, datasetNames = NULL) {

    # Number of data points
    N <- dim(clLabels)[1]
    # Number of datasets
    M <- dim(clLabels)[2]

    # Number of clusters in each dataset
    K <- rep(NA, M)
    for (i in seq_len(M)) {
        K[i] <- length(table(clLabels[, i]))
    }

    # If no names are provided for the datasets, assign names "1", "2", "3" and
    # so on.
    if (is.null(datasetNames)) {
        datasetNames <- as.character(seq_len(M))
    }

    # Initialise objects containing the output
    moc <- matrix(NA, N, sum(K))
    rownames(moc) <- rownames(clLabels)
    datasetIndicator <- NULL
    datasetNamesMOC <- NULL

    count <- 0

    # For each dataset
    for (i in seq_len(M)) {
        # Get cluster names
        clLabels_i <- unique(clLabels[!is.na(clLabels[, i]), i])
        # For each cluster name
        for (j in clLabels_i) {
            # Fill in the corresponding row in MOC
            count <- count + 1
            moc[, count] <- (clLabels[, i] == j) * 1
            datasetIndicator <- c(datasetIndicator, i)
            datasetNamesMOC <- c(datasetNamesMOC, datasetNames[i])
        }
    }

    # Check whether all the rows of MOC have been filled
    if (count != dim(moc)[2])
        warning("MOC has not been filled completely!")

    output <- list(moc = moc, datasetIndicator = datasetIndicator,
                   datasetNames = datasetNamesMOC)
    return(output)
}