#' @name getCOCA

#' @title Get subtypes from COCA

#' @description This function wraps the COCA (Cluster-of-Clusters Analysis) algorithm and provides standard output for `getMoHeatmap()` and `getConsensusMOIC()`.

#' @param data List of matrices.

#' @param N.clust Number of clusters.

#' @param methods A string vector storing the names of clustering methods to be used to cluster the observations in each subdataset.

#' @param distances A string vector storing the name of distances to be used in the clustering step for each subdataset.

#' @param type Data type corresponding to the list of matrics, which can be gaussian, binomial or possion.

#' @return A list with the following components:

#'

#'         \code{fit}        an object returned by \link[coca]{coca}.

#'

#'         \code{clust.res}  a data.frame storing sample ID and corresponding clusters.

#'

#'         \code{clust.dend} a dendrogram of sample clustering.

#'

#'         \code{mo.method}  a string value indicating the method used for multi-omics integrative clustering.

#' @import coca

#' @importFrom vegan vegdist

#' @export

#' @examples # There is no example and please refer to vignette.

#' @references Hoadley KA, Yau C, Wolf DM, et al (2014). Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell, 158(4):929-944.

getCOCA <- function(data      = NULL,

                    N.clust   = NULL,

                    type      = rep("gaussian", length(data)),

                    methods   = "hclust",

                    distances = "euclidean") {

  # check data

  n_dat <- length(data)

  if(n_dat > 6){

    stop('current verision of MOVICS can support up to 6 datasets.')

  }

  if(n_dat < 2){

    stop('current verision of MOVICS needs at least 2 omics data.')

  }

  useless.argument <- type

  data <- lapply(data, t)

  ### Build matrix of clusters

  outputBuildMOC <- coca::buildMOC(data,

                                   M         = length(data),

                                   K         = N.clust,

                                   methods   = methods,

                                   distances = distances)

  ### Extract matrix of clusters and dataset indicator vector

  moc <- outputBuildMOC$moc

  datasetIndicator <- outputBuildMOC$datasetIndicator

  hcs <- hclust(vegdist(as.matrix(moc), method = "jaccard"), "ward.D")

  coca <- cutree(hcs,N.clust)

  #coca <- coca::coca(moc, K = N.clust)

  clustres <- data.frame(samID = rownames(data[[1]]),

                         clust = as.numeric(coca),

                         row.names = rownames(data[[1]]),

                         stringsAsFactors = FALSE)

  #clustres <- clustres[order(clustres$clust),]

  return(list(fit = outputBuildMOC, clust.res = clustres, clust.dend = hcs, mo.method = "COCA"))

}