--- a
+++ b/R/getCOCA.R
@@ -0,0 +1,63 @@
+#' @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"))
+}