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+++ b/R/getLRAcluster.R
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+#' @name getLRAcluster
+#' @title Get subtypes from LRAcluster
+#' @description This function wraps the LRAcluster (Integrated cancer omics data anlsysi by low rank approximation) algorithm and provides standard output for `getMoHeatmap()` and `getConsensusMOIC()`.
+#' @param data List of matrices.
+#' @param N.clust Number of clusters.
+#' @param clusterAlg A string value to indicate the cluster algorithm for similarity matrix; 'ward.D' by default.
+#' @param type Data type corresponding to the list of matrics, which can be gaussian, binomial or possion; 'gaussian' by default.
+#' @return A list with the following components:
+#'
+#'         \code{fit}        an object returned by \link[LRAcluster]{LRAcluster}.
+#'
+#'         \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.
+#' @examples # There is no example and please refer to vignette.
+#' @importFrom dplyr %>%
+#' @export
+#' @references Wu D, Wang D, Zhang MQ, Gu J (2015). Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. BMC Genomics, 16(1):1022.
+getLRAcluster <- function(data       = NULL,
+                          N.clust    = NULL,
+                          type       = rep("gaussian", length(data)),
+                          clusterAlg = "ward.D"){
+
+  # 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.')
+  }
+
+  data <- lapply(data, as.matrix)
+
+  if(is.element("binomial",type)) {
+    bindex <- which(type == "binomial")
+    for (i in bindex) {
+      a <- which(rowSums(data[[i]]) == 0)
+      b <- which(rowSums(data[[i]]) == ncol(data[[i]]))
+      if(length(a) > 0) {
+        data[[i]] <- data[[i]][which(rowSums(data[[i]]) != 0),] # remove all zero
+      }
+
+      if(length(b) > 0) {
+        data[[i]] <- data[[i]][which(rowSums(data[[i]]) != ncol(data[[i]])),] # remove all one
+      }
+
+      if(length(a) + length(b) > 0) {
+        message(paste0("--", names(data)[i],": a total of ",length(a) + length(b), " features were removed due to the categories were not equal to 2!"))
+      }
+    }
+    type[bindex] <- "binary"
+  }
+
+  fit <- LRAcluster(data, dimension = N.clust, types = as.list(type))
+  dist <- fit$coordinate %>% t %>% dist
+  clust.dend <- hclust(dist, method = clusterAlg)
+
+  clustres <- data.frame(samID = colnames(data[[1]]),
+                         clust = cutree(clust.dend,k = N.clust),
+                         row.names = colnames(data[[1]]),
+                         stringsAsFactors = FALSE)
+  #clustres <- clustres[order(clustres$clust),]
+
+  return(list(fit = fit, clust.res = clustres, clust.dend = clust.dend, mo.method = "LRAcluster"))
+}