--- a
+++ b/R/getCIMLR.R
@@ -0,0 +1,86 @@
+#' @name getCIMLR
+#' @title Get subtypes from CIMLR
+#' @description This function wraps the CIMLR (Cancer Integration via Multikernel Learning) algorithm and provides standard output for `getMoHeatmap()` and `getConsensusMOIC()`.
+#' @param data List of matrices.
+#' @param N.clust Number of clusters.
+#' @param cores.ratio Ratio of the number of cores to be used when computing the multi-kernel.
+#' @param verbose A logic value to indicate if supressing progression.
+#' @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[CIMLR]{CIMLR}.
+#'
+#'         \code{clust.res} a data.frame storing sample ID and corresponding clusters.
+#'
+#'         \code{feat.res}  the results of features selection process.
+#'
+#'         \code{mo.method} a string value indicating the method used for multi-omics integrative clustering.
+#' @import CIMLR
+#' @export
+#' @examples # There is no example and please refer to vignette.
+#' @references Ramazzotti D, Lal A, Wang B, Batzoglou S, Sidow A (2018). Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat Commun, 9(1):4453.
+getCIMLR <- function(data        = NULL,
+                     N.clust     = NULL,
+                     type        = rep("gaussian", length(data)),
+                     cores.ratio = 0,
+                     verbose     = TRUE){
+
+  # 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
+  if(verbose) {
+    fit <- quiet(CIMLR(data,
+                       c= N.clust,
+                       cores.ratio = cores.ratio))
+  } else {
+    fit <- CIMLR(data,
+                 c= N.clust,
+                 cores.ratio = cores.ratio)
+  }
+
+  message("clustering done...")
+  input_dat <- do.call(rbind,lapply(seq(along = data), function(x){
+    ddd <- data[[x]]
+    rownames(ddd) <- paste(rownames(ddd), names(data)[x], sep = "+")
+    ddd
+  }))
+
+  if(verbose) {
+    ranks <- quiet(CIMLR_Feature_Ranking(A = fit$S, X = input_dat))
+  } else {
+    ranks <- CIMLR_Feature_Ranking(A = fit$S, X = input_dat)
+  }
+  ranks$names <- rownames(input_dat)[ranks$aggR]
+  fit$selectfeatures <- ranks
+  message("feature selection done...")
+
+  clustres <- data.frame(samID = colnames(data[[1]]),
+                         clust = fit$y$cluster,
+                         row.names = colnames(data[[1]]),
+                         stringsAsFactors = FALSE)
+  #clustres <- clustres[order(clustres$clust),]
+
+  f <- sapply(strsplit(ranks$name, "+",fixed = TRUE), "[",1)
+  d <- sapply(strsplit(ranks$name, "+",fixed = TRUE), "[",2)
+
+  featres <- data.frame(feature = f,
+                        dataset = d,
+                        pvalue = ranks$pval,
+                        stringsAsFactors = FALSE)
+  feat.res <- NULL
+  for (d in unique(featres$dataset)) {
+    tmp <- featres[which(featres$dataset == d),]
+    tmp <- tmp[order(tmp$pvalue, decreasing = FALSE),]
+    tmp$rank <- 1:nrow(tmp)
+    feat.res <- rbind.data.frame(feat.res,tmp)
+  }
+
+  return(list(fit = fit, clust.res = clustres, feat.res = feat.res, mo.method = "CIMLR"))
+}