--- a
+++ b/R/getSNF.R
@@ -0,0 +1,57 @@
+#' @name getSNF
+#' @title Get subtypes from SNF
+#' @description This function wraps the SNF (Similarity Network Fusion) algorithm and provides standard output for `getMoHeatmap()` and `getConsensusMOIC()`.
+#' @param data List of matrices.
+#' @param N.clust Number of clusters.
+#' @param K An integer value to indicate the number of neighbors in K-nearest neighbors part of the algorithm.
+#' @param t An integer value to indicate the number of interations for the diffusion process.
+#' @param sigma A numerical value to indicate the variance for local model.
+#' @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[SNFtool]{SNF}.
+#'
+#'         \code{clust.res} a data.frame storing sample ID and corresponding clusters.
+#'
+#'         \code{mo.method} a string value indicating the method used for multi-omics integrative clustering.
+#' @export
+#' @examples # There is no example and please refer to vignette.
+#' @import SNFtool
+#' @importFrom dplyr %>%
+#' @references Wang B, Mezlini AM, Demir F, et al (2014). Similarity network fusion for aggregating data types on a genomic scale. Nat Methods, 11(3):333-337.
+getSNF <- function(data    = NULL,
+                   N.clust = NULL,
+                   type    = rep("gaussian", length(data)),
+                   K       = 30,
+                   t       = 20,
+                   sigma   = 0.5){
+
+  # 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)
+
+  dat <- lapply(data, function (dd){
+    dd <- dd %>% as.matrix
+    W <- dd %>% SNFtool::dist2(dd) %>% SNFtool::affinityMatrix(K = K, sigma = sigma)
+  })
+  W <-  SNFtool::SNF(Wall = dat,
+                     K    = K,
+                     t    = t)
+  clust.SNF = W %>% SNFtool::spectralClustering(N.clust)
+
+  clustres <- data.frame(samID = rownames(data[[1]]),
+                         clust = clust.SNF,
+                         row.names = rownames(data[[1]]),
+                         stringsAsFactors = FALSE)
+  #clustres <- clustres[order(clustres$clust),]
+
+  return(list(fit = W, clust.res = clustres, mo.method = "SNF"))
+}