#' @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"))

}