#' @name getConsensusClustering

#' @title Get subtypes from ConsensusClustering

#' @description This function wraps the Consensus Clustering algorithm and provides standard output for `getMoHeatmap()` and `getConsensusMOIC()`.

#' @param data List of matrices.

#' @param N.clust Number of clusters.

#' @param norMethod A string vector indicate the normalization method for consensus clustering.

#' @param reps An integer value to indicate the number of subsamples.

#' @param pItem A numerical value to indicate the proportion of items to sample.

#' @param pFeature A numerical value to indicate the proportion of features to sample.

#' @param clusterAlg A string value to indicate the cluster algorithm.

#' @param innerLinkage A string value to indicate the heirachical linakge method for subsampling.

#' @param finalLinkage A string value to indicate the heirarchical method for consensus matrix.

#' @param distance A string value to indicate the distance function.

#' @param plot A string value to indicate the output format for heatmap.

#' @param writeTable A logical value to indicate if writing output and log to csv.

#' @param title A string value for output directory.

#' @param seed A numerical value to set random seed for reproducible results.

#' @param verbose A logical value to indicate if printing messages to the screen to indicate progress.

#' @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[ConsensusClusterPlus]{ConsensusClusterPlus}.

#'

#'         \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.

#' @export

#' @examples # There is no example and please refer to vignette.

#' @import ConsensusClusterPlus

#' @importFrom dplyr %>%

#' @references Monti S, Tamayo P, Mesirov J, et al (2003). Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Mach Learn, 52:91-118.

getConsensusClustering <- function(data         = NULL,

                                   N.clust      = NULL,

                                   type         = rep("gaussian", length(data)),

                                   norMethod    = "none",

                                   reps         = 500,

                                   pItem        = 0.8,

                                   pFeature     = 0.8,

                                   clusterAlg   = "hc",

                                   innerLinkage = "ward.D",

                                   finalLinkage = "ward.D",

                                   distance     = "pearson",

                                   plot         = NULL,

                                   writeTable   = F,

                                   title        = file.path(getwd(),"consensuscluster"),

                                   seed         = 123456,

                                   verbose      = F){

  # 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(is.null(norMethod)) {

    d <- do.call(rbind, data)

  } else {

    if(!is.element(norMethod, c("median-centered","mean-centered","z-score","none"))) {

      stop("the normalized method should be one of median-centered, mean-centered, z-score or none!")

    }

    if(norMethod == "median-centered") {

      d <- do.call(rbind, data)

      d <- sweep(d,1, apply(d,1,median,na.rm=T))

    }

    if(norMethod == "mean-centered") {

      d <- do.call(rbind, data)

      d <- sweep(d,1, apply(d,1,mean,na.rm=T))

    }

    if(norMethod == "z-score") {

      d <- do.call(rbind, data)

      d <- t(scale(t(d)))

    }

    if(norMethod == "none") {

      d <- do.call(rbind, data)

    }

  }

    fit <-  ConsensusClusterPlus(d            = as.matrix(d),

                                 maxK         = ifelse(N.clust == 2, 3, N.clust), # cannot set as 2

                                 reps         = reps,

                                 pItem        = pItem,

                                 pFeature     = pFeature,

                                 clusterAlg   = clusterAlg,

                                 innerLinkage = innerLinkage,

                                 finalLinkage = finalLinkage,

                                 distance     = distance,

                                 seed         = seed,

                                 verbose      = verbose,

                                 plot         = plot,

                                 writeTable   = writeTable,

                                 title        = title)

  res <- fit[[N.clust]]

  clustres <- data.frame(samID = colnames(data[[1]]),

                         clust = as.numeric(res$consensusClass),

                         row.names = colnames(data[[1]]),

                         stringsAsFactors = F)

  #clustres <- clustres[order(clustres$clust),]

  return(list(fit = fit, clust.res = clustres, clust.dend = res$consensusTree, mo.method = "ConsensusClustering"))

}