#' @name getMoCluster

#' @title Get subtypes from MoCluster

#' @description This function wraps the MoCluster (Multiple omics data integrative clustering) algorithm and provides standard output for `getMoHeatmap()` and `getConsensusMOIC()`.

#' @param data List of matrices.

#' @param N.clust Number of clusters.

#' @param ncomp An integer value to indicate the number of components to calculate. To calculate more components requires longer computational time.

#' @param method A string value can be one of CPCA, GCCA and MCIA; CPCA by default.

#' @param option A string value could be one of c('lambda1', 'inertia', 'uniform') to indicate how the different matrices should be normalized.

#' @param k A numeric value to indicate the absolute number (if k >= 1) or the proportion (if 0 < k < 1) of non-zero coefficients for the variable loading vectors. It could be a single value or a vector has the same length as x so the sparsity of individual matrix could be different.

#' @param center A logical value to indicate if the variables should be centered. TRUE by default.

#' @param scale A logical value to indicate if the variables should be scaled. TRUE by default.

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

#' @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[mogsa]{mbpca}.

#'

#'         \code{clust.res}  a data.frame storing sample ID and corresponding clusters.

#'

#'         \code{feat.res}   the results of features selection process.

#'

#'         \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 mogsa

#' @importFrom dplyr %>%

#' @references Meng C, Helm D, Frejno M, Kuster B (2016). moCluster: Identifying Joint Patterns Across Multiple Omics Data Sets. J Proteome Res, 15(3):755-765.

getMoCluster <- function(data       = NULL,

                         N.clust    = NULL,

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

                         ncomp      = NULL,

                         method     = "CPCA",

                         option     = "lambda1",

                         k          = 10,

                         center     = TRUE,

                         scale      = TRUE,

                         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.')

  }

  useless.argument <- type

  if(!is.element(method, c("CPCA","GCCA","MCIA"))) {

    stop("method should be one of CPCA [consensus PCA], GCCA [generalized canonical correlation analysis], or MCIA [multiple co-inertia analysis]!")

  }

  if(is.null(ncomp)) {

    ncomp = N.clust

  }

  moas <- data %>% mogsa::mbpca(ncomp      = ncomp,

                                k          = k,

                                method     = switch(method,

                                                    "CPCA" = "globalScore",

                                                    "GCCA" = "blockScore",

                                                    "MCIA" = "blockLoading"),

                                option     = option,

                                center     = center,

                                scale      = scale,

                                moa        = TRUE,

                                svd.solver = "fast",

                                maxiter    = 1000,

                                verbose    = FALSE)

  scrs <- moas %>% moaScore

  dist <- scrs %>% 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),]

  message("clustering done...")

  featres <- moas@loading[which(moas@loading[,1] != 0),]

  f <- sub('_[^_]*$', '', rownames(featres))

  d <- sub('.*_', '', rownames(featres))

  featres <- data.frame(feature = f,

                        dataset = d,

                        load = featres[,1],

                        stringsAsFactors = FALSE)

  feat.res <- NULL

  for (d in unique(featres$dataset)) {

    tmp <- featres[which(featres$dataset == d),]

    feat.res <- rbind.data.frame(feat.res,tmp)

  }

  message("feature selection done...")

  return(list(fit = moas, clust.res = clustres, feat.res = feat.res, clust.dend = clust.dend, mo.method = "MoCluster"))

}