#' @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"))

}