#' @name getPINSPlus

#' @title Get subtypes from PINSPlus

#' @description This function wraps the PINSPlus (Perturbation Clustering for data INtegration and disease Subtyping) algorithm and provides standard output for `getMoHeatmap()` and `getConsensusMOIC()`.

#' @param data List of matrices.

#' @param N.clust Number of clusters

#' @param clusteringMethod The name of built-in clustering algorithm that PerturbationClustering will use. Currently supported algorithm are kmeans, pam and hclust. Default value is "kmeans".

#' @param iterMin The minimum number of iterations. Default value is 50

#' @param iterMax The maximum number of iterations. Default value is 500.

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

#' @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[PINSPlus]{PerturbationClustering}.

#'

#'         \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.

#' @importFrom PINSPlus PerturbationClustering

#' @importFrom dplyr %>%

#' @references Nguyen H, Shrestha S, Draghici S, Nguyen T (2019). PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics, 35(16):2843-2846.

getPINSPlus <- function(data             = NULL,

                        N.clust          = NULL,

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

                        norMethod        = "none",

                        clusteringMethod = "kmeans",

                        iterMin          = 50,

                        iterMax          = 500){

  # 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 = TRUE))

    }

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

      d <- do.call(rbind, data)

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

    }

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

      d <- do.call(rbind, data)

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

    }

    if(norMethod == "none") {

      d <- do.call(rbind, data)

    }

  }

  if(!is.element(clusteringMethod, c("kmeans", "hclust", "pam"))) {

    stop("clusteringMethod should be one of kmeans, hclust, or pam!")

  }

  data <- t(d)

  # for multi-omics but cannot determine cluster number

  # fit <- SubtypingOmicsData(data,

  #                           kMin = N.clust,

  #                           kMax = N.clust,

  #                           clusteringMethod = clusteringMethod,

  #                           iterMin = iterMin,

  #                           iterMax = iterMax,

  #                           verbose = T)

  # for one "feature" but can determine cluster number

  fit <- PerturbationClustering(data             = data,

                                kMin             = N.clust,

                                kMax             = N.clust,

                                clusteringMethod = clusteringMethod,

                                iterMin          = iterMin,

                                iterMax          = iterMax,

                                verbose          = TRUE)

  clustres <- data.frame(samID = rownames(data),

                         clust = fit$cluster,

                         row.names = rownames(data),

                         stringsAsFactors = FALSE)

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

  return(list(fit = fit, clust.res = clustres, mo.method = "PINSPlus"))

}