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+++ b/R/getPINSPlus.R
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+#' @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"))
+}