#' @name getLRAcluster

#' @title Get subtypes from LRAcluster

#' @description This function wraps the LRAcluster (Integrated cancer omics data anlsysi by low rank approximation) algorithm and provides standard output for `getMoHeatmap()` and `getConsensusMOIC()`.

#' @param data List of matrices.

#' @param N.clust Number of clusters.

#' @param clusterAlg A string value to indicate the cluster algorithm for similarity matrix; 'ward.D' by default.

#' @param type Data type corresponding to the list of matrics, which can be gaussian, binomial or possion; 'gaussian' by default.

#' @return A list with the following components:

#'

#'         \code{fit}        an object returned by \link[LRAcluster]{LRAcluster}.

#'

#'         \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.

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

#' @importFrom dplyr %>%

#' @export

#' @references Wu D, Wang D, Zhang MQ, Gu J (2015). Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification. BMC Genomics, 16(1):1022.

getLRAcluster <- function(data       = NULL,

                          N.clust    = NULL,

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

                          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.')

  }

  data <- lapply(data, as.matrix)

  if(is.element("binomial",type)) {

    bindex <- which(type == "binomial")

    for (i in bindex) {

      a <- which(rowSums(data[[i]]) == 0)

      b <- which(rowSums(data[[i]]) == ncol(data[[i]]))

      if(length(a) > 0) {

        data[[i]] <- data[[i]][which(rowSums(data[[i]]) != 0),] # remove all zero

      }

      if(length(b) > 0) {

        data[[i]] <- data[[i]][which(rowSums(data[[i]]) != ncol(data[[i]])),] # remove all one

      }

      if(length(a) + length(b) > 0) {

        message(paste0("--", names(data)[i],": a total of ",length(a) + length(b), " features were removed due to the categories were not equal to 2!"))

      }

    }

    type[bindex] <- "binary"

  }

  fit <- LRAcluster(data, dimension = N.clust, types = as.list(type))

  dist <- fit$coordinate %>% t %>% 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),]

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

}