#' @name getMOIC

#' @title Get subtypes from multi-omics integrative clustering

#' @description Using `getMOIC()`, users can choose one out of the ten algorithms embedded in `MOVICS`. Users can implement multi-omics clustering in a simplest way of which the only requirement is to specify and at least specify a list of matrices (argument of `data`), a number of cluster (argument of `N.clust`), and clustering method (argument of `methodslist`) in `getMOIC()`. It is possible to pass various arguments that are specific to each method. Of course, users can also directly call different algorithms by using functions start with `get` and end with the name of the algorithm (e.g., `getSNF`; please refer to `?get%algorithm_name%` for more details about the editable arguments)

#' @param data List of matrices (Maximum number of matrices is 6).

#' @param methodslist A string list specifying one or multiple methods to run (See Details).

#' @param N.clust Number of clusters.

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

#' @param ... Additionnal parameters for each method (only works when only one method chosen)

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

#' @export

#' @return A list of results returned by each specified algorithms.

#' @import SNFtool

#' @import IntNMF

#' @import mogsa

#' @import coca

#' @import iClusterPlus

#' @import CIMLR

#' @import PINSPlus

#' @import ConsensusClusterPlus

#' @details

#' Method for integrative clustering will be chosed according to the value of argument 'methodslist':

#'

#' If \code{methodslist == "IntNMF"}, Integrative clustering methods using Non-Negative Matrix Factorization

#'

#' If \code{methodslist == "SNF"}, Similarity network fusion.

#'

#' If \code{methodslist == "LRAcluster"}, Integrated cancer omics data analysis by low rank approximation.

#'

#' If \code{methodslist == "PINSPlus"}, Perturbation Clustering for data integration and disease subtyping

#'

#' If \code{methodslist == "ConsensusClustering"}, Consensus clustering

#'

#' If \code{methodslist == "NEMO"}, Neighborhood based multi-omics clustering

#'

#' If \code{methodslist == "COCA"}, Cluster Of Clusters Analysis

#'

#' If \code{methodslist == "CIMLR"}, Cancer Integration via Multikernel Learning (Support Feature Selection)

#'

#' If \code{methodslist == "MoCluster"}, Identifying joint patterns across multiple omics data sets (Support Feature Selection)

#'

#' If \code{methodslist == "iClusterBayes"}, Integrative clustering of multiple genomic data by fitting a Bayesian latent variable model (Support Feature Selection)

#'

#' @references

#' Pierre-Jean M, Deleuze J F, Le Floch E, et al. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration[J]. Briefings in Bioinformatics, 2019.

#'

#' intNMF:

#' Chalise P, Fridley BL. Integrative clustering of multi-level omic data based on non-negative matrix factorization algorithm. PLoS One. 2017;12(5):e0176278.

#'

#' iClusterBayes:

#' Mo Q, Shen R, Guo C, Vannucci M, Chan KS, Hilsenbeck SG. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics. 2018;19(1):71-86.

#'

#' SNF:

#' Wang B, Mezlini AM, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014;11(3):333-337.

#'

#' Mocluster:

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

#'

#' LRAcluster:

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

#'

#' CIMLR:

#' Ramazzotti D, Lal A, Wang B, Batzoglou S, Sidow A. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat Commun. 2018;9(1):4453.

#'

#' PINSPlus:

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

#'

#' ConsensusClustering:

#' Monti S, Tamayo P, Mesirov J, et al. Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning. 2003;52:91-118.

#'

#' NEMO:

#' Rappoport N, Shamir R. NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics. 2019;35(18):3348-3356.

#'

#' COCA:

#' Hoadley KA, Yau C, Wolf DM, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014;158(4):929-944.

getMOIC <- function(data        = NULL,

                    methodslist = list("SNF", "CIMLR", "PINSPlus", "NEMO", "COCA", "MoCluster", "LRAcluster", "ConsensusClustering", "IntNMF", "iClusterBayes"),

                    N.clust     = NULL,

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

                    ...){

  # check argument

  if (!is.list(data)) {

    stop("data is not a list!")

  }

  n_dat <- length(data)

  if(n_dat > 6){

    stop('current verision of MOVICS can support up to 6 omics data.')

  }

  if(n_dat < 2){

    stop('current verision of MOVICS needs at least 2 omics data.')

  }

  if(is.null(names(data))){

    names(data) <- sprintf("dat%s", 1:length(data))

  }

  num.methods <- length(unlist(methodslist))

  if(is.vector(methodslist)) {methodslist <- as.list(methodslist)}

  if(!all(is.element(unlist(methodslist), c("SNF", "CIMLR", "PINSPlus", "NEMO", "COCA", "MoCluster", "LRAcluster", "ConsensusClustering", "IntNMF", "iClusterBayes")))) {

    stop("current version of MOVICS supports 10 algorithms. Allowed values contain c('SNF', 'CIMLR', 'PINSPlus', 'NEMO', 'COCA', 'MoCluster', 'LRAcluster', 'ConsensusClustering', 'IntNMF', 'iClusterBayes').")

  }

  if(num.methods > 1) {

    message("--you choose more than 1 algorithm and all of them shall be run with parameters by default.")

  }

  # Check dimension

  if(max(sapply(data, dim)[2,]) != min(sapply(data, dim)[2,])){

    message(sprintf("number of samples in dat %s is %s\n", 1:length(data), sapply(data, dim)[2,]))

    stop("data do not contain the same number of samples!")

  }

  reslist <- list()

  for (method in unlist(methodslist)) {

    doMOIC <- switch(method,

                     "IntNMF"              = getIntNMF,

                     "iClusterBayes"       = getiClusterBayes,

                     "SNF"                 = getSNF,

                     "MoCluster"           = getMoCluster,

                     "LRAcluster"          = getLRAcluster,

                     "CIMLR"               = getCIMLR,

                     "PINSPlus"            = getPINSPlus,

                     "ConsensusClustering" = getConsensusClustering,

                     "NEMO"                = getNEMO,

                     "COCA"                = getCOCA

    )

    reslist[[method]] <- doMOIC(data, N.clust, type, ...)

    message(paste0(method," done..."))

  }

  if(num.methods == 1) {

    return(reslist[[1]])

  } else {

    return(reslist)

  }

}