#' @title Clustering of single-cell transcriptome data
#' @description This functions performs the initial clustering of the RaceID
#'   algorithm.
#' @docType methods
#' @param object \code{DISCBIO} class object.
#' @param clustnr Maximum number of clusters for the derivation of the cluster
#'   number by the saturation of mean within-cluster-dispersion. Default is 20.
#' @param bootnr A numeric value of booststrapping runs for \code{clusterboot}.
#'   Default is 50.
#' @param metric Is the method to transform the input data to a distance object.
#'   Metric has to be one of the following: ["spearman", "pearson", "kendall",
#'   "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"].
#' @param do.gap A logical vector that allows generating the number of clusters
#'   based on the gap statistics. Default is TRUE.
#' @param SE.method The SE.method determines the first local maximum of the gap
#'   statistics. The SE.method has to be one of the following:["firstSEmax",
#'   "Tibs2001SEmax", "globalSEmax", "firstmax", "globalmax"]. Default is
#'   "Tibs2001SEmax"
#' @param SE.factor A numeric value of the fraction of the standard deviation by
#'   which the local maximum is required to differ from the neighboring points
#'   it is compared to. Default is 0.25.
#' @param B.gap Number of bootstrap runs for the calculation of the gap
#'   statistics. Default is 50
#' @param cln Number of clusters to be used. Default is \code{NULL} and the
#'   cluster number is inferred by the saturation criterion.
#' @param rseed Random integer to enforce reproducible clustering results.
#' @param quiet if `TRUE`, intermediate output is suppressed
#' @importFrom stats as.dist cor kmeans
#' @importFrom cluster clusGap maxSE
#' @importFrom graphics pairs
#' @importFrom methods is
#' @return The DISCBIO-class object input with the cpart slot filled.
#' @examples
#' sc <- DISCBIO(valuesG1msTest) # changes signature of data
#' sc <- Clustexp(sc, cln = 2)
setGeneric("Clustexp", function(
    object, clustnr = 3, bootnr = 50,
    metric = "pearson", do.gap = TRUE, SE.method = "Tibs2001SEmax",
    SE.factor = .25, B.gap = 50, cln = 0, rseed = NULL, quiet = FALSE) {
  standardGeneric("Clustexp")
})

#' @export
#' @rdname Clustexp
setMethod(
  f = "Clustexp",
  signature = "DISCBIO",
  definition = function(object, clustnr, bootnr, metric, do.gap, SE.method,
                        SE.factor, B.gap, cln, rseed, quiet) {
    if (!is.numeric(clustnr)) {
      stop("clustnr has to be a positive integer")
    } else if (round(clustnr) != clustnr | clustnr <= 0) {
      stop("clustnr has to be a positive integer")
    }
    if (!is.numeric(bootnr)) {
      stop("bootnr has to be a positive integer")
    } else if (round(bootnr) != bootnr | bootnr <= 0) {
      stop("bootnr has to be a positive integer")
    }
    if (!(
      metric %in% c(
        "spearman", "pearson", "kendall", "euclidean", "maximum",
        "manhattan", "canberra", "binary", "minkowski"
      )
    )) {
      stop(
        "metric has to be one of the following: spearman, ",
        "pearson, kendall, euclidean, maximum, manhattan, ",
        "canberra, binary, minkowski"
      )
    }
    if (!(
      SE.method %in% c(
        "firstSEmax", "Tibs2001SEmax", "globalSEmax", "firstmax",
        "globalmax"
      )
    )) {
      stop(
        "SE.method has to be one of the following: ",
        "firstSEmax, Tibs2001SEmax, globalSEmax, ",
        "firstmax, globalmax"
      )
    }
    if (!is.numeric(SE.factor)) {
      stop("SE.factor has to be a non-negative integer")
    } else if (SE.factor < 0) {
      stop("SE.factor has to be a non-negative integer")
    }
    if (!(is.numeric(do.gap) | is.logical(do.gap))) {
      stop("do.gap has to be logical (TRUE or FALSE)")
    }
    if (!is.numeric(B.gap)) {
      stop("B.gap has to be a positive integer")
    } else if (round(B.gap) != B.gap | B.gap <= 0) {
      stop("B.gap has to be a positive integer")
    }
    if (!is.numeric(cln)) {
      stop("cln has to be a non-negative integer")
    } else if (round(cln) != cln | cln < 0) {
      stop("cln has to be a non-negative integer")
    }
    if (!is.null(rseed) & !is.numeric(rseed)) {
      stop("rseed has to be numeric or NULL")
    }
    if (!do.gap & cln == 0) {
      stop("cln has to be a positive integer or do.gap has to be TRUE")
    }

    # Operations
    object@clusterpar <-
      list(
        clustnr = clustnr,
        bootnr = bootnr,
        metric = metric,
        do.gap = do.gap,
        SE.method = SE.method,
        SE.factor = SE.factor,
        B.gap = B.gap,
        cln = cln,
        rseed = rseed
      )
    y <- clustfun(
      object@fdata,
      clustnr,
      bootnr,
      metric,
      do.gap,
      SE.method,
      SE.factor,
      B.gap,
      cln,
      rseed = rseed,
      quiet = quiet
    )
    object@kmeans <- list(
      kpart = y$clb$result$partition,
      jaccard = y$clb$bootmean,
      gap = y$gpr
    )
    object@distances <- as.matrix(y$di)
    object@fcol <- rainbow(max(y$clb$result$partition))
    object@cpart <- object@kmeans$kpart
    return(object)
  }
)