#' @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)

  }

)