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+++ b/R/DIscBIO-generic-Clustexp.R
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+#' @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)
+  }
+)