--- a
+++ b/R/DIscBIO-generic-ClassVectoringDT.R
@@ -0,0 +1,100 @@
+#' @title Generating a class vector to be used for the decision tree analysis.
+#' @description This function generates a class vector for the input dataset so
+#'   the decision tree analysis can be implemented afterwards.
+#' @param object \code{DISCBIO} class object.
+#' @param Clustering Clustering has to be one of the following: ["K-means",
+#'   "MB"]. Default is "K-means"
+#' @param K A numeric value of the number of clusters.
+#' @param First A string vector showing the first target cluster.  Default is
+#'   "CL1"
+#' @param Second A string vector showing the second target cluster.  Default is
+#'   "CL2"
+#' @param sigDEG A data frame of the differentially expressed genes (DEGs)
+#'   generated by running "DEGanalysis()" or "DEGanalysisM()".
+#' @param quiet If `TRUE`, suppresses intermediary output
+#' @return A data frame.
+setGeneric(
+  "ClassVectoringDT",
+  function(object, Clustering = "K-means", K, First = "CL1", Second = "CL2",
+           sigDEG, quiet = FALSE) {
+    standardGeneric("ClassVectoringDT")
+  }
+)
+
+#' @rdname ClassVectoringDT
+#' @export
+setMethod(
+  "ClassVectoringDT",
+  signature = "DISCBIO",
+  definition = function(
+    object, Clustering = "K-means", K, First = "CL1", Second = "CL2", sigDEG,
+    quiet = FALSE
+  ) {
+    if (!(Clustering %in% c("K-means", "MB"))) {
+      stop("Clustering has to be either K-means or MB")
+    }
+    if (length(sigDEG[, 1]) < 1) {
+      stop(
+        "run DEGanalysis or DEGanalysis2clust ",
+        "before running ClassVectoringDT"
+      )
+    }
+
+    if (Clustering == "K-means") {
+      Cluster_ID <- object@cpart
+    }
+
+    if (Clustering == "MB") {
+      Cluster_ID <- object@MBclusters$clusterid
+    }
+    Obj <- object@expdata
+    SC <- DISCBIO(Obj)
+    SC <- Normalizedata(SC)
+    DatasetForDT <- SC@fdata
+    Nam <- colnames(DatasetForDT)
+    num <- 1:K
+    num1 <- paste("CL", num, sep = "")
+    for (n in num) {
+      Nam <- ifelse((Cluster_ID == n), num1[n], Nam)
+    }
+    colnames(DatasetForDT) <- Nam
+    chosenColumns <- which(
+      colnames(DatasetForDT) == First |
+        colnames(DatasetForDT) == Second
+    )
+    sg1 <- DatasetForDT[, chosenColumns]
+    dim(sg1)
+    # Creating a dataset that includes only the DEGs
+    gene_list <- sigDEG[, 1]
+    gene_names <- rownames(DatasetForDT)
+    idx_genes <- is.element(gene_names, gene_list)
+    gene_names2 <- gene_names[idx_genes]
+    DEGsfilteredDataset <- sg1[gene_names2, ]
+    if (!quiet) {
+      message(
+        "The DEGs filtered normalized dataset contains:\n",
+        "Genes: ", length(DEGsfilteredDataset[, 1]), "\n",
+        "cells: ", length(DEGsfilteredDataset[1, ])
+      )
+    }
+    G_list <- sigDEG
+    genes <- rownames(DEGsfilteredDataset)
+    DATAforDT <- cbind(genes, DEGsfilteredDataset)
+
+    DATAforDT <- merge(DATAforDT, G_list, by.x = "genes", by.y = "DEGsE")
+    DATAforDT
+    DATAforDT[, 1] <- DATAforDT[, length(DATAforDT[1, ])]
+    DATAforDT <- DATAforDT[!duplicated(DATAforDT[, 1]), ]
+
+    rownames(DATAforDT) <- DATAforDT[, 1]
+    DATAforDT <- DATAforDT[, c(-1, -length(DATAforDT[1, ]))]
+    sg <- factor(gsub(
+      paste0("(", First, "|", Second, ").*"),
+      "\\1",
+      colnames(DATAforDT)
+    ), levels = c(paste0(First), paste0(Second)))
+    sg <- sg[!is.na(sg)]
+    colnames(DATAforDT) <- sg
+    return(DATAforDT)
+  }
+)