#' @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)

  }

)