#' @title tSNE map with labels

#' @description Visualizing k-means or model-based clusters using tSNE maps

#' @param object \code{DISCBIO} class object.

#' @rdname plotLabelstSNE

#' @importFrom graphics text

#' @return Plot containing the ID of the cells in each cluster

setGeneric("plotLabelstSNE", function(object) {

  standardGeneric("plotLabelstSNE")

})

#' @rdname plotLabelstSNE

#' @export

setMethod(

  "plotLabelstSNE",

  signature = "DISCBIO",

  definition = function(object) {

    # ======================================================================

    # Validating

    # ======================================================================

    ran_k <- length(object@tsne) > 0

    ran_m <- length(object@MBtsne) > 0

    if (ran_k) {

      Clusters <- object@kmeans$kpart

      x <- object@tsne

    } else if (ran_m) {

      Clusters <- object@MBclusters$clusterid

      x <- object@MBtsne

    } else {

      stop("run comptsne before plotLabelstSNE")

    }

    # ======================================================================

    # Plotting

    # ======================================================================

    ClustersFactor <- as.factor(Clusters)

    ClustersFactor <- gsub("1", "black", ClustersFactor)

    ClustersFactor <- gsub("2", "blue", ClustersFactor)

    ClustersFactor <- gsub("3", "green", ClustersFactor)

    ClustersFactor <- gsub("4", "red", ClustersFactor)

    ClustersFactor <- gsub("5", "yellow", ClustersFactor)

    ClustersFactor <- gsub("6", "gray", ClustersFactor)

    COL <- ClustersFactor

    labels <- names(object@ndata)

    plot(

      x,

      xlab = "Dim 1",

      ylab = "Dim 2",

      pch = 20,

      cex = .5,

      col = "lightgrey"

    )

    text(

      x[, 1],

      x[, 2],

      labels,

      cex = .7,

      col = COL

    )

  }

)