#' @title Pseudo-time ordering based on k-means clusters

#' @description This function takes the exact output of exprmclust function and

#'   construct Pseudo-time ordering by mapping all cells onto the path that

#'   connects cluster centers.

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

#' @param quiet if `TRUE`, suppresses intermediary output

#' @param export if `TRUE`, exports order table to csv

#' @param filename Name of the exported file (if `export=TRUE`)

#' @importFrom TSCAN TSCANorder

#' @note This function has been replaced by pseudoTimeOrdering(), but it is

#'   being kept for legacy purposes. It will, however, be removed from future

#'   versions of DIscBIO.

#' @return The DISCBIO-class object input with the kordering slot filled.

setGeneric("KmeanOrder", function(

    object, quiet = FALSE, export = FALSE,

    filename = "Cellular_pseudo-time_ordering_based_on_k-meansc-lusters") {

  standardGeneric("KmeanOrder")

})

#' @export

#' @rdname KmeanOrder

setMethod(

  "KmeanOrder",

  signature = "DISCBIO",

  definition = function(object, quiet, export, filename) {

    warning(

      "KmeanOrder() has been replaced with pseudoTimeOrdering(), ",

      "which performs pseudo-time ordering for both k-means ",

      "and model-based clustering. ",

      "This function is being kept for legacy purposes, ",

      "but will be removed in future versions of DIscBIO. ",

      "Please adapt your scripts accordingly."

    )

    # Validation

    if (length(object@kmeans$kpart) == 0) {

      stop("run Clustexp before KmeanOrder")

    }

    Obj <- object@fdata

    Clusters <- object@cpart

    sampleNames <- colnames(object@fdata)

    lpsmclust <- Exprmclust(Obj, K = 4, reduce = FALSE, cluster = Clusters)

    lpsorder <- TSCANorder(lpsmclust)

    orderID <- lpsorder

    order <- seq_len(length(lpsorder))

    orderTable <- data.frame(order, orderID)

    if (export) write.csv(orderTable, file = paste0(filename, ".csv"))

    if (!quiet) print(orderTable)

    FinalOrder <- orderTable[match(sampleNames, orderTable$orderID), ]

    out_order <- FinalOrder[, 1]

    names(out_order) <- names(Clusters)

    object@kordering <- out_order

    return(object)

  }

)