R/DIscBIO-generic-plotLabelstSNE.R

In DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

#' @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
    )
  }
)