R/cytoPlotDatabase3d.R

In optimalFlow: optimalFlow

#' cytoPlotDatabase3d
#'
#' A plot3d wrapper for a database (list) of cytometries as a mixture of multivariate normals as used in optimalFlowTemplates.
#'
#' @param database.cytometries.as.mixtures A list where each component is a mixture distribution. That is, each component is a list, where each element contains the parameters of a component of the mixture as a list with entries: mean, cov, weight and type.
#' @param dimensions A vector containing the two variables on which to perform the projection.
#' @param xlim the x limits (x1, x2) of the plot. Note that x1 > x2 is allowed and leads to a ‘reversed axis’. The default value, NULL, indicates that the range of the finite values to be plotted should be used.
#' @param ylim the y limits of the plot.
#' @param zlim the z limits of the plot.
#' @param xlab a label for the x axis, defaults to a description of x.
#' @param ylab a label for the y axis, defaults to a description of y.
#' @param zlab a label for the z axis, defaults to a description of z.
#' @param colour If TRUE plots elements of a mixture distribution in different colours. If FALSE plots them in black.
#'
#' @return A three dimensional plot of ellipsoids containing the 95% of probability for the respectives components of the mixture distributions.
#' @examples
#' database <- buildDatabase(
#'   dataset_names = paste0('Cytometry', c(2:5, 7:9, 12:17, 19, 21)),
#'   population_ids = c('Monocytes', 'CD4+CD8-', 'Mature SIg Kappa', 'TCRgd-'))
#' templates.optimalFlow <-
#'   optimalFlowTemplates(
#'     database = database, templates.number = 5, cl.paral = 1
#'   )
#' # # To execute requires an actual monitor since it uses rgl.
#' # cytoPlotDatabase3d(templates.optimalFlow$database.elliptical[which(templates.optimalFlow$clustering == 3)], dimensions = c(4,3), xlim = c(0, 8000), ylim = c(0, 8000), zlim = c(0, 8000), xlab = "", ylab = "", ylab = c(0, 8000))
#'
#' @import rgl
#' @export
cytoPlotDatabase3d <- function (database.cytometries.as.mixtures, dimensions = c(1, 2, 3), xlim = c(0, 8000),
                                ylim = c(0, 8000), zlim = c(0, 8000), xlab = "", ylab = "", zlab = "", colour = TRUE){
  n.cytometries <- length(database.cytometries.as.mixtures)
  cytometry.as.mixture <- database.cytometries.as.mixtures[[1]]
  n.cells <- length(cytometry.as.mixture)
  rgl::plot3d(rgl::ellipse3d(cytometry.as.mixture[[1]]$
                               cov[dimensions, dimensions],
                             centre = cytometry.as.mixture[[1]]$
                               mean[dimensions]),
              xlim = xlim, ylim = ylim, zlim = zlim, alpha = 0.5,
              col = 1, xlab = xlab, ylab = ylab,
              zlab = zlab)
  if (colour) {
    for (j in 2:n.cells){
      rgl::plot3d(rgl::ellipse3d(cytometry.as.mixture[[j]]$cov[dimensions, dimensions],
                                 centre = cytometry.as.mixture[[j]]$
                                   mean[dimensions]), alpha = 0.5, add = TRUE, col = j)
    }
    for (i in 2:n.cytometries){
      cytometry.as.mixture <- database.cytometries.as.mixtures[[i]]
      n.cells <- length(cytometry.as.mixture)
      for (j in 1:n.cells){
        rgl::plot3d(rgl::ellipse3d(cytometry.as.mixture[[j]]$cov[dimensions, dimensions],
                                   centre = cytometry.as.mixture[[j]]$
                                     mean[dimensions]), alpha = 0.5, add = TRUE, col = j)
      }
    }
  } else {
    for (j in 2:n.cells){
      rgl::plot3d(rgl::ellipse3d(cytometry.as.mixture[[j]]$cov[dimensions, dimensions],
                                 centre = cytometry.as.mixture[[j]]$
                                   mean[dimensions]), alpha = 0.5, add = TRUE, col = 1)
    }
    for (i in 2:n.cytometries){
      cytometry.as.mixture <- database.cytometries.as.mixtures[[i]]
      n.cells <- length(cytometry.as.mixture)
      for (j in 1:n.cells){
        rgl::plot3d(rgl::ellipse3d(cytometry.as.mixture[[j]]$cov[dimensions, dimensions],
                                   centre = cytometry.as.mixture[[j]]$
                                     mean[dimensions]), alpha = 0.5, add = TRUE, col = 1)
      }
    }
  }
}