R/plot-methods.R

In flowViz: Visualization for flow cytometry

## ==========================================================================
## a simple plot method without strange plot parameter and friends. It does
## the most intuitive thing: take a flowFrame and do a pairs plot if there
## are more than 2 parameters. Do a smoothScatter plot for exactly 2
## parameters and a histogram for exactly one.
## If you want to plot specific columns, subset the frame before
## plotting or specify them in the y argument.
## We record parameters of the plot in the internal flowViz.state environment
## which will later be useful to figure out the plotting parameters when
## adding gate outlines to a plot
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

## helper function to do the actual plotting
fplot <- function(x, smooth, pch, xlim, ylim = NULL, ...)
{
    values <- exprs(x)
    sel <- tolower(colnames(values)) != "time"
#    flowViz.state[["plotted"]] <- TRUE
    if(missing(pch))
        pch <- "."
    l <- ncol(values)
    if(l==1){
        if (missing(xlim))
            xlim <- unlist(range(x[,1]))
        hist(values, xlab=colnames(x), xlim = xlim, ylim = ylim, ...)
#        flowViz.state[["type"]] <- "hist"
    }
    else if (l==2) {
        if (missing(xlim))
            xlim <- unlist(range(x[,1]))
        if (is.null(ylim))
            ylim <- unlist(range(x[,2]))
        if (smooth) {
            smoothScatter(values, pch=pch, xlim=xlim, ylim=ylim, ...)
#            flowViz.state[["type"]] <- "smooth"
        }else{
            plot(values, pch=pch, xlim=xlim, ylim=ylim, ...)
#            flowViz.state[["type"]] <- "dot"
        }
    } else {
        if(smooth) {
            x <- x[,sel]
            print(splom(x, pch=pch, ...))
#            flowViz.state[["type"]] <- "splom"
        }
        else {
            pairs(values[,sel], pch=pch, ...)
#            flowViz.state[["type"]] <- "pairs"
        }
    }
#    flowViz.state[["parameters"]] <- colnames(values)
}


#' Very basic plotting of flowFrames
#' 
#' A basic method to plot \code{\link[flowCore:flowFrame-class]{flowFrame}}
#' objects. Depending on the number of dimensions, different types of plots are
#' generates. See below for details.
#' 
#' 
#' Basic plots for \code{flowFrame} objects. If the object has only a single
#' parameter this produces a \code{\link[graphics:hist]{histogram}}. For
#' exactly two parameters we plot a bivariate density map (see
#' \code{\link[graphics]{smoothScatter}}) and for more than two parameters we
#' produce a simple \code{\link[lattice]{splom}} plot. To select specific
#' parameters from a \code{flowFrame} for plotting, either subset the object or
#' specify the parameters as a character vector in the second argument to
#' \code{plot}. The \code{smooth} parameters lets you toggle between
#' density-type \code{\link[graphics]{smoothScatter}} plots and regular scatter
#' or pairs plots.  For far more sophisticated plotting of flow cytometry data,
#' see the lattice-style plot methods provided by this package.
#' 
#' @param ... other arguments 
#' 
#' @name plot
#' 
#' @author F. Hahne
#' @seealso
#' 
#' \code{\link[flowViz]{xyplot}},
#' \code{\link[flowCore:flowFrame-class]{flowFrame}},
#' \code{\link[flowViz]{densityplot}}
#' @keywords methods
#' @importFrom stats4 plot
#' @export 
#' @aliases plot,flowFrame,missing-method
setMethod("plot", signature(x="flowFrame", y="missing"),
          function(x, y, smooth=TRUE, ...)
      {
          fplot(x, smooth=smooth, ...)
          return(invisible(NULL))
      })

#' @export 
#' @rdname plot
#' @param x flowFrame
#' @param y (optional) channel names
#' @param smooth logical
#' @param pch point type
setMethod("plot",signature(x="flowFrame", y="character"),
          function(x, y, smooth=TRUE, pch, ...)
      {
          if(!all(y %in% colnames(x)))
              stop("subset out of bounds", call.=FALSE)
          x <- x[,y]
          fplot(x, smooth=smooth, pch=pch, ...)
          return(invisible(NULL))
      })