xyplot {flowViz} | R Documentation

## Methods implementing Lattice xyplots for flow data.

### Description

These functions create Trellis scatter plots (a.k.a. dot plots in the Flow Cytometry community) from flow cytometry data.

### Usage

    ## Method for 'flowFrame' objects without a formula.
    ## This creates plots of all flow parameters agains
    ## time.
    ## S4 method for signature 'flowFrame, missing':
    xyplot(
        x,
        data,
        time,
        xlab,
        ylab="",
        layout,
        prepanel=prepanel.xyplot.flowframe.time,
        panel=panel.xyplot.flowframe.time,
        type="discrete",
        ...)

    ## prepanel function for time line plots of flowFrames
    prepanel.xyplot.flowframe.time(
        x,
        y,
        frame,
        time,
        ...)

    ## panel function for time line plots of flowFrames
    panel.xyplot.flowframe.time(
        x,
        y,
        frame,
        time,
        type="discrete",
        nrpoints=0,
        binSize=100,
        ...)

    ## method for formulae with 'flowFrame' objects
    ## S4 method for signature 'formula, flowFrame':
    xyplot(
        x,
        data,
        smooth=TRUE,
        prepanel=prepanel.xyplot.flowframe,
        panel=panel.xyplot.flowframe,
        ...)

    ## prepanel function for generic xyplots of flowFrames
    prepanel.xyplot.flowframe(
        frame,
        channel.x.name,
        channel.y.name,
        ...)

    ## panel function for generic xyplots of flowFrames
    panel.xyplot.flowframe(
        x,
        y,
        frame,
        filter=NULL,
        smooth=TRUE,
        margin=TRUE,
        outline=FALSE,
        channel.x.name,
        channel.y.name,
        pch=gpar$flow.symbol$pch,
        alpha=gpar$flow.symbol$alpha,
        cex=gpar$flow.symbol$cex,
        col=gpar$flow.symbol$col,
        gp,
        ...)

    ## method for 'flowSet' objects
    ## S4 method for signature 'formula, flowSet':
    xyplot(
        x,
        data,
        xlab,
        ylab,
        as.table=TRUE,
        prepanel=prepanel.xyplot.flowset,
        panel=panel.xyplot.flowset,
        pch = ".",
        smooth = TRUE,
        filter = NULL,
        par.settings=NULL,
        ...)

    ## prepanel function for generic xyplots of flowSets
    prepanel.xyplot.flowset(
        x,
        frames,
        channel.x.name,
        channel.y.name,
        ...)

    ## panel function for generic xyplots of flowSets
    panel.xyplot.flowset(
        x,
        frames,
        filter=NULL,
        channel.x,
        channel.y,
        ...)

    ## method for various workflow objects
    ## S4 method for signature 'formula, view':
    xyplot(
        x,
        data,
        ...)

    ## S4 method for signature 'view, missing':
    xyplot(
        x,
        data,
        ...)

    ## S4 method for signature 'formula, gateView':
    xyplot(
        x,
        data,
        filter=NULL,
        par.settings,
        ...)

### Arguments

`x` | A formula describing the structure of the plot and the variables to be used in the display. In the `prepanel` and `panel` functions, also the names of `flowFrames` or any of the annotation data columns in the `phenoData` slot.
`data, y, frame` | a `flowSet` or `flowFrame` object that serves as the source of data. For the workflow methods, this can also be various `view` or `actionItem` objects.
`time` | A character string giving the name of the data column recording time. If not provided, we try to guess from the available parameters.
`xlab, ylab` | Labels for data axes, with suitable defaults taken from the formula.
`as.table, layout` | These arguments are passed unchanged to the corresponding methods in lattice, and are listed here only because they provide different defaults. See documentation for the original methods for details.
`type` | type of rendering; see `panel.xyplot` for details. For the basic `flowFrame` method without a detailed formula, the addtional type `discrete` is available, which plots a smoothed average of the flow cytometry values against time.
`nrpoints` | The number of points plotted on the smoothed plot in sparse regions. This is only listed here because we use a different default. See `panel.smoothScatter` for details.
`binSize` | The size of a bin (i.e., the number of events within a bin) used for the smoothed average timeline plots.
`channel.x.name, channel.y.name` | Character strings giving corresponding names used to match filter parameters if applicable.
`smooth` | Logical. If `TRUE`, `panel.smoothScatter` is used to display a partially smoothed version of the data. Otherwise, events are plotted individually, as in a standard scatter plot.
`filter` | A `filter`, `filterResult` or `filterResultList` object or a list of such objects of the same length as the `flowSet`. The appropriate spherical 2D representation of this filter will be superimposed on the plot if `smooth=TRUE`, or the resukt of the filtering operation will be indicated by grouping if `smooth=FALSE` . The software will figure out whether the `filter` needs to be evaluated in order to be plotted (in which case providing a `filterResult` can speed things up considerably).
`margin` | Logical indicating whether to truncate the density estimation on the margins of the measurement range and plot margin events as lines if `smooth=TRUE`. To avoid visual artifacts it is highly recommended to set this option to `TRUE`.
`outline` | Logical, specifying whether to add the boundaries of a gate to the plot when `smooth=FALSE` in addition to the grouping. Defaults to `FALSE`.
`pch, cex, col, alpha` | Graphical parameters used when `smooth=FALSE`. These mostly exist for conveniance and much more control is available throught the `lattice`-like `par.setting` and `flowViz.par.set` customization. See `flowViz.par.set` for details.
`par.settings` | A list of lists of graphical parameters. See `flowViz.par.set` for details.
`gp` | A list of graphical parameters that are passed down to the low level panel functions. This is for internal use only. The public user interface to set graphical parameters is either `par.settings` for customization of a single call or `flowViz.par.set` for customization of session-wide defaults.
`prepanel` | The prepanel function. See `xyplot`.
`panel` | The panel function. See `xyplot`.
`channel.x, channel.y` | Expressions defining the x and y variables in terms of columns in the data. Can involve functions or multiple columns from the data, however this usage is discouraged.
`frames` | An environment containing frame-specific data.
`...` | More arguments, usually passed on to the underlying lattice methods.

### Details

The implementation of `xyplot` in `flowViz` is very close to the original `lattice` version. Concepts like conditioning and the use of panels apply directly to the flow cytometry data. The single fundamental difference is that conditioning variables are not evaluated in the context of the raw data, but rather in the `phenoData` slot environment (only for the `flowSet` methods. Thus, we can directly condition on pheotypic variables like sample groups, patients or treatments.

In the formula interface, the primary and secondary variables (separated by the tilde) have to be valid parameter names. Please note that frequently used variants like `FSC-H` and `SSC-H` are not syntactically correct R symbols, and need to be wrapped in `` ``. E.g., ``FSC-H``. For `flowSets`, the use of a conditioning variable is optional. We implicitely condition on `flowFrames` and the default is to arrange panels by sample names.

### Methods

xyplot
  `signature(x = "flowFrame", data = "missing")`: Creates diagnostic time series plots of flow parameter values against time. These plots are useful to detect quality issues in the raw data. If not provided explicitely via the `tine` argument, the time parameter will be automatically detected. The additional arguments `xlab`, `ylab`, `nrpoints`, and `layout` are only listed because `flowViz` provides different defaults. Internally, they are directly passed on to the underlying `lattice` functions
. Argument `type` can be a combination of any of the types allowed in `lattice xyplots`, or `discrete`, in which case a smoothed average of the parameter against time is plotted. `binSize` controls the binning that is used for the smoothing procedure.
xyplot
  `signature(x = "formula", data = "flowFrame")`: Creates scatter plots (a.k.a. dot plots) of a pair of FCM channels. Depending on the setting of the `smooth` argument, the data will be rendered as a partially smoothed density estimate (`smooth=TRUE`, the default) or as a regular scatter plot with separate points for individual events. The formula interface allows for fairly general plotting, however there are certain limitations on the use of expressions as part of the formulae. Unless you are sure about what you are doing, you should transform the raw data in a separate step using one of the tools in the `flowCore` package rather than inline using the formula interface. The method allows to superimpose gating results though the `filter` argument. If `smooth=TRUE`, we try to add spherical 2D representations of the gates if applicable. For `smooth=FALSE`, gates are indicated by a grouping mechanism using different point shapes or colors (unless `outline` is also `TRUE`, in which case the gate outlines are superimposed in addition to the grouping). Argument `margins` controls how events on the margins of the measurement range are treated. The default (`TRUE`) is to discard them from any density estimation and later add them as separate glyphs. See `flowViz.par.set` for details on controlling graphical parameters in these plots.
xyplot
  `signature(x = "formula", data = "flowSet")`: Scatter plots from a `flowSet` object. We allow for conditioning on variables in the `phenoData` slot of the `flowSet`. All additional arguments that apply to the `flowFrame` method are also valid for `flowSets`.
xyplot
  `signature(x = "formula", data = "view")`: Scatter plots from a `view` object. Depending on the particulars of the view, the method tries to come up with reasonable defaults. Full customization is also available though all the before mentioned arguments.
xyplot
  `signature(x = "view", data = "missing")`: Scatter plots from a `view` object. This is the fallback method in the case of absolutely no customization. It serves as the default plot method for all `views`.
xyplot
  `signature(x = "formula", data = "gateView")`: Scatter plots from a `gateView` object. This allows for customization of the `gateView` plots but still provides reasonable defaults, e.g., for added gates.

### Author(s)

F. Hahne, D. Sarkar

### See Also

Not all standard lattice arguments will have the intended effect, but many should. For a fuller description of possible arguments and their effects, consult documentation on lattice.

### Examples

    data(GvHD)
    GvHD <- GvHD[pData(GvHD)$Patient %in% 5:6]

    ## a bivariate scatterplot
    ## by default ('smooth=TRUE') panel.smoothScatter is used
    xyplot(`FSC-H` ~ `SSC-H`, GvHD[["s5a05"]], nbin = 100,
    main="A single flowFrame")

    ## A non-smooth version of the same data
    xyplot(`FSC-H` ~ `SSC-H`, GvHD[["s5a05"]], nbin = 100,
    main="A single flowFrame", smooth=FALSE)

    ## Visual artifacts created by the pileup of margin events
    xyplot(`FSC-H` ~ `SSC-H`, GvHD[["s5a05"]], nbin = 100,
    main="A single flowFrame", margin=FALSE)

    ## simple bivariate scatter plot (a.k.a. dot plot)
    ## for the whole flowSet, conditioning on Patient and
    ## Visit
    xyplot(`SSC-H` ~ `FSC-H` | Patient:Visit, data = GvHD)

    ## several examples with time on the X axis
    ## first for a flowFrame
    xyplot(GvHD[[1]])

    ## and for flowSets
    xyplot(`FSC-H` ~ Time | Visit, GvHD,
           smooth = FALSE, type = "l",
           subset = (Patient == 5))

    xyplot(`FSC-H` ~ Time | Patient+Visit, GvHD,
           smooth = FALSE, type = "a",
           strip = FALSE, strip.left = TRUE,
           aspect = "xy")

    ## combine plots for two channels
    ssc.time <-

        xyplot(`SSC-H` ~ Time | factor(Patient):factor(Visit), GvHD,
               smooth = FALSE, type = "a",
               strip = FALSE,
               strip.left = strip.custom(horizontal = TRUE),
               par.strip.text = list(lines = 3),
               between = list(y = rep(c(0, 0.5), c(6, 1))),
               scales = list(x = list(axs = "i"), y = list(draw = FALSE)),
               layout = c(1, 14))

    fsc.time <-
        xyplot(`FSC-H` ~ Time | factor(Patient):factor(Visit), GvHD,
               smooth = FALSE, type = "a",
               strip = FALSE,
               strip.left = strip.custom(horizontal = TRUE),
               par.strip.text = list(lines = 3),
               between = list(y = rep(c(0, 0.5), c(6, 1))),
               scales = list(x = list(axs = "i"), y = list(draw = FALSE)),
               layout = c(1, 14))

    plot(fsc.time, split = c(1, 1, 2, 1))
    plot(ssc.time, split = c(2, 1, 2, 1), newpage = FALSE)

    ## saving plots as variables allows more manipulation
    plot(update(fsc.time[8:14], layout = c(1, 7)),
         split = c(1, 1, 1, 2))

    plot(update(ssc.time[8:14], layout = c(1, 7)),
         split = c(1, 2, 1, 2), newpage = FALSE)

    ## displaying filters
    n2gate <- norm2Filter("SSC-H", "FSC-H")

    xyplot(`SSC-H` ~ `FSC-H` | Patient:Visit, data = GvHD,
           filter=n2gate, subset=Patient==5)

    xyplot(`SSC-H` ~ `FSC-H` | Patient:Visit,
           data=transform("SSC-H"=asinh,"FSC-H"=asinh) %on% GvHD,
           smooth=FALSE, filter=n2gate, subset=Patient==5)

    n2gate.results <- filter(GvHD, n2gate)

    xyplot(`SSC-H` ~ `FSC-H` | Visit, data=GvHD,
           subset=Patient == "6",
           filter=n2gate.results, smooth=FALSE)

[Package flowViz version 1.8.0 Index]