xyplot: Methods implementing Lattice xyplots for flow data.
In flowViz: Visualization for flow cytometry

Description Usage Arguments Details Methods Author(s) See Also Examples

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

## 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.xyplot.flowframe.time(x, y, frame, time, xlim, ylim, ...)

panel.xyplot.flowframe.time(x, y, frame, time, type = "discrete",
  nrpoints = 0, binSize = 100, ...)

## S4 method for signature 'formula,flowFrame'
xyplot(x, data, filter = NULL,
  overlay = NULL, stats = FALSE, strip.text = NULL, ...)

prepanel.xyplot.flowframe(frame, channel.x.name, channel.y.name, x, y,
  xlim, ylim, ...)

panel.xyplot.flowframe(frame, filter = NULL, smooth = TRUE,
  margin = TRUE, outline = FALSE, channel.x.name, channel.y.name,
  pch = gp$flow.symbol$pch, alpha = gp$flow.symbol$alpha,
  cex = gp$flow.symbol$cex, col = gp$flow.symbol$col, gp, xbins = 0,
  binTrans = sqrt, stats = FALSE, pos = 0.5, digits = 2,
  abs = FALSE, overlay = NULL, checkName = TRUE, sample.ratio = 1,
  overlay.symbol = NULL, ...)

## S4 method for signature 'formula,flowSet'
xyplot(x, data, ...)

prepanel.xyplot.flowset(x, frames, channel.x.name, channel.y.name, xlim,
  ylim, ...)

panel.xyplot.flowset(x, frames, filter = NULL, channel.x, channel.y,
  overlay = NULL, stats = FALSE, ...)

## 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,
  ...)

## S4 method for signature 'formula,ncdfFlowSet'
xyplot(x, data, ...)

## S4 method for signature 'formula,ncdfFlowList'
xyplot(x, data, ...)

`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`, `flowFrame`, `ncdfFlowSet`, or `ncdfFlowList` object that serves as the source of data.
`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.
`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.
`prepanel`	The prepanel function. See `xyplot`.
`panel`	The panel function. See `xyplot`.
`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.
`...`	marker.only `logical` specifies whether to show both channel and marker names More arguments, usually passed on to the underlying lattice methods.
`xlim, ylim`	limits for data axes. If not given, they are taken from the ranges stored in flowFrame
`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.
`filter`	A `filter`, `filterResult` or `filterResultList` object or a list of such objects of the same length as the `flowSet`. Also a `filters` or A `filtersList` can be passed to xyplot in order to plot multiple filters/gates(with the same x,y parameters) on one panel to represent multiple sub-populations. The appropriate spherical 2D representation of this filter will be superimposed on the plot if `smooth=TRUE`, or the result 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).
`overlay`	The extra cell events plotted on top of the current cell population. It is a `flowSet` for `panel.xyplot.flowset` function and a `flowFrame` for `xyplot(c("formula","flowFrame"))` method.
`stats, pos, digits, abs`	Arguments to control statistics that is associated with `filter` to be plotted Currently only population proportion/percentage is supported. `stats` is a `logical` scalar indicating whether to display statistics. Default is FALSE. `pos` is the `numeric` scalar (range within c(0,1)) or vector(length of 2,first is for x-axis,second for y-axis) to control the position of the statistics label. It is set as 0.5,which is the center. `digits` is an `integer` indicating the number of significant digits to be used when displaying the percentage of population statistics,Default is 2. see more details from `format` `abs` is a `logical` scalar indicating whether the `pos` is relative to the gate boundary or the entire xy-axis(absolute position). By default it is set as FALSE,which indicates the position is relative to gate.
`strip.text`	A `character` that customizes the text in strip. Default is NULL, which does not display the strip box at all. It is only valid when plotting a `flowFrame`
`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. If `FALSE`, a graphical parameter `colramp` can be used to obtain a coloring of points that is indicative of their local density.
`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.
`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.
`xbins`	The argument passed to `hexbin` ,which is the number of bins partitioning the range of xbnds. It is set as 0 by default,which plots all the events without binning. When it is larger than 0,hexbin plot engine is used for the faster plotting. Note that it is only valid when `smooth` is set as FALSE .
`binTrans`	The argument passed to `grid.hexagons` ,which is a transformation function (or NULL) for the count. It is `sqrt` by default.
`checkName`	`logical` indicating whether to skip checking the bracket '(' in channel name
`sample.ratio`	`numeric` the ratio of sub-sampling of events to speed up plotting.
`overlay.symbol`	list of the lattice graphic parameters to format the overlay points.
`frames`	An environment containing frame-specific data.
`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.
`par.settings`	A list of lists of graphical parameters. See `flowViz.par.set` for 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.

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. Argument par.settings can be used to supply lists of graphical parameters. 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.

F. Hahne, D. Sarkar

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.

library(flowCore)
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)

## A non-smooth version of the same data with customerized color scheme
require(IDPmisc)
colramp <- colorRampPalette(IDPcolorRamp(21))
xyplot(`FSC-H` ~ `SSC-H`, GvHD[["s5a05"]], nbin = 100,
       main="A single flowFrame", smooth=FALSE,
       colramp=colramp, pch=20, cex=0.1)

## A hexbin version of non-smooth scatter plot  
xyplot(`FSC-H` ~ `SSC-H`, GvHD[["s5a05"]], xbin = 128
       ,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)

## Same bivariate scatter plot with replacing default color
require(IDPmisc)
cols <- colorRampPalette(IDPcolorRamp(21))
xyplot(`SSC-H` ~ `FSC-H` | Patient:Visit, data = GvHD, colramp=cols)

## 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), xbin = 32)

xyplot(`FSC-H` ~ Time | Patient+Visit, GvHD, 
       smooth = FALSE, type = "a",
       strip = FALSE, strip.left = TRUE,
       aspect = "xy", xbin = 32)


## 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), xbin = 32)

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), xbin = 32)

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
library(flowStats)
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, xbin = 32)


## displaying filters with stats
n2gate.results <- filter(GvHD, n2gate)

xyplot(`SSC-H` ~ `FSC-H` | Visit, data=GvHD,
       subset=Patient == "6",
       filter=n2gate.results, smooth=FALSE, xbin = 32
       ,stats=TRUE
       ,abs=TRUE
       ,digits=3 
       )

       
## displaying multiple filters in one panel with stats
recGate1<-rectangleGate("FL3-H"=c(2.3,4.1),"FL2-H"=c(6.8,9))
recGate2<-rectangleGate("FL3-H"=c(1,3),"FL2-H"=c(4,6))
filters1<-filters(list(recGate1,recGate2))
trans<-transform("FL2-H"=asinh,"FL3-H"=asinh)
trans_data<-transform(GvHD[1:2],trans)
#replicate filters object across samples
flist <- list(filters1 , filters1)
names(flist) <- sampleNames(trans_data)
xyplot(`FL2-H` ~ `FL3-H`
	   ,data=trans_data 
       ,filter= flist
       ,stats=TRUE
       ,margin=FALSE
       , xbin = 32
       , smooth = FALSE
       )

#display recGate2 as a overlay 
overlay <- Subset(trans_data,recGate1)
xyplot(`FL2-H` ~ `FL3-H`
	   ,data=trans_data 
       ,filter=recGate2
       ,stats=TRUE
       ,margin=FALSE
       , smooth = FALSE
       , xbin = 32
       ,overlay= list(rect2 = overlay)
       ,par.settings = list(overlay.symbol = list(cex = 0.1))
       )