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plot events in 1D or 2D, using ggplot2

Source: R/ggplots.R
ggplotEvents.Rd

plot events of specific channels of either : flowCore::flowFrame, or flowCore::flowSet in 2D or 1D, mimicking FlowJo type of graph.
if 1D : geom_density will be used
if 2D : geom_hex will be used

Usage

ggplotEvents(
  obj,
  xChannel,
  yChannel = NULL,
  nDisplayCells = Inf,
  seed = NULL,
  bins = 216,
  fill = "lightblue",
  alpha = 0.2,
  xScale = c("linear", "logicle"),
  yScale = c("linear", "logicle"),
  xLogicleParams = NULL,
  yLogicleParams = NULL,
  xLinearRange = NULL,
  yLinearRange = NULL,
  transList = NULL,
  runTransforms = FALSE
)

Arguments

obj

a flowCore::flowFrame or flowCore::flowSet

xChannel

channel (name or index) or marker name to be displayed on x axis

yChannel

channel (name or index) or marker name to be displayed on y axis

nDisplayCells

maximum number of events that will be plotted. If the number of events exceed this number, a sub-sampling will be performed

seed

seed used for sub-sampling (if any)

bins

used in geom_hex

fill

used in geom_density

alpha

used in geom_density

xScale

scale to be used for the x axis (note "linear" corresponds to no transformation)

yScale

scale to be used for the y axis (note "linear" corresponds to no transformation)

xLogicleParams

if (xScale == "logicle"), the parameters of the logicle transformation to be used, as a list(w = ..., m = ..., a = ..., t = ...). If NULL, these parameters will be estimated by flowCore::estimateLogicle()

yLogicleParams

if (yScale == "logicle"), the parameters of the logicle transformation to be used, as a list(w = ..., m = ..., a = ..., t = ...). If NULL, these parameters will be estimated by flowCore::estimateLogicle()

xLinearRange

if (xScale == "linear"), the x axis range to be used

yLinearRange

if (yScale == "linear"), the y axis range to be used

transList

optional list of scale transformations to be applied to each channel. If it is non null, 'x/yScale', 'x/yLogicleParams' and 'x/yLinear_range' will be discarded.

runTransforms

(TRUE/FALSE) Will the application of non linear scale result in data being effectively transformed ?

  • If TRUE, than the data will undergo transformations prior to visualization.

  • If FALSE, the axis will be scaled but the data themselves will not be transformed.

Value

a list of ggplot objects

Examples


data(OMIP021Samples)

### 1D Examples ###

# simple linear scale example
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "FSC-A",
             xScale = "linear")


# with explicit linear range
ggplotEvents(OMIP021Samples[[1]],
                  xChannel = "FSC-A",
                  xScale = "linear",
                  xLinearRange = c(0, 250000))


# with linear scale, several flow frames
ggplotEvents(OMIP021Samples, xChannel = "FSC-A", xScale = "linear")


# simple logicle scale example
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "450/50Violet-A",
             xScale = "logicle")


# logicle scale, explicit parameters
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "450/50Violet-A",
             xScale = "logicle", xLogicleParams = list(
                 a = 1,
                 w = 2,
                 m = 7,
                 t = 270000))


# with sub-sampling
ggplotEvents(OMIP021Samples[[2]],
             xChannel = "450/50Violet-A",
             xScale = "logicle", nDisplayCells = 5000)


# tuning some plot parameters
ggplotEvents(OMIP021Samples[[2]],
             xChannel = "450/50Violet-A",
             xScale = "logicle", alpha = 0.5, fill = "red")


# examples that use a transformation list, estimated after compensation
compensationMatrix <- flowCore::spillover(OMIP021Samples[[1]])$SPILL

ffC <- runCompensation(OMIP021Samples[[1]],
                       spillover = compensationMatrix,
                       updateChannelNames = FALSE)

transList <- flowCore::estimateLogicle(
    ffC,
    colnames(compensationMatrix))

transList <-
    c(transList,
      flowCore::transformList(
          "FSC-A",
          flowCore::linearTransform(a = 0.00001)))

# linear example, without running the transformations on data
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "450/50Violet-A",
             xScale = "linear", 
             transList = transList,
             runTransforms = FALSE)


# linear example, now running the transformations on data
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "450/50Violet-A",
             xScale = "linear", 
             transList = transList,
             runTransforms = TRUE)


# logicle example, without running the transformations on data
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "FSC-A",
             xScale = "logicle", 
             transList = transList,
             runTransforms = FALSE)


# logicle example, now running the transformations on data
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "FSC-A",
             xScale = "logicle", 
             transList = transList,
             runTransforms = TRUE)


### 2D examples ###


# simple linear example
ggplotEvents(OMIP021Samples[[1]],
                  xChannel = "FSC-A",
                  xScale = "linear",
                  yChannel = "610/20Violet-A",
                  yScale = "logicle")


# simple linear example, 2 flow frames
ggplotEvents(OMIP021Samples,
             xChannel = "FSC-A",
             xScale = "linear",
             yChannel = "SSC-A",
             yScale = "linear")


# logicle vs linear example
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "450/50Violet-A",
             xScale = "logicle",
             yChannel = "SSC-A",
             yScale = "linear")


# 2X logicle example
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "TETaGC",
             xScale = "logicle",
             yChannel = "CD27",
             yScale = "logicle")


# tuning nb of bins
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "TETaGC",
             xScale = "logicle",
             yChannel = "CD27",
             yScale = "logicle",
             bins = 128)


# using transformation list, not run on data
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "TETaGC",
             xScale = "logicle",
             yChannel = "CD27",
             yScale = "logicle",
             transList = transList,
             runTransforms = FALSE)


# using transformation list, run on data                  
ggplotEvents(OMIP021Samples[[1]],
             xChannel = "TETaGC",
             xScale = "logicle",
             yChannel = "CD27",
             yScale = "logicle",
             transList = transList,
             runTransforms = TRUE)