Plot of channel intensity marginal densities

ggplotMarginalDensities uses ggplot2 to draw plots of marginal densities of selected channels of a flowSet. If the flowSet contains several flowFrames, events are concatenated together per group, or all together in the absence of groups.

Usage

ggplotMarginalDensities(
  x,
  sampleSubset,
  channels,
  pDataForColour,
  pDataForGroup,
  nEventInSubsample = Inf,
  seed = NULL,
  transList
)

Arguments

x

a flowCore::flowSet (or a single flowCore::flowFrame)

sampleSubset

(optional) a logical vector, of the same length as x, indicating which flow frames to keep in the plot. Typically it is obtained through the evaluation of a logical condition the rows of phenoData(fs).

channels

(optional) - can be indices, or channel names, or markers.

pDataForColour

(optional) which variable of phenoData(fs) will be used as colour aesthetic. Should be a character.

pDataForGroup

(optional) which variable of phenoData(fs) will be used as group aesthetic. Should be a character. A separate marginal density will be calculated for each group and overlaid on the same channel density plots.

nEventInSubsample

how many event to take (per flowFrame of the flowSet) for marginal density approximation.

seed

if not null, used in subsampling.

transList

a flowCore::transformList that will be applied before plotting.

Value

a ggplot object

Examples

library(CytoPipeline)

data(OMIP021Samples)

# estimate scale transformations 
# and transform the whole OMIP021Samples

transList <- estimateScaleTransforms(
    ff = OMIP021Samples[[1]],
    fluoMethod = "estimateLogicle",
    scatterMethod = "linearQuantile",
    scatterRefMarker = "BV785 - CD3")

OMIP021Trans <- CytoPipeline::applyScaleTransforms(
    OMIP021Samples, 
    transList)
    

# As there are only 2 samples in OMIP021Samples dataset,
# we create artificial samples that are random combinations of both samples

ffList <- c(
    flowCore::flowSet_to_list(OMIP021Trans),
    lapply(3:5,
           FUN = function(i) {
               aggregateAndSample(
                   OMIP021Trans,
                   seed = 10*i,
                   nTotalEvents = 5000)[,1:22]
           }))

fsNames <- c("Donor1", "Donor2", paste0("Agg",1:3))
names(ffList) <- fsNames

fsAll <- as(ffList,"flowSet")

flowCore::pData(fsAll)$grpId <- factor(c("D1", "D2", rep("Agg", 3)))
flowCore::pData(fsAll)$lbl <- paste0("S", 1:5)

# plot densities, all samples together
p <- ggplotMarginalDensities(fsAll)

# plot densities, per sample
p <- ggplotMarginalDensities(fsAll, pDataForGroup = "lbl")

# plot densities, per sample and coloured by group
p <- ggplotMarginalDensities(
    fsAll, 
    pDataForGroup = "lbl",
    pDataForColour = "grpId")