Plot a flow frame in 1D with explicit user given scale transform

This function plots a 1D view, i.e. the marginal distribution for one specified channel, of the given flow frame, using the specific user-provided scale transformation parameters.

Usage

plotScaleTransformedChannel(
  ff,
  channel,
  applyTransform = c("axis scale only", "data"),
  transfoType = c("linear", "logicle"),
  linA,
  linB,
  negDecades,
  width,
  posDecades
)

Arguments

ff

the flowFrame to be plotted

channel

the name of the channel of which to display the marginal distribution (i.e. the channel name used as column in the ff expression matrix).

applyTransform

if "data", data are explicitly transformed using the user provided sclae transformation parameters, before display if "axis scale only" (default), the data are not transformed, i.e. only the x axis scale is defined according to the scale transformation parameters.

transfoType

the transformation type, currently only linear and logicle(bi-exponential) are supported.

linA

the intercept parameter of the linear transformation.

linB

the slope parameter of the linear transformation.

negDecades

the number of additional decades on the negative side for the logicle transformation.

width

the width parameter of the logicle transformation.

posDecades

the number of positive decades of the logicle tranformation.

Value

a ggplot object

Examples

# run CytoPipeline object first

outputDir <- base::tempdir()


rawDataDir <-
    system.file("extdata", package = "CytoPipeline")
experimentName <- "OMIP021_PeacoQC"
sampleFiles <- 
    file.path(
        rawDataDir, 
        list.files(rawDataDir, pattern = "Donor"))
jsonDir <- system.file("extdata", package = "CytoPipeline")
jsonPath <- file.path(jsonDir, "pipelineParams.json")

pipL2 <- CytoPipeline(
    jsonPath,
    experimentName = experimentName,
    sampleFiles = sampleFiles)

suppressWarnings(execute(
    pipL2,
    rmCache = TRUE,
    path = outputDir))
#> #####################################################
#> ### running SCALE TRANSFORMATION processing steps ###
#> #####################################################
#> Proceeding with step 1 [flowframe_read] ...
#> Proceeding with step 2 [remove_margins] ...
#> Removing margins from file : Donor1.fcs
#> Removing margins from file : Donor2.fcs
#> Proceeding with step 3 [compensate] ...
#> Proceeding with step 4 [flowframe_aggregate] ...
#> Proceeding with step 5 [scale_transform_estimate] ...
#> #####################################################
#> ### NOW PRE-PROCESSING FILE /__w/_temp/Library/CytoPipeline/extdata/Donor1.fcs...
#> #####################################################
#> Proceeding with step 1 [flowframe_read] ...
#> Proceeding with step 2 [remove_margins] ...
#> Removing margins from file : Donor1.fcs
#> Proceeding with step 3 [compensate] ...
#> Proceeding with step 4 [remove_doublets] ...
#> Proceeding with step 5 [remove_debris] ...
#> Proceeding with step 6 [remove_dead_cells] ...
#> Proceeding with step 7 [perform_QC] ...
#> Applying PeacoQC method...
#> Starting quality control analysis for Donor1.fcs
#> Calculating peaks
#> MAD analysis removed 30.75% of the measurements
#> The algorithm removed 30.75% of the measurements
#> Proceeding with step 8 [transform] ...
#> #####################################################
#> ### NOW PRE-PROCESSING FILE /__w/_temp/Library/CytoPipeline/extdata/Donor2.fcs...
#> #####################################################
#> Proceeding with step 1 [flowframe_read] ...
#> Proceeding with step 2 [remove_margins] ...
#> Removing margins from file : Donor2.fcs
#> Proceeding with step 3 [compensate] ...
#> Proceeding with step 4 [remove_doublets] ...
#> Proceeding with step 5 [remove_debris] ...
#> Proceeding with step 6 [remove_dead_cells] ...
#> Proceeding with step 7 [perform_QC] ...
#> Applying PeacoQC method...
#> Starting quality control analysis for Donor2.fcs
#> Calculating peaks
#> MAD analysis removed 24.38% of the measurements
#> The algorithm removed 24.38% of the measurements
#> Proceeding with step 8 [transform] ...

ff <- CytoPipeline::getCytoPipelineFlowFrame(
pipL2,
path = outputDir,
whichQueue = "scale transform",
objectName = "flowframe_aggregate_obj"
)

plotScaleTransformedChannel(
    ff,
    channel = "FSC-A",
    transfoType = "linear",
    linA = 0.0002,
    linB = -0.5)


plotScaleTransformedChannel(
    ff,
    channel = "Comp-670/30Violet-A",
    transfoType = "logicle",
    negDecades = 1,
    width = 0.5,
    posDecades = 4
)


plotScaleTransformedChannel(
    ff,
    channel = "CD3",
    applyTransform = "data",
    transfoType = "logicle",
    negDecades = 1,
    width = 0.5,
    posDecades = 4
)