R/plots.R
plotScaleTransformedChannel.Rd
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.
the flowFrame
to be plotted
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).
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.
the transformation type, currently only
linear
and logicle
(bi-exponential) are supported.
the intercept parameter of the linear transformation.
the slope parameter of the linear transformation.
the number of additional decades on the negative side for the logicle transformation.
the width parameter of the logicle transformation.
the number of positive decades of the logicle tranformation.
a ggplot object
# 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] ...
#> Compensating file : Donor1.fcs
#> Compensating file : Donor2.fcs
#> 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] ...
#> Compensating file : Donor1.fcs
#> 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] ...
#> Compensating file : Donor2.fcs
#> 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
)