CytoPipeline class

Class representing a flow cytometry pipeline, and composed of two processing queues, i.e. lists of CytoProcessingStep objects :

• a list of CytoProcessingStep(s) for pre-calculation of scale transformations per channel

• a list of CytoProcessingStep(s) for the pre-processing of flow frames

# S4 method for CytoPipeline
show(object)

# S4 method for missing
CytoPipeline(
  object,
  experimentName = "default_experiment",
  sampleFiles = character(),
  pData = NULL
)

# S4 method for list
CytoPipeline(
  object,
  experimentName = "default_experiment",
  sampleFiles = character(),
  pData = NULL
)

# S4 method for character
CytoPipeline(
  object,
  experimentName = "default_experiment",
  sampleFiles = character(),
  pData = NULL
)

# S3 method for CytoPipeline
as.list(x, ...)

experimentName(x)

experimentName(x) <- value

sampleFiles(x)

sampleFiles(x) <- value

pData(x)

pData(x) <- value

Arguments

object

a character() containing a JSON input

experimentName

the experiment name

sampleFiles

the sample files

pData

the pheno Data (data.frame or NULL)

x

a CytoPipeline object

...

additional arguments (not used here)

value

the new value to be assigned

Value

nothing

• for as.list.CytoPipeline: the obtained list

Slots

scaleTransformProcessingQueue

A list of CytoProcessingStep objects containing the steps for obtaining the scale transformations per channel

flowFramesPreProcessingQueue

A list of CytoProcessingStep objects containing the steps for pre-processing of the samples flow frames

experimentName

A character containing the experiment (run) name

sampleFiles

A character vector storing all fcs files to be run into the pipeline

pData

An optional data.frame containing additional information for each sample file. The pData raw names must correspond to basename(sampleFiles) otherwise validation of the CytoPipeline object will fail!

Examples

### *** EXAMPLE 1: building CytoPipeline step by step *** ###

rawDataDir <-
    system.file("extdata", package = "CytoPipeline")
experimentName <- "OMIP021_PeacoQC"
sampleFiles <- file.path(rawDataDir, list.files(rawDataDir,
                                             pattern = "Donor"))
                                             
outputDir <- base::tempdir()

# main parameters : sample files and output files
pipL <- CytoPipeline(experimentName = experimentName,
                     sampleFiles = sampleFiles)

### SCALE TRANSFORMATION STEPS ###

pipL <-
    addProcessingStep(pipL,
                      whichQueue = "scale transform",
                      CytoProcessingStep(
                          name = "flowframe_read",
                          FUN = "readSampleFiles",
                          ARGS = list(
                              whichSamples = "all",
                              truncate_max_range = FALSE,
                              min.limit = NULL
                          )
                      )
    )

pipL <-
    addProcessingStep(pipL,
                      whichQueue = "scale transform",
                      CytoProcessingStep(
                          name = "remove_margins",
                          FUN = "removeMarginsPeacoQC",
                          ARGS = list()
                     )
    )

pipL <-
    addProcessingStep(pipL,
                      whichQueue = "scale transform",
                      CytoProcessingStep(
                          name = "compensate",
                          FUN = "compensateFromMatrix",
                          ARGS = list(matrixSource = "fcs")
                      )
    )

pipL <-
    addProcessingStep(pipL,
                      whichQueue = "scale transform",
                      CytoProcessingStep(
                          name = "flowframe_aggregate",
                          FUN = "aggregateAndSample",
                          ARGS = list(
                              nTotalEvents = 10000,
                              seed = 0
                          )
                      )
    )

pipL <-
    addProcessingStep(pipL,
                      whichQueue = "scale transform",
                      CytoProcessingStep(
                          name = "scale_transform_estimate",
                          FUN = "estimateScaleTransforms",
                          ARGS = list(
                              fluoMethod = "estimateLogicle",
                              scatterMethod = "linear",
                              scatterRefMarker = "BV785 - CD3"
                          )
                      )
    )

### PRE-PROCESSING STEPS ###

pipL <-
    addProcessingStep(pipL,
                      whichQueue = "pre-processing",
                      CytoProcessingStep(
                          name = "flowframe_read",
                          FUN = "readSampleFiles",
                          ARGS = list(
                              truncate_max_range = FALSE,
                              min.limit = NULL
                          )
                      )
    )

pipL <-
    addProcessingStep(pipL,
                      whichQueue = "pre-processing",
                      CytoProcessingStep(
                          name = "remove_margins",
                          FUN = "removeMarginsPeacoQC",
                          ARGS = list()
                      )
    )

pipL <-
    addProcessingStep(pipL,
                      whichQueue = "pre-processing",
                      CytoProcessingStep(
                          name = "compensate",
                          FUN = "compensateFromMatrix",
                          ARGS = list(matrixSource = "fcs")
                      )
    )

pipL <-
    addProcessingStep(
        pipL,
        whichQueue = "pre-processing",
        CytoProcessingStep(
            name = "remove_debris",
            FUN = "removeDebrisManualGate",
            ARGS = list(
                FSCChannel = "FSC-A",
                SSCChannel = "SSC-A",
                gateData =  c(73615, 110174, 213000, 201000, 126000,
                              47679, 260500, 260500, 113000, 35000)))
    )

pipL <-
    addProcessingStep(pipL,
                      whichQueue = "pre-processing",
                      CytoProcessingStep(
                          name = "remove_dead_cells",
                          FUN = "removeDeadCellsManualGate",
                          ARGS = list(
                              FSCChannel = "FSC-A",
                              LDMarker = "L/D Aqua - Viability",
                              gateData = c(0, 0, 250000, 250000,
                                           0, 650, 650, 0)
                          )
                      )
    )

pipL <-
    addProcessingStep(
        pipL,
        whichQueue = "pre-processing",
        CytoProcessingStep(
            name = "perform_QC",
            FUN = "qualityControlPeacoQC",
            ARGS = list(
                preTransform = TRUE,
                min_cells = 150, # default
                max_bins = 500, # default
                step = 500, # default,
                MAD = 6, # default
                IT_limit = 0.55, # default
                force_IT = 150, # default
                peak_removal = 0.3333, # default
                min_nr_bins_peakdetection = 10 # default
            )
        )
    )

pipL <-
    addProcessingStep(pipL,
                      whichQueue = "pre-processing",
                      CytoProcessingStep(
                          name = "transform",
                          FUN = "applyScaleTransforms",
                          ARGS = list()
                      )
    )

### *** EXAMPLE 2: building CytoPipeline from JSON file *** ###

jsonDir <- system.file("extdata", package = "CytoPipeline")
jsonPath <- file.path(jsonDir, "pipelineParams.json")

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