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CytoPipeline class

Source: R/CytoPipelineClass.R
CytoPipelineClass.Rd

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

Usage

# S4 method for class 'CytoPipeline'
show(object)

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

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

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

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

experimentName(x)

experimentName(x) <- value

sampleFiles(x)

sampleFiles(x) <- value

pData(x)

pData(x) <- value

sampleDisplayNames(x, sampleFiles = NULL)

sampleNameFromDisplayName(x, displayName)

Arguments

object

a character() containing a JSON input

experimentName

the experiment name

sampleFiles

a character (e.g. sampleFileNames) or a numeric vector (e.g. indices of sample files). If NULL, all samples will be displayed.

pData

the pheno Data (data.frame or NULL)

x

a CytoPipeline object

...

additional arguments (not used here)

value

the new value to be assigned. the pData<- setter is a bit more liberal than it used to be:

  1. It can accept new pData containing more rows than existing sample names (the corresponding subset of pData is taken).

  2. It can accept pData with row names pointing to either sample file full paths or base file names

  3. It can accept pData with no row names provided the number of rows correspond to the number of sample files. Row names are then set by default to sample file base names (if unique), or sample file full paths.

displayName

a character

Value

nothing

  • for as.list.CytoPipeline: the obtained list

  • for sampleDisplayNames: a character vector of sample display names

  • for sampleNameFromDisplayName: the sample name corresponding to the specified display name. of sample display names

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 should correspond to the sample files (using full paths or base paths). If the pData contains a columns with name 'displayName', this will have an impact in the sampleDisplayNames() function, i.e. sample display names will be the one mentioned in pData, instead of typically base file names (or larger paths if base file names are not unique)

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)