this function is a wrapper around PeacoQC::PeacoQC() function. It also pre-selects the channels to be handled (=> all signal channels)
qualityControlPeacoQC(
ff,
preTransform = FALSE,
transList = NULL,
outputDiagnostic = FALSE,
outputDir = NULL,
...
)
a flowCore::flowFrame
if TRUE, apply the transList scale transform prior to running the gating algorithm
applied in conjunction with preTransform
if TRUE, stores diagnostic files generated by PeacoQC in outputDir directory
used in conjunction with outputDiagnostic
additional parameters passed to PeacoQC::PeacoQC()
a flowCore::flowFrame with removed low quality events from the input
rawDataDir <-
system.file("extdata", package = "CytoPipeline")
sampleFiles <-
file.path(rawDataDir, list.files(rawDataDir, pattern = "Donor"))
truncateMaxRange <- FALSE
minLimit <- NULL
# create flowCore::flowSet with all samples of a dataset
fsRaw <- readSampleFiles(
sampleFiles = sampleFiles,
whichSamples = "all",
truncate_max_range = truncateMaxRange,
min.limit = minLimit)
suppressWarnings(ff_m <- removeMarginsPeacoQC(x = fsRaw[[2]]))
#> Removing margins from file : Donor2.fcs
ff_c <-
compensateFromMatrix(ff_m,
matrixSource = "fcs")
#> Compensating file : Donor2.fcs
transList <-
estimateScaleTransforms(
ff = ff_c,
fluoMethod = "estimateLogicle",
scatterMethod = "linear",
scatterRefMarker = "BV785 - CD3")
ff_QualityControl <- suppressWarnings(
qualityControlPeacoQC(
ff_c,
preTransform = TRUE,
transList = transList,
min_cells = 150,
max_bins = 500,
MAD = 6,
IT_limit = 0.55,
force_IT = 150,
peak_removal = (1/3),
min_nr_bins_peakdetection = 10))
#> Applying PeacoQC method...
#> Starting quality control analysis for Donor2.fcs
#> Calculating peaks
#> MAD analysis removed 9.57% of the measurements
#> The algorithm removed 9.57% of the measurements