PeacoQC: Peak-based detection of high quality cytometry data

In PeacoQC: Peak-based selection of high quality cytometry data

Description

PeacoQC will determine peaks on the channels in the flowframe. Then it will remove anomalies caused by e.g. clogs, changes in speed etc. by using an IsolationTree and/or the MAD method.

Usage

PeacoQC(ff, channels, determine_good_cells="all",
        plot=TRUE, save_fcs=TRUE, output_directory=".",
        name_directory="PeacoQC_results", report=TRUE,
        events_per_bin=2000, MAD=6, IT_limit=0.55,
        consecutive_bins=5, remove_zeros=FALSE, suffix_fcs="_QC", ...)

Arguments

ff	A flowframe or the location of an fcs file. Make sure that the flowframe is compensated and transformed. If it is mass cytometry data, only a transformation is necessary.
channels	Indices or names of the channels in the flowframe on which peaks have to be determined.
determine_good_cells	If set to FALSE, the algorithm will only determine peaks. If it is set to "all", the bad measurements will be filtered out based on the MAD and IT analysis. It can also be put to "MAD" or "IT" to only use one method of filtering.
plot	If set to TRUE, the PlotPeacoQC function is run to make an overview plot of the deleted measurements. Default is TRUE.
save_fcs	If set to TRUE, the cleaned fcs file will be saved in the output_directory as: filename_QC.fcs. The _QC name can be altered with the suffix_fcs parameter. An extra column named "Original_ID" is added to this fcs file where the cells are given their original cell id. Default is TRUE.
output_directory	Directory where a new folder will be created that consists of the generated fcs files, plots and report. If set to NULL, nothing will be stored.The default folder is the working directory.
name_directory	Name of folder that will be generated in output_directory. The default is "PeacoQC_results".
report	Overview text report that is generated after PeacoQC is run. If set to FALSE, no report will be generated. The default is TRUE.
events_per_bin	Number of events that are put in one bin. Default is 2000.
MAD	The MAD parameter. Default is 6. If this is increased, the algorithm becomes less strict.
IT_limit	The IsolationTree parameter. Default is 0.55. If this is increased, the algorithm becomes less strict.
consecutive_bins	If 'good' bins are located between bins that are removed, they will also be marked as 'bad'. The default is 5.
remove_zeros	If this is set to TRUE, the zero values will be removed before the peak detection step. They will not be indicated as 'bad' value. This is recommended when cleaning mass cytometry data. Default is FALSE.
suffix_fcs	The suffix given to the new fcs files. Default is "_QC".
...	Options to pass on to the PlotPeacoQC function (display_cells, manual_cells, prefix)

Value

This function returns a list with a number of items. It will include "FinalFF" where the transformed, compensated and cleaned flowframe is stored. It also contains the starting parameters and the information necessary to give to PlotPeacoQC if the two functions are run seperatly. The GoodCells list is also given where 'good' measurements are indicated as TRUE and the to be removed measurements as FALSE.

Examples

# General pipeline for preprocessing and quality control with PeacoQC

# Read in raw fcs file
fileName <- system.file("extdata", "111.fcs", package="PeacoQC")
ff <- flowCore::read.FCS(fileName)

# Define channels where the margin events should be removed
# and on which the quality control should be done
channels <- c(1, 3, 5:14, 18, 21)

ff <- RemoveMargins(ff=ff, channels=channels, output="frame")

# Compensate and transform the data

ff <- flowCore::compensate(ff, flowCore::keyword(ff)$SPILL)
ff <- flowCore::transform(ff,
                            flowCore::estimateLogicle(ff,
                            colnames(flowCore::keyword(ff)$SPILL)))
#Run PeacoQC
PeacoQC_res <- PeacoQC(ff, channels,
                        determine_good_cells="all",
                        plot=TRUE, save_fcs=TRUE)

PeacoQC documentation built on Nov. 8, 2020, 8:18 p.m.