gatingset_to_cytobank: Convert a GatingSet to a Cytobank-compatible gatingML

In RGLab/CytoML: A GatingML Interface for Cross Platform Cytometry Data Sharing

Convert a GatingSet to a Cytobank-compatible gatingML

Description

this function retrieves the gates from GatingSet and writes a customed GatingML-2.0 file that can be imported into cytobank.

Usage

gatingset_to_cytobank(
  gs,
  outFile,
  showHidden = FALSE,
  cytobank.default.scale = TRUE,
  ...
)

Arguments

gs	a GatingSet object
outFile	a file name
showHidden	whether to include the hidden population nodes in the output
cytobank.default.scale	logical flag indicating whether to use the default Cytobank asinhtGml2 settings. Currently it should be set to TRUE in order for gates to be displayed properly in Cytobank because cytobank currently does not parse the global scale settings from GatingML.
...	rescale.gate default is TRUE. which means the gate is rescaled to the new scale that is understandable by cytobank. It is recommended not to change this behavior unless user wants to export to a gatingML file used for other purpose other than being imported into cytobank.

Details

The process can be divided into four steps: 1. Read in gate geometry, compensation and transformation from gatingSet 2. Rescale gate boundaries with flowjo_biexp() so gates can be displayed properly in Cytobank 3. Save gates and hierarchy structure to R environment 4. Write environment out to gatingML using write.GatingML()

Value

nothing

Examples

library(flowWorkspace)

dataDir <- system.file("extdata",package="flowWorkspaceData")
gs <- load_gs(list.files(dataDir, pattern = "gs_manual",full = TRUE))

gs_pop_remove(gs, "CD8")

#output to cytobank
outFile <- tempfile(fileext = ".xml")
gatingset_to_cytobank(gs, outFile) #type by default is 'cytobank'

RGLab/CytoML documentation built on Feb. 1, 2024, 12:34 a.m.