A GatingSet
object can be exported as a GatingML file or GatingML-based wsp flowJo workspace (version 10) so that they can be loaded into Cytobank
or flowJo
.
The GatingSet
to be exported can be either parsed from Cytobank
or flowJo
or created by automated gating algorithms from openCtyo
. Here we will demontrate the latter.
library(ncdfFlow)
library(flowWorkspace)
library(CytoML)
dataDir <- system.file("extdata",package="flowWorkspaceData")
#load raw FCS
fs <- load_cytoset_from_fcs(file.path(dataDir,"CytoTrol_CytoTrol_1.fcs"))
gs <- GatingSet(fs)
#compensate
comp <- spillover(fs[[1]])[["SPILL"]]
chnls <- colnames(comp)
comp <- compensation(comp)
gs <- compensate(gs, comp)
#transform
trans <- flowjo_biexp_trans()
trans <- transformerList(chnls, trans)
gs <- transform(gs, trans)
Note that the compensation and transformation must be applied directly to GatingSet
instead of flowSet/ncdfFlowSet
so that these information will be stored in the GatingSet
object and exported to gatingML eventually.
library(openCyto)
#load the original template for tcell panel
tbl <- data.table::fread(system.file("extdata/gating_template/tcell.csv", package = "openCyto"))
#modify some paramters to fit the current data range
tbl[5, gating_args:= "gate_range = c(1e3, 3e3)"]
tbl[c(8,11), gating_args:= "gate_range = c(2e3, 3e3)"]
#write the new template to disc
gtFile <- tempfile()
write.csv(tbl, file = gtFile)
##reload the new template
gt <- gatingTemplate(gtFile, autostart = 1L)
#run the gating
gating(gt, gs)
#hide the gates that are not of interest
toggle.helperGates(gt, gs)
#visualize the gates
library(ggcyto)
autoplot(gs[[1]])