This vignette demonstrates how the gatingML files exported from Cytobank can be imported into R as a GatingSet object.

library(flowWorkspace)
library(CytoML)
acs <- system.file("extdata/cytobank_experiment.acs", package = "CytoML")

Create cytobank_experiment object from the ACS bundle exported from Cytobank

ce <- open_cytobank_experiment(acs)
ce
## cytobank Experiment:  tcell 
## gatingML File:  /tmp/RtmpNfVH7F/file177d213559e1e/experiments/3637/cytobank_gate_ml2_v41.xml 
## compensations:  3 
## fcsFiles:  1 
## panels:  1 
## scales:  12

cytobank_experiment is a wrapper around the ACS file, which can be inspected by various accessors.

sampleNames(ce)
## [1] "CytoTrol_CytoTrol_1.fcs"
colnames(ce)
##  [1] "FSC-A"  "FSC-H"  "FSC-W"  "SSC-A"  "B710-A" "R660-A" "R780-A" "V450-A"
##  [9] "V545-A" "G560-A" "G780-A" "Time"
markernames(ce)
## $CytoTrol_CytoTrol_1.fcs
##  [1] "FSC-A"        "FSC-H"        "FSC-W"        "SSC-A"        "CD4"         
##  [6] "CD38 APC"     "CD8 APCH7"    "CD3"          "HLA-DR V500"  "CCR7 PE"     
## [11] "CD45RA PECy7" "Time"
pData(ce)
##                                            name Conditions Individuals
## CytoTrol_CytoTrol_1.fcs CytoTrol_CytoTrol_1.fcs condition1       ptid1

Then import cytobank_experiment into GatingSet

gs <- cytobank_to_gatingset(ce)

Alternatively, the import can be done by gatingML and fcs files that are downloaded separately form Cytobank without ACS.

xmlfile <- ce$gatingML
fcsFiles <- list.files(ce$fcsdir, full.names = TRUE)
gs <- cytobank_to_gatingset(xmlfile, fcsFiles)

However, it doesn’t have the information from yaml file (part of ACS). E.g. sample tags (i.e. pData) and customized markernames. So it is recommended to import ACS.

Inspect the results

library(ggcyto)
## Plot the gates
autoplot(gs[[1]])

# Extract the population statistics
gs_pop_get_count_fast(gs, statType = "count")
##                        name                                     Population
##  1: CytoTrol_CytoTrol_1.fcs                                    /not debris
##  2: CytoTrol_CytoTrol_1.fcs                           /not debris/singlets
##  3: CytoTrol_CytoTrol_1.fcs                       /not debris/singlets/CD3
##  4: CytoTrol_CytoTrol_1.fcs                   /not debris/singlets/CD3/CD8
##  5: CytoTrol_CytoTrol_1.fcs            /not debris/singlets/CD3/CD8/CD8_Q2
##  6: CytoTrol_CytoTrol_1.fcs                   /not debris/singlets/CD3/CD4
##  7: CytoTrol_CytoTrol_1.fcs                /not debris/singlets/CD3/CD4/Q1
##  8: CytoTrol_CytoTrol_1.fcs                /not debris/singlets/CD3/CD4/Q2
##  9: CytoTrol_CytoTrol_1.fcs                /not debris/singlets/CD3/CD4/Q4
## 10: CytoTrol_CytoTrol_1.fcs                /not debris/singlets/CD3/CD4/Q3
## 11: CytoTrol_CytoTrol_1.fcs /not debris/singlets/CD3/CD8/CD8_Q2/CD38 range
## 12: CytoTrol_CytoTrol_1.fcs  /not debris/singlets/CD3/CD8/CD8_Q2/HLA range
##                                  Parent Count ParentCount
##  1:                                root 87876      119531
##  2:                         /not debris 79845       87876
##  3:                /not debris/singlets 53135       79845
##  4:            /not debris/singlets/CD3 12862       53135
##  5:        /not debris/singlets/CD3/CD8  2331       12862
##  6:            /not debris/singlets/CD3 33653       53135
##  7:        /not debris/singlets/CD3/CD4   419       33653
##  8:        /not debris/singlets/CD3/CD4 11429       33653
##  9:        /not debris/singlets/CD3/CD4  4119       33653
## 10:        /not debris/singlets/CD3/CD4 17686       33653
## 11: /not debris/singlets/CD3/CD8/CD8_Q2  2331        2331
## 12: /not debris/singlets/CD3/CD8/CD8_Q2  2315        2331