title: “How to parse gatingML into a GatingSet” author: “Mike Jiang” date: “October 27, 2015”

output: html_document

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

Load the gatingML file as a graphGML object

library(flowWorkspace)
xmlfile <- system.file("extdata/cytotrol_tcell_cytobank.xml", package = "flowWorkspace")
g <- read.gatingML.cytobank(xmlfile)
class(g)

## [1] "graphGML"
## attr(,"package")
## [1] "flowWorkspace"

graphGML stores the gating hierarchy

g

## --- Gating hieararchy parsed from GatingML: 
##  with  12  populations defined

It can be inspected by various accessors.

getNodes(g)

## GateSet_722318 GateSet_722319 GateSet_722320 GateSet_722321 GateSet_722322 
##          "CD4"          "CD8"           "Q4"           "Q3"     "singlets" 
## GateSet_722323 GateSet_722324 GateSet_722325 GateSet_722326 GateSet_722327 
##   "not debris"           "Q1"           "Q2"       "CD8_Q2"          "CD3" 
## GateSet_722328 GateSet_722329 
##   "CD38 range"    "HLA range"

getParent(g, "GateSet_722318")

## [1] "GateSet_722327"

getChildren(g, "GateSet_722318")

## [1] "GateSet_722320" "GateSet_722321" "GateSet_722324" "GateSet_722325"

The Population tree can be plotted

plot(g)

The node with dotted border means the tailored gates(or sample-specific gates) are defined for that population.

Read raw FCS files

fcsFiles <- list.files(pattern = "CytoTrol", system.file("extdata", package = "flowWorkspaceData"), full = T)
fs <- read.ncdfFlowSet(fcsFiles)

Compensate the data

fs <- compensate(fs, g)

Transform the data

trans <- getTransformations(g)
trans

## An object of class "transformList"
## Slot "transforms":
## $`B710-A`
## transformMap for parameter 'B710-A' mapping to 'B710-A'
## 
## $`G560-A`
## transformMap for parameter 'G560-A' mapping to 'G560-A'
## 
## $`G780-A`
## transformMap for parameter 'G780-A' mapping to 'G780-A'
## 
## $`R660-A`
## transformMap for parameter 'R660-A' mapping to 'R660-A'
## 
## $`R780-A`
## transformMap for parameter 'R780-A' mapping to 'R780-A'
## 
## $`V450-A`
## transformMap for parameter 'V450-A' mapping to 'V450-A'
## 
## $`V545-A`
## transformMap for parameter 'V545-A' mapping to 'V545-A'
## 
## 
## Slot "transformationId":
## [1] "defaultTransformation"

fs <- transform(fs, trans)

Visualize the outcome of compensation and transformation

 densityplot(~`CD4`, fs)
 xyplot(`CD4` ~`CD8`, fs, smooth = FALSE, xbin = 32)

Construct the GatingSet and apply the gates

gs <- GatingSet(fs)
gating(g, gs)

Plot the gates

plotGate(gs[[1]])

Extract the population statistics

getPopStats(gs, statType = "count")

##                        name Population     Parent Count ParentCount
##  1: CytoTrol_CytoTrol_1.fcs not debris       root 87876      119531
##  2: CytoTrol_CytoTrol_1.fcs   singlets not debris 79845       87876
##  3: CytoTrol_CytoTrol_1.fcs        CD3   singlets 53135       79845
##  4: CytoTrol_CytoTrol_1.fcs        CD8        CD3 12862       53135
##  5: CytoTrol_CytoTrol_1.fcs     CD8_Q2        CD8  2331       12862
##  6: CytoTrol_CytoTrol_1.fcs  HLA range     CD8_Q2  2315        2331
##  7: CytoTrol_CytoTrol_1.fcs CD38 range     CD8_Q2  2331        2331
##  8: CytoTrol_CytoTrol_1.fcs        CD4        CD3 33653       53135
##  9: CytoTrol_CytoTrol_1.fcs         Q2        CD4 11429       33653
## 10: CytoTrol_CytoTrol_1.fcs         Q1        CD4   419       33653
## 11: CytoTrol_CytoTrol_1.fcs         Q3        CD4 17686       33653
## 12: CytoTrol_CytoTrol_1.fcs         Q4        CD4  4119       33653
## 13: CytoTrol_CytoTrol_2.fcs not debris       root 87648      115728
## 14: CytoTrol_CytoTrol_2.fcs   singlets not debris 79641       87648
## 15: CytoTrol_CytoTrol_2.fcs        CD3   singlets 50761       79641
## 16: CytoTrol_CytoTrol_2.fcs        CD8        CD3 12413       50761
## 17: CytoTrol_CytoTrol_2.fcs     CD8_Q2        CD8  2208       12413
## 18: CytoTrol_CytoTrol_2.fcs  HLA range     CD8_Q2  2193        2208
## 19: CytoTrol_CytoTrol_2.fcs CD38 range     CD8_Q2  2208        2208
## 20: CytoTrol_CytoTrol_2.fcs        CD4        CD3 32226       50761
## 21: CytoTrol_CytoTrol_2.fcs         Q2        CD4 10904       32226
## 22: CytoTrol_CytoTrol_2.fcs         Q1        CD4   383       32226
## 23: CytoTrol_CytoTrol_2.fcs         Q3        CD4 16965       32226
## 24: CytoTrol_CytoTrol_2.fcs         Q4        CD4  3974       32226
##                        name Population     Parent Count ParentCount