## Loading required package: flowWorkspaceData
The openCyto package is designed to facilitate the application of automated gating methods in a sequential way to mimic the construction of a manual gating scheme.
Traditionally, scientists have to draw the gates for each individual sample on each 2-D projection (2 channels) within
flowJo. Alternatively, they can draw template gates on one sample and replicate them to other samples, then manually inspect the gate on each sample
to do the correction if necessary. Either way is time consuming and subjective, thus not suitable for the large data sets
generated by high-throughput flow cytometry, CyTOF, or “cross-lab” data analysis.
Here is one
xml workspace (manual gating scheme) exported from
flowDataPath <- system.file("extdata", package = "flowWorkspaceData") wsfile <- list.files(flowDataPath, pattern="manual.xml",full = TRUE) wsfile
##  "/home/biocbuild/bbs-3.12-bioc/R/library/flowWorkspaceData/extdata/manual.xml"
By using the
CytoML package, We can load it into R,
library(CytoML) ws <- open_flowjo_xml(wsfile)
manual gatesdefined in
xmlto the raw
gs <- flowjo_to_gatingset(ws, name= "T-cell", subset =1, isNcdf = TRUE)
and then visualize the
gh <- gs[] plot(gh)
This is a gating scheme for a
T cell panel, which tries to identify
T cell sub-populations.
We can achieve the same results by using the automated gating pipeline provided by this package.
flowClust and other packages provide many different gating methods to
detect cell populations and draw gates automatically.
flowWorkspace package provides the
GatingSet as an efficient data structure to store, query and visualize the hierarchical gated data.
By taking advantage of these tools, the
openCyto package can create the automated gating pipeline by a
gatingTemplate, which is essentially the same kind of hierarchical gating scheme
used by scientists.
First of all, we need to describe the gating hierarchy in a spread sheet (a plain text format). This spread sheet must have the following columns:
alias: a name used to label the cell population, with the path composed of the alias and its precedent nodes (e.g. /root/A/B/alias) being uniquely identifiable.
pop: population patterns of
+/-+/-, which tell the algorithm which side (postive or negative) of a 1-D gate or which quadrant of a 2-D gate are to be kept.
parent: the parent population alias, whose path also has to be uniquely identifiable.
dims: characters seperated by commas specifying the dimensions (1-D or 2-D) used for gating. These can be either channel names or stained marker names.
gating_method: the name of the gating function (e.g.
flowClust). It is invoked by a wrapper function that has the identical function name prefixed with a dot.(e.g.
gating_args: the named arguments passed to the gating function
collapseDataForGating: When TRUE, data is collapsed (within groups if
groupByis specified) before gating and the gate is replicated across collapsed samples. When set FALSE (or blank), the
groupByargument is only used by
preprocessingand ignored by gating.
groupBy: If provided, samples are split into groups by the unique combinations of the named study variable (i.e. column names of pData, e.g.“PTID:VISITNO”). When this is numeric (N), samples are grouped by every N samples
preprocessing_method: the name of the preprocessing function (e.g.
prior_flowClust). It is invoked by a wrapper function that has the identical function name prefixed with a dot (e.g.
.prior_flowClust). The preprocessing results are then passed to the appropriate gating wrapper function through its
preprocessing_args: the named arguments passed to the preprocessing function.
Here is an example of a gating template.
library(openCyto) library(data.table) gtFile <- system.file("extdata/gating_template/tcell.csv", package = "openCyto") dtTemplate <- fread(gtFile) dtTemplate
## alias pop parent dims gating_method ## 1: nonDebris + root FSC-A gate_mindensity ## 2: singlets + nonDebris FSC-A,FSC-H singletGate ## 3: lymph + singlets FSC-A,SSC-A flowClust ## 4: cd3 + lymph CD3 gate_mindensity ## 5: * -/++/- cd3 cd4,cd8 gate_mindensity ## 6: activated cd4 ++ cd4+cd8- CD38,HLA tailgate ## 7: activated cd8 ++ cd4-cd8+ CD38,HLA tailgate ## 8: CD45_neg - cd4+cd8- CD45RA gate_mindensity ## 9: CCR7_gate + CD45_neg CCR7 flowClust ## 10: * +/-+/- cd4+cd8- CCR7,CD45RA refGate ## 11: * +/-+/- cd4-cd8+ CCR7,CD45RA gate_mindensity ## gating_args collapseDataForGating groupBy preprocessing_method ## 1: NA NA ## 2: NA NA ## 3: K=2,target=c(1e5,5e4) NA NA prior_flowClust ## 4: TRUE 4 ## 5: gate_range=c(1,3) NA NA ## 6: NA NA standardize_flowset ## 7: tol=0.08 NA NA standardize_flowset ## 8: gate_range=c(2,3) NA NA ## 9: neg=1,pos=1 NA NA ## 10: CD45_neg:CCR7_gate NA NA ## 11: NA NA ## preprocessing_args ## 1: NA ## 2: NA ## 3: NA ## 4: NA ## 5: NA ## 6: NA ## 7: NA ## 8: NA ## 9: NA ## 10: NA ## 11: NA
Each row is usually corresponding to one cell population and the gating method that is used to get that population. We will try to explain how to create this gating template based on the manual gating scheme row by row.
## alias pop parent dims gating_method gating_args collapseDataForGating ## 1: nonDebris + root FSC-A gate_mindensity NA ## groupBy preprocessing_method preprocessing_args ## 1: NA NA
"nonDebris"(specified in the
root(which is always the first node of a
mindensity(one of the
gatingfunctions provided by
openCytopackage) as the
gating_methodto gate on dimension (
popfield indicates the
positiveside of the 1-D gate is kept as the population of interest.
preprocessinginvolved in this gate, so the other columns are left blank.
## alias pop parent dims gating_method gating_args ## 1: singlets + nonDebris FSC-A,FSC-H singletGate ## collapseDataForGating groupBy preprocessing_method preprocessing_args ## 1: NA NA NA
singletGate(a function from the
polygonGatewill be generated on
dims) for each sample.
popfield stands for
"singlets+". But here it is 2-D gate, which means we want to keep the area inside of the polygon.
## alias pop parent dims gating_method gating_args ## 1: lymph + singlets FSC-A,SSC-A flowClust K=2,target=c(1e5,5e4) ## collapseDataForGating groupBy preprocessing_method preprocessing_args ## 1: NA NA prior_flowClust NA
aliasspecifies the name of population.
gating_methodto do the 2-dimensional gating,
dimsis a comma-separated string:
FSC-A) goes first,
SSC-A) the second. This order doesn't affect the gating process but will determine how the gates are displayed.
flowClustalgorithm accepts can be put in
gating_argsas if they are typed in the
R console. see
help(flowClust)for more details of these arguments
flowClustalgorithm accepts the extra argument
priorthat is calculated during the
preprocessingstage (before the actual gating). Thus, we supply the
## alias pop parent dims gating_method gating_args collapseDataForGating ## 1: cd3 + lymph CD3 gate_mindensity TRUE ## groupBy preprocessing_method preprocessing_args ## 1: 4 NA
This is similar to the
nonDebris gate except that we specify
which tells the pipeline to
collapse all samples into one and apply
mindensity to the collapsed data on
Once the gate is generated, it is replicated across all samples. This is only useful when each individual sample does not have
enough events to deduce the gate. Here we do this just for the purpose of proof of concept.
The fifth row specifies
cd4+/-cd8+/-, which will be expanded into 6 rows.
## alias pop parent dims gating_method gating_args ## 1: * -/++/- cd3 cd4,cd8 gate_mindensity gate_range=c(1,3) ## collapseDataForGating groupBy preprocessing_method preprocessing_args ## 1: NA NA NA
The first two rows are two 1-D gates that will be generated by
gating_method on each
## alias pop parent dims gating_method ## 1: cd4+ + /nonDebris/singlets/lymph/cd3 cd4 gate_mindensity ## 2: cd8+ + /nonDebris/singlets/lymph/cd3 cd8 gate_mindensity ## gating_args collapseDataForGating groupBy preprocessing_method ## 1: gate_range=c(1,3) ## 2: gate_range=c(1,3) ## preprocessing_args ## 1: ## 2:
Then another 4 rows are 4
rectangleGates that corresponds to the 4
quadrants in the 2-D projection (
cd4 vs cd8).
## alias pop parent dims gating_method ## 1: cd4+cd8+ ++ /nonDebris/singlets/lymph/cd3 cd4,cd8 refGate ## 2: cd4-cd8+ -+ /nonDebris/singlets/lymph/cd3 cd4,cd8 refGate ## 3: cd4+cd8- +- /nonDebris/singlets/lymph/cd3 cd4,cd8 refGate ## 4: cd4-cd8- -- /nonDebris/singlets/lymph/cd3 cd4,cd8 refGate ## gating_args ## 1: /nonDebris/singlets/lymph/cd3/cd4+:/nonDebris/singlets/lymph/cd3/cd8+ ## 2: /nonDebris/singlets/lymph/cd3/cd4+:/nonDebris/singlets/lymph/cd3/cd8+ ## 3: /nonDebris/singlets/lymph/cd3/cd4+:/nonDebris/singlets/lymph/cd3/cd8+ ## 4: /nonDebris/singlets/lymph/cd3/cd4+:/nonDebris/singlets/lymph/cd3/cd8+ ## collapseDataForGating groupBy preprocessing_method preprocessing_args ## 1: ## 2: ## 3: ## 4:
As we see here,
gating_method indicates that they are constructed based on the
gate coordinates of the previous two 1-D gates.
Those 1-D gates are thus considered as “reference gates” that are referred to by a colon-separated
alias string in
Alternatively, we can expand it into these 6 rows explicitly in the spreadsheet.
But this convenient representation is recommended unless the user wants to have finer control on how the gating is done.
For instance, sometimes we need to use different
gating_methods to generate 1-D gates on
Or it could be the case that
cd8 gating needs to depend on
cd4 gating, i.e. the
cd4-) instead of
Sometimes we want to have a customized
alias other than the quadrant-like name (
x+y+) that gets generated automatically.
(e.g. 5th row of the gating template)
After the gating template is defined in the spreadsheet, it can be loaded into R:
gt_tcell <- gatingTemplate(gtFile, autostart = 1L) gt_tcell
## --- Gating Template: default ## with 29 populations defined
Besides looking at the spreadsheet, we can examine the gating scheme by visualizing it:
As we can see, the gating scheme has been expanded as we described above.
All the colored arrows source from a
parent population and the grey arrows source from a
Once we are satisfied with the gating template, we can apply it to the actual flow data.
First of all, we load the raw FCS files into R by
ncdfFlow::read.ncdfFlowSet (it uses less memory than
flowCore::read.flowSet) and create an empty
fcsFiles <- list.files(pattern = "CytoTrol", flowDataPath, full = TRUE) ncfs <- read.ncdfFlowSet(fcsFiles) fr <- ncfs[] gs <- GatingSet(ncfs) gs
## A GatingSet with 2 samples
Then, we compensate the data. If we have compensation controls (i.e. singly stained samples), we can calculate the
compensation matrix by using the
Here we simply use the compensation matrix defined in the
compMat <- gh_get_compensations(gh) gs <- compensate(gs, compMat)
Here is one example showing the compensation outcome:
All of the stained channels need to be transformed properly before the gating.
Here we use the
flowCore::estimateLogicle method to determine the
chnls <- parameters(compMat) trans <- estimateLogicle(gs[], channels = chnls) gs <- transform(gs, trans)
Here is one example showing the transformation outcome:
Now we can apply the gating template to the data:
Optionally, we can run the pipeline in parallel to speed up gating. e.g.
gt_gating(gt_tcell, gs, mc.cores=2, parallel_type = "multicore")
After gating, there are some extra populations generated automatically by the pipeline (e.g.
We can hide these populations if we are not interested in them:
nodesToHide <- c("cd8+", "cd4+" , "cd4-cd8-", "cd4+cd8+" , "cd4+cd8-/HLA+", "cd4+cd8-/CD38+" , "cd4-cd8+/HLA+", "cd4-cd8+/CD38+" , "CD45_neg/CCR7_gate", "cd4+cd8-/CD45_neg" , "cd4-cd8+/CCR7+", "cd4-cd8+/CD45RA+" ) lapply(nodesToHide, function(thisNode) gs_pop_set_visibility(gs, thisNode, FALSE))
And rename the populations:
gs_pop_set_name(gs, "cd4+cd8-", "cd4") gs_pop_set_name(gs, "cd4-cd8+", "cd8")
Sometimes it will be helpful (especially when working with data that is already gated) to be able to interact with the
GatingSet directly without the need to write the complete csv gating template. We can apply each automated gating method using the same fields as in the
gatingTemplate, but provided as arguments to the
gs_add_gating_method function. The populations added by each of these calls to
gs_add_gating_method can be removed sequentially by
gs_remove_gating_method, which will remove all populations added by the prior call to
gs_add_gating_method. These two functions allow for interactive stagewise prototyping of a
For example, suppose we wanted to add a
CD38-/HLA- sub-population to the
cd4+cd8- population. We could do this as follows:
gs_add_gating_method(gs, alias = "non-activated cd4", pop = "--", parent = "cd4", dims = "CD38,HLA", gating_method = "tailgate") plot(gs[])
The addition of this population can then easily be undone by a call to
openCyto package allows users to specify their gating schemes and gate the data
in a data-driven fashion. It frees the scientists from the labor-intensitive manual gating routines
and increases the speed as well as the reproducibilty and objectivity of the data analysis work.