Package 'CytoML'

Description

add customInfo nodes to each gate node and add BooleanAndGates

Usage

addCustomInfo(root, gs, flowEnv, cytobank.default.scale = TRUE, showHidden)

Arguments

Value

XML root node

Description

Extract channels from cytobank_experiment

Usage

ce_get_channels(x, panel_name = NULL)

Arguments

Description

Obtain the spillover matrices for the samples in a Cytobank experiment

Usage

ce_get_compensations(x)

Arguments

Value

A named list of spillover matrices

Description

Extract markers from cytobank_experiment

Usage

ce_get_markers(x, panel_name = NULL)

Arguments

Description

Obtain counts of the number of samples associated with each marker panel in a Cytobank experiment

Usage

ce_get_panels(x)

Arguments

Value

A tibble of panels with sample counts

Description

Obtain a mapping between the samples and marker panels in a Cytobank experiment

Usage

ce_get_samples(x)

Arguments

Value

A tibble with rows containing sample names and their associated panel names

Description

Obtain the transformations associated with each channel in a Cytobank experiment

Usage

ce_get_transformations(x, panel_name = NULL)

Arguments

Value

A transformerList object containing transformation objects for each transformed channel

Description

compensate a GatingSet based on the compensation information stored in graphGML object

Usage

## S4 method for signature 'GatingSet,graphGML'
compensate(x, spillover, ...)

Arguments

Value

compensated GatingSet

Description

Reconstruct the population tree from the GateSets

Usage

constructTree(flowEnv, gateInfo)

Arguments

Value

a graphNEL represent the population tree. The gate and population name are stored as nodeData in each node.

Description

These methods mirror similar accessor methods for the GatingSet class.

Usage

## S4 method for signature 'cytobank_experiment'
markernames(object)

## S4 method for signature 'cytobank_experiment'
colnames(x, do.NULL = "missing", prefix = "missing")

## S4 method for signature 'cytobank_experiment'
sampleNames(object)

## S4 method for signature 'cytobank_experiment'
pData(object)

Arguments

Description

A wrapper that parses the gatingML and FCS files (or cytobank_experiment object) into GatingSet

Usage

## Default S3 method:
cytobank_to_gatingset(x, FCS, trans = NULL, ...)

## S3 method for class 'cytobank_experiment'
cytobank_to_gatingset(x, panel_id = 1, ...)

Arguments

Value

a GatingSet

Examples

## Not run: 
acsfile <- system.file("extdata/cytobank_experiment.acs", package = "CytoML")
ce <- open_cytobank_experiment(acsfile)
xmlfile <- ce$gatingML
fcsFiles <- list.files(ce$fcsdir, full.names = TRUE)
gs <<- cytobank_to_gatingset(xmlfile, fcsFiles)
library(ggcyto)
autoplot(gs[[1]])

## End(Not run)

Description

GatingSet2cytobank –> gatingset_to_cytobank

GatingSet2flowJo –> gatingset_to_flowjo

cytobankExperiment –> open_cytobank_experiment

cytobank2GatingSet –> cytobank_to_gatingset

parseWorkspace –> flowjo_to_gatingset

getKeywords –> fj_ws_get_keywords

getSamples –> fj_ws_get_samples

getSampleGroups –> fj_ws_get_sample_groups

openDiva –> open_diva_xml

parseWorkspace –> diva_to_gatingset

Description

Return a data.frame of sample group information from a FACSDiva workspace

Usage

diva_get_samples(x)
diva_get_sample_groups(x)

Arguments

Value

A data.frame with columns tub, name, and specimen

Description

Function to parse a FACSDiva Workspace, generate a GatingHierarchy or GatingSet object, and associated flowCore gates.

Usage

diva_to_gatingset(
  obj,
  name = NULL,
  subset = NULL,
  path = obj@path,
  worksheet = c("normal", "global"),
  swap_cols = list(`FSC-H` = "FSC-W", `SSC-H` = "SSC-W"),
  verbose = FALSE,
  ...
)

Arguments

Description

Inherited from flowjo_workspace-class

Slots

version:

Object of class "character". The version of the XML workspace.

file:

Object of class "character". The file name.

.cache:

Object of class "environment". An environment for internal use.

path:

Object of class "character". The path to the file.

doc:

Object of class "XMLInternalDocument". The XML document object.

options:

Object of class "integer". The XML parsing options passed to xmlTreeParse.

See Also

GatingSet GatingHierarchy

Description

It is equivalent to the behavior of shifting the off-scale boundary events into the gate boundary that is describled in bounding transformation section of gatingML standard.

Usage

extend(
  gate,
  bound,
  data.range = NULL,
  plot = FALSE,
  limits = c("original", "extended")
)

## S3 method for class 'polygonGate'
extend(
  gate,
  bound,
  data.range = NULL,
  plot = FALSE,
  limits = c("original", "extended")
)

## S3 method for class 'rectangleGate'
extend(gate, ...)

## S3 method for class 'ellipsoidGate'
extend(gate, ...)

Arguments

Details

The advantage of extending gates instead of shifting data are two folds: 1. Avoid the extra computation each time applying or plotting the gates 2. Avoid changing the data distribution caused by adding the gates

Normally this function is not used directly by user but invoked when parsing GatingML file exported from Cytobank.

Value

a flowCore filter/gate

Examples

library(flowCore)
sqrcut <- matrix(c(300,300,600,600,50,300,300,50),ncol=2,nrow=4)
colnames(sqrcut) <- c("FSC-H","SSC-H")
pg <- polygonGate(filterId="nonDebris", sqrcut)
pg
bound <- matrix(c(100,3e3,100,3e3), 
    byrow = TRUE, nrow = 2, 
   dimnames = list(c("FSC-H", "SSC-H"), 
     c("min", "max")))
bound
pg.extened <- extend(pg, bound, plot = TRUE)

Description

Retrieve keywords associated with a workspace

Usage

fj_ws_get_keywords(obj, y, ...)

Arguments

Details

Retrieve a list of keywords from a flowjo_workspace

Value

A list of keyword - value pairs.

Examples

## Not run: 
  d<-system.file("extdata",package="flowWorkspaceData")
  wsfile<-list.files(d,pattern="manual.xml",full=TRUE)
  ws <- open_flowjo_xml(wsfile)

  fj_ws_get_samples(ws)
  res <- try(fj_ws_get_keywords(ws,"CytoTrol_CytoTrol_1.fcs"), silent = TRUE)
  print(res[[1]])
  fj_ws_get_keywords(ws, 1)

## End(Not run)

Description

Return a data frame of sample group information from a flowJo workspace

Usage

fj_ws_get_sample_groups(x)

Arguments

Details

Note that the samples with 0 populations are also included (since count populations requires traversing xml for all samples thus can be expensive) Returns a table of samples and groups defined in the flowJo workspace

Value

A data.frame containing the groupName, groupID, and sampleID for each sample in the workspace. Each sample may be associated with multiple groups.

See Also

flowjo_workspace-class flowjo_to_gatingset

Examples

## Not run: 
    #ws is a flowjo_workspace
    fj_ws_get_sample_groups(ws);
  
## End(Not run)

Description

Return a data frame of samples contained in a flowJo workspace

Usage

fj_ws_get_samples(x, group_id = NULL)

Arguments

Details

The samples with 0 populations are excluded. Returns a data.frame of samples in the flowjo_workspace, including their sampleID, name

Value

A data.frame with columns sampleID, name

Examples

## Not run: 
        #ws is a flowjo_workspace
        fj_ws_get_samples(ws);
      
## End(Not run)

Description

Objects can be created by calls of the form new("flowjo_workspace.xml", ...).

Slots

doc:

Object of class "externalptr".

See Also

GatingSet GatingHierarchy

Examples

require(flowWorkspaceData)
  d<-system.file("extdata",package="flowWorkspaceData")
  wsfile<-list.files(d,pattern="A2004Analysis.xml",full=TRUE)
  ws <- open_flowjo_xml(wsfile);
  ws
  fj_ws_get_samples(ws)

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

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'

Description

It is a R wrapper for the docker app (https://hub.docker.com/r/rglab/gs-to-flowjo)

Usage

gatingset_to_flowjo(gs, outFile, showHidden = FALSE, docker_img = NULL, ...)

Arguments

Details

Docker images for gatingset_to_flowjo will be maintained at https://gallery.ecr.aws/x4k5d9i7/cytoverse/gs-to-wsp

docker pull public.ecr.aws/x4k5d9i7/cytoverse/gs-to-wsp:latest

Value

nothing

Examples

## Not run: 
library(flowWorkspace)

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

#output to flowJo
outFile <- tempfile(fileext = ".wsp")
gatingset_to_flowjo(gs, outFile)

#or directly use the archive as the input (to avoid the extra copying inside of the wrapper)
gatingset_to_flowjo(gs_path, outFile)

## End(Not run)

Description

get children nodes

Usage

## S4 method for signature 'graphGML,character'
getChildren(obj, y)

Arguments

Value

a graphNEL node

Examples

## Not run: 
g <- read.gatingML.cytobank(xmlfile)
getChildren(g, "GateSet_722326")
getParent(g, "GateSet_722326")

## End(Not run)

Description

Extract compensation from graphGML object.

Usage

## S3 method for class 'graphGML'
getCompensationMatrices(x)

Arguments

Value

compensation object or "FCS" when compensation comes from FCS keywords

Description

get gate from the node

Usage

## S4 method for signature 'graphGML,character'
getGate(obj, y)

Arguments

Value

the gate information associated with the node

Description

get nodes from graphGML object

Usage

## S4 method for signature 'graphGML'
getNodes(x, y, order = c("default", "bfs", "dfs", "tsort"), only.names = TRUE)

Arguments

Value

It returns the node names and population names by default. Or return the entire nodeData associated with each node.

Examples

## Not run: 
g <- read.gatingML.cytobank(xmlfile)
getNodes(g)
getNodes(g, only.names = FALSE)

## End(Not run)

Description

get parent nodes

Usage

## S4 method for signature 'graphGML,character'
getParent(obj, y)

Arguments

Value

a graphNEL node

Description

Extract transformations from graphGML object.

Usage

## S3 method for class 'graphGML'
getTransformations(x, ...)

Arguments

Value

transformerList object

Description

Each node corresponds to a population(or GateSet) defined in gatingML file. The actual gate object (both global and tailored gates) is associated with each node as nodeData. Compensation and transformations are stored in graphData slot.

Details

The class simply extends the graphNEL class and exists for the purpose of method dispatching.

Description

compare the counts to cytobank's exported csv so that the parsing result can be verified.

Usage

gs_compare_cytobank_counts(
  gs,
  file,
  id.vars = c("FCS Filename", "population"),
  ...
)

Arguments

Value

a data.table (in long format) that contains the counts from openCyto and Cytobank side by side.

Examples

acsfile <- system.file("extdata/cytobank_experiment.acs", package = "CytoML")
ce <- open_cytobank_experiment(acsfile)
gs <- cytobank_to_gatingset(ce)
## verify the stats are correct
statsfile <- ce$attachments[1]
dt_merged <- gs_compare_cytobank_counts(gs, statsfile, id.vars = "population", skip = "FCS Filename")
all.equal(dt_merged[, count.x], dt_merged[, count.y], tol = 5e-4)

Description

Given the leaf node, try to find out if a collection of nodes can be matched to a path in a graph(tree) by the bottom-up searching

Usage

matchPath(g, leaf, nodeSet)

Arguments

Value

TRUE if path is found, FALSE if not path is matched.

Description

Construct a cytobank_experiment object from ACS file

Usage

open_cytobank_experiment(acs, exdir = tempfile())

Arguments

Value

cytobank_experiment object

Description

open Diva xml workspace

Usage

open_diva_xml(file, options = 0, ...)

Arguments

Value

a diva_workspace object

Examples

## Not run: 
library(flowWorkspace)
library(CytoML)
ws <- open_diva_xml(system.file('extdata/diva/PE_2.xml', package = "flowWorkspaceData"))
ws
diva_get_sample_groups(ws)
gs <- diva_to_gatingset(ws, name = 2, subset = 1)
sampleNames(gs)
gs_get_pop_paths(gs)
plotGate(gs[[1]])

## End(Not run)

Description

Open a flowJo workspace and return a flowjo_workspace object. Close a flowjo_workspace, destroying the internal representation of the XML document, and freeing the associated memory.

Usage

open_flowjo_xml(file, options = 0, sample_names_from = "keyword", ...)

Arguments

Details

Open an XML flowJo workspace file and return a flowjo_workspace object. The workspace is represented using a XMLInternalDocument object. Close a flowJoWorkpsace after finishing with it. This is necessary to explicitly clean up the C-based representation of the XML tree. (See the XML package).

Value

a flowjo_workspace object.

Examples

## Not run: 
	file<-"myworkspace.xml"
	ws<-open_flowjo_xml(file);
	ws

## End(Not run)

Description

Fcs filename and gate name stored in 'custom_info' element are beyong the scope of the gatingML standard and thus not covered by the default 'read.gatingML'.

Usage

parse.gateInfo(file, ...)

Arguments

Value

a data.frame that contains three columns: id (gateId), name (gate name), fcs (fcs_file_filename).

Description

Function to parse a flowJo Workspace, generate a GatingHierarchy or GatingSet object, and associated flowCore gates. The data are not loaded or acted upon until an explicit call to recompute() is made on the GatingHierarchy objects in the GatingSet.

Usage

parseWorkspace(obj, ...)

## S4 method for signature 'flowjo_workspace'
parseWorkspace(obj, ...)

flowjo_to_gatingset(
  ws,
  name = NULL,
  subset = list(),
  execute = TRUE,
  path = "",
  cytoset = NULL,
  backend_dir = tempdir(),
  backend = get_default_backend(),
  includeGates = TRUE,
  additional.keys = "$TOT",
  additional.sampleID = FALSE,
  keywords = character(),
  keywords.source = "XML",
  keyword.ignore.case = FALSE,
  extend_val = 0,
  extend_to = -4000,
  channel.ignore.case = FALSE,
  leaf.bool = TRUE,
  include_empty_tree = FALSE,
  skip_faulty_gate = FALSE,
  compensation = NULL,
  transform = TRUE,
  fcs_file_extension = ".fcs",
  greedy_match = FALSE,
  mc.cores = 1,
  ...
)

Arguments

Details

A flowjo_workspace is generated with a call to open_flowjo_xml(), passing the name of the xml workspace file. This returns a flowjo_workspace, which can be parsed using the flowjo_to_gatingset() method. The function can be called non-interactively by passing the index or name of the group of samples to be imported via flowjo_to_gatingset(obj,name=x), where x is either the numeric index, or the name. The subset argument allows one to select a set of files from the chosen sample group. The routine will take the intersection of the files in the sample group, the files specified in subset and the files available on disk, and import them.

Value

a GatingSet, which is a wrapper around a list of GatingHierarchy objects, each representing a single sample in the workspace. The GatingHierarchy objects contain graphNEL trees that represent the gating hierarchy of each sample. Each node in the GatingHierarchy has associated data, including the population counts from flowJo, the parent population counts, the flowCore gates generated from the flowJo workspace gate definitions. Data are not yet loaded or acted upon at this stage. To execute the gating of each data file, a call to execute() must be made on each GatingHierarchy object in the GatingSet. This is done automatically by default, and there is no more reason to set this argument to FALSE.

See Also

fj_ws_get_sample_groups,GatingSet

Examples

## Not run: 
	 #f is a xml file name of a flowJo workspace
	ws <- open_flowjo_xml(f)
 #parse the second group
	gs <- flowjo_to_gatingset(ws, name = 2); #assume that the fcs files are under the same folder as workspace

 
 gs <- flowjo_to_gatingset(ws, name = 4
                        , path = dataDir     #specify the FCS path 
                        , subset = "CytoTrol_CytoTrol_1.fcs")     #subset the parsing by FCS filename

 

 gs <- flowjo_to_gatingset(ws, path = dataDir, name = 4
                         , keywords = c("PATIENT ID", "SAMPLE ID", "$TOT", "EXPERIMENT NAME") #tell the parser to extract keywords as pData
                         , keywords.source = "XML" # keywords are extracted from xml workspace (alternatively can be set to "FCS")
                         , additional.keys = c("PATIENT ID") #use additional keywords together with FCS filename to uniquely identify samples
                         , execute = F) # parse workspace without the actual gating (can save time if just want to get the info from xml)

#subset by pData (extracted from keywords)
gs <- flowjo_to_gatingset(ws, path = dataDir, name = 4
                         , subset = `TUBE NAME` %in% c("CytoTrol_1", "CytoTrol_2")
                         , keywords = "TUBE NAME")


#overide the default compensation defined in xml with the customized compenstations
gs <- flowjo_to_gatingset(ws, name = 2, compensation = comps); #comp is either a compensation object or a list of compensation objects

## End(Not run)

Description

The node with dotted order represents the population that has tailored gates (sample-specific gates) defined.

Usage

## S4 method for signature 'graphGML,missing'
plot(x, y = "missing", label = c("popName", "gateName"))

Arguments

Value

nothing

Examples

## Not run: 
g <- read.gatingML.cytobank(xmlfile)
plot(g)

## End(Not run)

Description

the parameter range from the flow data associated with GatingHierarchy

Usage

## S3 method for class 'GatingHierarchy'
range(..., na.rm = FALSE, type = c("instrument", "data"), raw.scale = FALSE)

Arguments

Value

matrix

Examples

## Not run: 
 range(gh, type = "data")#return data range
 range(gh) #return instrument range
 range(gh, raw.scale = TRUE) #inverse transform the range to the raw scale

## End(Not run)

Description

The Default parser (read.gatingML) does not parse the population tree as well as the custom information from cytobank. (e.g. gate name, fcs filename).

Usage

read.gatingML.cytobank(file, ...)

Arguments

Value

a graphGML that represents the population tree. The gate and population name are stored in nodeData of each node. Compensation and transformations are stored in graphData.

Examples

## Not run: 
g <- read.gatingML.cytobank(xml) #parse the population tree
#plot(g) #visualize it

## End(Not run)

Description

show method for graphGML

Usage

## S4 method for signature 'graphGML'
show(object)

Arguments

Value

nothing