Package 'openCyto'

Description

It is the inverse function of gatingTemplate constructor.

Usage

## S3 method for class 'gatingTemplate'
as.data.table(x, keep.rownames = FALSE)

Arguments

Value

a data.table

Description

It extends refGate class.

Description

get gating method dimensions

Usage

## S4 method for signature 'gtMethod'
dims(x)

Arguments

Description

It is generated automatically by the csv template preprocessing to handle the gating function that returns multiple gates.

Description

rewritten in c++, till eval stats::lm.wfit r function in underlying cpp11 code It is internally used for singletGate, thus its output format may not be generic enough for common model fitting . e.g. it doesn't take formula as input

Usage

fast_rlm(x, y, maxit = 20)

Arguments

Description

constuctor for fcEllipsoidGate

Usage

fcEllipsoidGate(x, priors, posts)

Arguments

Description

It stores priors and posteriors as well as the actual ellipsoidGate.

Description

Bascially it extends flowCore 'filter classes to have extra slot to store priors and posteriors

Description

constuctor for fcFilterList

Usage

fcFilterList(x)

Arguments

Description

Each filter in the filterList must extends the fcFilter class

Description

constuctor for fcPolygonGate

Usage

fcPolygonGate(x, priors, posts)

Arguments

Description

It stores priors and posteriors as well as the actual polygonGate.

Description

constuctor for fcRectangleGate

Usage

fcRectangleGate(x, priors, posts)

Arguments

Description

It stores priors and posteriors as well as the actual rectangleGate.

Description

It adds an extra node data slot "fList"(which is a filterList object) to the gatingTemplate

Usage

fcTree(gt)

Arguments

Description

It is a graphNEL used as a container to store priors and posteriors for each flowClust gate that can be visualized for the purpose of fine-tunning parameters for flowClust algorithm

Description

We cluster the observations in fr into K clusters.

Usage

gate_flowclust_1d(
  fr,
  params,
  filterId = "",
  K = NULL,
  trans = 0,
  min.count = -1,
  max.count = -1,
  nstart = 1,
  prior = NULL,
  criterion = c("BIC", "ICL"),
  cutpoint_method = c("boundary", "min_density", "quantile", "posterior_mean",
    "prior_density"),
  neg_cluster = 1,
  cutpoint_min = NULL,
  cutpoint_max = NULL,
  min = NULL,
  max = NULL,
  quantile = 0.99,
  quantile_interval = c(0, 10),
  plot = FALSE,
  debug = FALSE,
  ...
)

Arguments

Details

By default, the cutpoint is chosen to be the boundary of the first two clusters. That is, between the first two cluster centroids, we find the midpoint between the largest observation from the first cluster and the smallest observations from the second cluster. Alternatively, if the cutpoint_method is min_density, then the cutpoint is the point at which the density between the first and second smallest cluster centroids is minimum.

Value

a rectangleGate object consisting of all values beyond the cutpoint calculated

Examples

## Not run: 
 gate <- gate_flowclust_1d(fr, params = "APC-A", K =2) # fr is a flowFrame

## End(Not run)

Description

We cluster the observations in fr into K clusters. We set the cutpoint to be the point at which the density between the first and second smallest cluster centroids is minimum.

Usage

gate_flowclust_2d(
  fr,
  xChannel,
  yChannel,
  filterId = "",
  K = 2,
  usePrior = "no",
  prior = list(NA),
  trans = 0,
  min.count = -1,
  max.count = -1,
  nstart = 1,
  plot = FALSE,
  target = NULL,
  transitional = FALSE,
  quantile = 0.9,
  translation = 0.25,
  transitional_angle = NULL,
  min = NULL,
  max = NULL,
  ...
)

Arguments

Details

The cluster for the population of interest is selected as the one with cluster centroid nearest the target in Euclidean distance. By default, the largest cluster (i.e., the cluster with the largest proportion of observations) is selected as the population of interest.

We also provide the option of constructing a transitional gate from the selected population of interest. The location of the gate can be controlled with the translation argument, which translates the gate along the major axis of the targest cluster as a function of the appropriate chi-squared coefficient. The larger translation is, the more gate is shifted in a positive direction. Furthermore, the width of the transitional gate can be controlled with the quantile argument.

The direction of the transitional gate can be controlled with the transitional_angle argument. By default, it is NULL, and we use the eigenvector of the target cluster that points towards the first quadrant (has positive slope). If transitional_angle is specified, we rotate the eigenvectors so that the angle between the x-axis (with the cluster centroid as the origin) and the major eigenvector (i.e., the eigenvector with the larger eigenvalue) is transitional_angle. So based on range that the angle falls in, the final rectangleGate will be constructed at the corresponding quadrant. i.e. Clockwise, [0,pi/2] UR, (pi/2, pi] LR, (pi, 3/2 * pi] LL, (3/2 * pi, 2 * pi] UL

Value

a polygonGate object containing the contour (ellipse) for 2D gating.

Examples

## Not run: 
 gate <- gate_flowclust_2d(fr, xChannel = "FSC-A", xChannel = "SSC-A", K = 3) # fr is a flowFrame

## End(Not run)

Description

We fit a kernel density estimator to the cells in the flowFrame and identify the two largest peaks. We then select as the cutpoint the value at which the minimum density is attained between the two peaks of interest.

Usage

gate_mindensity(
  fr,
  channel,
  filterId = "",
  positive = TRUE,
  gate_range = NULL,
  min = NULL,
  max = NULL,
  peaks = NULL,
  ...
)

Arguments

Details

In the default case, the two peaks of interest are the two largest peaks obtained from the link{density} function.

In the special case that there is only one peak, we are conservative and set the cutpoint as the min(x) if positive is TRUE, and the max(x) otherwise.

Value

a rectangleGate object based on the minimum density cutpoint

Examples

## Not run: 
 gate <- gate_mindensity(fr, channel = "APC-A") # fr is a flowFrame

## End(Not run)

Description

Analogous to the original openCyto::mindensity(), mindensity2 operates on a standard flowFrame. Its behavior is closely modeled on the original mindensity() whenever possible. However, the underlying peak-finding algorithm (improvedMindensity) behaves significantly differently.

Usage

gate_mindensity2(
  fr,
  channel,
  filterId = "",
  gate_range = NULL,
  min = NULL,
  max = NULL,
  peaks = NULL,
  ...
)

Arguments

Value

a rectangleGate object based on the minimum density cutpoint

Author(s)

Greg Finak, Phu T. Van

Examples

## Not run: 
 gate <- gate_mindensity2(fr, channel = "APC-A") # fr is a flowFrame

## End(Not run)

Description

The order of 1d-gating is determined so that the gates better capture the distributions of flow data.

Usage

gate_quad_sequential(fr, channels, gFunc, min = NULL, max = NULL, ...)

Arguments

Value

a filters that contains four rectangleGates

Description

This gating method identifies two quadrants (first, and third quadrants) by fitting the data with tmixture model. It is particually useful when the two markers are not well resolved thus the regular quadGate method based on 1d gating will not find the perfect cut points on both dimensions.

Usage

gate_quad_tmix(
  fr,
  channels,
  K,
  usePrior = "no",
  prior = list(NA),
  quantile1 = 0.8,
  quantile3 = 0.8,
  trans = 0,
  plot = FALSE,
  ...
)

Arguments

Value

a filters object that contains four polygonGates following the order of (-+,++,+-,–)

Description

It is possible that the cutpoint calculated by quantile function may not produce the exact the probability set by 'probs' argument if there are not enough cell events to reach that precision. Sometime the difference could be significant.

Usage

gate_quantile(
  fr,
  channel,
  probs = 0.999,
  plot = FALSE,
  filterId = "",
  min = NULL,
  max = NULL,
  ...
)

Arguments

Value

a rectangleGate

Examples

## Not run: 
 gate <- gate_quantile(fr, Channel = "APC-A", probs = 0.995) # fr is a flowFrame

## End(Not run)

Description

We construct a singlet gate by applying a robust linear model. By default, we model the forward-scatter height (FSC-H)as a function of forward-scatter area (FSC-A). If sidescatter is given, forward-scatter height is as a function of area + sidescatter + sidescatter / area.

Usage

gate_singlet(
  x,
  area = "FSC-A",
  height = "FSC-H",
  sidescatter = NULL,
  prediction_level = 0.99,
  subsample_pct = NULL,
  wider_gate = FALSE,
  filterId = "singlet",
  maxit = 5,
  ...
)

Arguments

Details

Because rlm relies on iteratively reweighted least squares (IRLS), the runtime to construct a singlet gate is dependent in part on the number of observations in x. To improve the runtime, we provide an option to subsample randomly a subset of x. A percentage of observations to subsample can be given in subsample_pct. By default, no subsampling is applied.

Value

a polygonGate object with the singlet gate

Description

Each cell population is stored in graph node and is connected with its parent population or its reference node for boolGate or refGate.

It parses the csv file that specifies the gating scheme for a particular staining pannel.

Usage

gatingTemplate(x, ...)

## S4 method for signature 'character'
gatingTemplate(
  x,
  name = "default",
  strict = TRUE,
  strip_extra_quotes = FALSE,
  ...
)

## S4 method for signature 'data.table'
gatingTemplate(
  x,
  name = "default",
  strict = TRUE,
  strip_extra_quotes = FALSE,
  ...
)

Arguments

Details

This csv must have the following columns:

'alias': a name used label the cell population, the path composed by the alias and its precedent nodes (e.g. /root/A/B/alias) has to be uniquely identifiable. So alias can not contain '/' character, which is reserved as path delimiter.

'pop': population patterns of '+/-‘ or ’+/-+/-', which tells the algorithm which side (postive or negative) of 1d gate or which quadrant of 2d gate to be kept.

'parent': the parent population alias, its path has to be uniquely identifiable.

'dims': characters seperated by comma specifying the dimensions(1d or 2d) used for gating. It can be either channel name or stained marker name (or the substrings of channel/marker names as long as they are uniquely identifiable.).

'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. '.flowClust')

'gating_args': the named arguments passed to gating function (Note that double quotes are often used as text delimiter by some csv editors. So try to use single quote instead if needed.)

'collapseDataForGating': When TRUE, data is collapsed (within groups if 'groupBy' specified) before gating and the gate is replicated across collapsed samples. When set FALSE (or blank),then 'groupBy' argument is only used by 'preprocessing' and ignored by gating.

'groupBy': If given, samples are split into groups by the unique combinations of study variable (i.e. column names of pData,e.g."PTID:VISITNO"). when split is numeric, then 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 gating wrapper function through 'pps_res' argument.

'preprocessing_args': the named arguments passed to preprocessing function.

Examples

## Not run:  
  gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))
  plot(gt)

## End(Not run)

Description

get gates saved in fcTree

Usage

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

Arguments

Description

get nodes from fcTree

Usage

## S4 method for signature 'fcTree'
getNodes(x, y)

Arguments

Description

To ease the process of replicating the existing (usually a manual one) gating schemes, this function populate an empty gating template with the 'alias', 'pop', 'parent' and 'dims' columns that exacted from an GatingHierarchy, and leave the other columns (e.g. 'gating_method') blank. So users can make changes to that template instead of writing from scratch.

Usage

gh_generate_template(gh)

Arguments

Value

a gating template in data.frame format that requires further edition after output to csv

Examples

library(flowWorkspace)
dataDir <- system.file("extdata",package="flowWorkspaceData")
gs <- load_gs(list.files(dataDir, pattern = "gs_manual",full = TRUE))
gh_generate_template(gs[[1]])

Description

When specified, the flow data is grouped by the grouping variable (column names in pData). Within each group, when isCollapse is set to TRUE, the gating method is applied to the collapsed data. Otherwise, it is done indepentently for each indiviudal sample(flowFrame). Grouping variable is also used by preprocessing method.

Usage

## S4 method for signature 'gtMethod'
groupBy(object)

Arguments

Description

When interacting with the existing gated data, this function provides an alternative way to interact with the GatingSet by supplying the gating description directly through arguments without the need to write the complete csv gating template.

Usage

gs_add_gating_method(
  gs,
  alias = "*",
  pop = "+",
  parent,
  dims = NA,
  gating_method,
  gating_args = NA,
  collapseDataForGating = NA,
  groupBy = NA,
  preprocessing_method = NA,
  preprocessing_args = NA,
  strip_extra_quotes = FALSE,
  ...
)

Arguments

Details

Calls to gs_add_gating_method can also be easily reversed with gs_remove_gating_method. Note, however, that it is not possible to differentiate between different GatingSet objects loaded from the same directory with load_gs within a session. Thus, to guarantee a clean history for gs_remove_gating_method, it is necessary to call gs_add_gating_method_init on the loaded GatingSet immediately after re-loading it. See the documentation for gs_add_gating_method_init for more details. This will not be an issue for GatingSet objects created directly using the constructor.

See Also

gs_remove_gating_method gs_add_gating_method_init

Examples

## Not run: 
# add quad gates 
gs_add_gating_method(gs, gating_method = "mindensity", dims = "CCR7,CD45RA", parent = "cd4-cd8+", pop = "CCR7+/-CD45RA+/-")

# polyfunctional gates (boolean combinations of exsiting marginal gates)
gs_add_gating_method(gs, gating_method = "polyFunctions", parent = "cd8", gating_args = "cd8/IFNg:cd8/IL2:cd8/TNFa")

#boolGate method
gs_add_gating_method(gs, alias = "IL2orIFNg", gating_method = "boolGate", parent = "cd4", gating_args = "cd4/IL2|cd4/IFNg") 

## End(Not run)

Description

Repeated calls to the load_gs method in the same session will yield indistinguishable objects that can result in overlapping history of gs_add_gating_method calls. This method allows for the history to be cleared if the user would like to reload the GatingSet and start fresh. Calling gs_add_gating_method_init without an argument will clear the entire gs_add_gating_method history.

Usage

gs_add_gating_method_init(gs)

Arguments

Examples

## Not run: 
# load in a GatingSet
gs <- load_gs(path)
# Add some nodes using gs_add_gating_method
gs_add_gating_method(gs, gating_method = "mindensity", dims = "CCR7,CD45RA", parent = "cd4-cd8+", pop = "CCR7+/-CD45RA+/-")
gs_add_gating_method(gs, gating_method = "polyFunctions", parent = "cd8", gating_args = "cd8/IFNg:cd8/IL2:cd8/TNFa")
# Remove the effect of the last gs_add_gating_method call using gs_remove_gating_method (note that the first call's effects remain)
gs_remove_gating_method(gs)
# Re-load the GatingSet to start over
gs <- load_gs(path)

# At this point, gs will still see the history of the first gs_add_gating_method call above
# which will cause problems for later calls to gs_remove_gating_method.
# To fix that, just call gs_add_gating_method_init() to start a clean history
gs_add_gating_method_init(gs)
# Now you can continue using gs_add_gating_method and gs_remove_gating_method from scratch
gs_add_gating_method(gs, gating_method = "mindensity", dims = "CCR7,CD45RA", parent = "cd4-cd8+", pop = "CCR7+/-CD45RA+/-")

## End(Not run)

Description

This function provides an easy way to remove the gates and nodes created by the most recent call to gs_add_gating_method on the specified GatingSet or GatingSetList, with a separate history being maintained for each such object. gs_remove_gating_method allows for repeated use, effectively serving as a multi-level undo function for gs_add_gating_method.

Usage

gs_remove_gating_method(gs)

Arguments

See Also

gs_add_gating_method gs_add_gating_method_init

Examples

## Not run: 
 # add quad gates 
 gs_add_gating_method(gs, gating_method = "mindensity", dims = "CCR7,CD45RA", parent = "cd4-cd8+", pop = "CCR7+/-CD45RA+/-")
 # Remove the gates and nodes resulting from that gs_add_gating_method call
 gs_remove_gating_method(gs)

## End(Not run)

Description

It loads the gating methods by topological order and applies them to GatingSet.

Usage

gt_gating(x, y, ...)

Arguments

Value

Nothing. As the side effect, gates generated by gating methods are saved in GatingSet.

Examples

## Not run: 
 gt <- gatingTemplate(file.path(path, "data/ICStemplate.csv"), "ICS")
 gs <- GatingSet(fs) #fs is a flowSet/ncdfFlowSet
 gt_gating(gt, gs)
 gt_gating(gt, gs, stop.at = "v") #proceed the gating until population 'v'
 gt_gating(gt, gs, start = "v") # start from 'v'
 gt_gating(gt, gs, parallel_type = "multicore", mc.cores = 8) #parallel gating using multicore
 #parallel gating by using cluster
 cl1 <- makeCluster (8, type = "MPI")
 gt_gating(gt, gs, parallel_type = "cluster", cl = cl1)
 stopCluster ( cl1 )

## End(Not run)

Description

get children nodes

Usage

gt_get_children(obj, y)

Arguments

Examples

## Not run: 
gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))

gt_get_nodes(gt, "/nonDebris")
gt_get_children(gt, "/nonDebris") 

## End(Not run)

Description

get gating method from the node

Usage

gt_get_gate(obj, y, z)

Arguments

Examples

## Not run: 
gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))
gt_get_nodes(gt, only.names = TRUE)
gt_get_nodes(gt, "/nonDebris")
gt_get_children(gt, "/nonDebris")
gt_get_gate(gt, "/nonDebris", "/nonDebris/singlets")

## End(Not run)

get nodes from gatingTemplate object

Description

get nodes from gatingTemplate object

Usage

gt_get_nodes(
  x,
  y,
  order = c("default", "bfs", "dfs", "tsort"),
  only.names = FALSE
)

Arguments

Examples

## Not run: 
gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))
gt_get_nodes(gt)[1:2]
gt_get_nodes(gt, only.names = TRUE)
gt_get_nodes(gt, "/nonDebris")

## End(Not run)

Description

get parent nodes

Usage

gt_get_parent(obj, y, isRef = FALSE)

Arguments

Examples

## Not run: 
gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))

gt_get_nodes(gt, "/nonDebris")
gt_get_parent(gt, "/nonDebris/singlets") 

## End(Not run)

Description

Print a list of the registered gating methods

Usage

gt_list_methods()

Value

Does not return anything. Prints a list of the available gating methods.

Description

The helper gates are defined as the referred gates in csv template. And all the chidlren of referred gates are also referred gates thus they are considered the helper gates and can usually be hidden to simply the final gating tree.

Usage

gt_toggle_helpergates(gt, gs)

gt_get_helpergates(gt, gs)

gt_delete_helpergates(gt, gs)

Arguments

Details

Note that delete action is NOT reversible.

Examples

## Not run: 
gt <- gatingTemplate(gtFile)
#run the gating
gt_gating(gt, gs)
#hide the gates that are not of interest
gt_toggle_helpergates(gt, gs) 
#or simply remove them if you are sure they will not be useful in future
gt_delete_helpergates(gt, gs) 

## End(Not run)

Description

A gating method object contains the specifics for generating the gates.

Slots

name

a character specifying the name of the gating method

dims

a character vector specifying the dimensions (channels or markers) of the gate

args

a list specifying the arguments passed to gating function

groupBy

a character or integer specifying how to group the data. If character, group the data by the study variables (columns in pData). If integer, group the data by every N samples.

collapse

a logical specifying wether to collapse the data within group before gating. it is only valid when groupBy is specified

Examples

## Not run: 
     gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))
     gh_pop_get_gate(gt, '2', '3')

## End(Not run)

Description

A class to represent a cell population that will be generated by a gating method.

Slots

id

numeric unique ID that is consistent with node label of graphNEL in gating template

name

character the name of population

alias

character the more user friendly name of population

Examples

## Not run: 
     gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))
      
     gt_get_nodes(gt, '2')

## End(Not run)

Description

It extends gtPopulation class.

Description

When TRUE, the flow data(multiple flowFrames) is collapsed into one and the gating method is applied on the collapsed data. Once the gate is generated, it is thenreplicated and applied to the each single flowFrame.

Usage

## S4 method for signature 'gtMethod'
isCollapse(object)

Arguments

Value

logical

Description

get gating method name

Usage

## S4 method for signature 'gtMethod'
names(x)

Arguments

Examples

## Not run: 
gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))

gtMthd <- gt_get_gate(gt, "/nonDebris/singlets",  "/nonDebris/singlets/lymph")
names(gtMthd) 
dims(gtMthd)
parameters(gtMthd)
isCollapse(gtMthd)
groupBy(gtMthd)

gtPop <- gt_get_nodes(gt, "/nonDebris/singlets/lymph/cd3/cd4+cd8-/CD38+")
names(gtPop)
alias(gtPop)

## End(Not run)

Description

get population name

Usage

## S4 method for signature 'gtPopulation'
names(x)

Arguments

Description

the class that carries event indices as well

Description

constructor for ocRectRefGate

Usage

ocRectRefGate(rectGate, boolExprs)

Arguments

Description

special gate type that mix the rectangleGate with boolean gate

Description

Hierarchical Gating Pipeline for flow cytometry data.

Details

openCyto is a package designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy.

Author(s)

Mike Jiang [email protected], John Ramey [email protected], Greg Finak [email protected]

Maintainer: Mike Jiang [email protected]

See Also

See gt_gating, gate_flowclust_1d, for an overview of gating functions.

Examples

## Not run: gatingTemplate('test.csv')

Description

add_pop –> gs_add_gating_method

add_pop_init –> gs_add_gating_method_init

prior_flowClust –> prior_flowclust

templateGen –> gh_generate_template

gate_flowClust_1d –> gate_flowclust_1d

gate_flowClust_2d –> gate_flowclust_2d

quantileGate –> gate_quantile

quadGate.seq –> gate_quad_sequential

quadGate.tmix –> gate_quad_tmix

gating –> gt_gating

getNodes –> gt_get_nodes

getChildren –> gt_get_children

getParent –> gt_get_parent

getGate –> gt_get_gate

listgtMethods –> gt_list_methods

registerPlugins –> register_plugins

remove_pop –> gs_remove_gating_method

toggle.helperGates –> gt_toggle_helpergates

get.helperGates –> gt_get_helpergates

delete.helperGates –> gt_delete_helpergates

Description

Get/set some global options for openCyto

Examples

opt <- getOption("openCyto")
#the threshold of minimum cell events required for the gating algorithm to proceed
opt[["gating"]][["minEvents"]]
#to change the threshold
opt[["gating"]][["minEvents"]] <- 100
options(openCyto = opt)

#switch off the validity check flags(Not recommended)
opt[["check.pop"]] <- FALSE
options(openCyto = opt)

Description

get parameters of the gating method/function

Usage

## S4 method for signature 'gtMethod'
parameters(object)

Arguments

Description

It is usually called by plot method for fcTree instead of directly by users.

Usage

## S4 method for signature 'fcFilterList,ANY'
plot(
  x,
  y,
  samples = NULL,
  posteriors = FALSE,
  xlim = NULL,
  ylim = NULL,
  node = NULL,
  data = NULL,
  breaks = 20,
  lwd = 1,
  ...
)

Arguments

Examples

## Not run:  
 env1<-new.env(parent=emptyenv())
 #gt is a gatingTemplate, gs is a GatingSet
 gt_gating(gt,gs,env1) #the flowClust gating results are stored in env1 
 plot(env1$fct,"nonDebris",post=T) #plot the priors as well as posteriors for the "nonDebris" gate

## End(Not run)

Description

This provides the priors and posteriors as well as the gates for the purpose of debugging flowClust gating algorithm

Usage

## S4 method for signature 'fcTree,character'
plot(x, y, channel = NULL, data = NULL, ...)

Arguments

Description

plot the gating scheme using Rgraphviz

Usage

## S4 method for signature 'gatingTemplate,missing'
plot(x, y, ...)

Arguments

Examples

## Not run: 
gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))
plot(gt) #plot entire tree
plot(gt, "lymph") #only plot the subtree rooted from "lymph"

## End(Not run)

Description

It extends boolMethod class and will be expanded to multiple boolMethod object.

Description

to support adding gate along with indices without loading flow data and computing

to support adding rectangleGate yet gating through boolean operations without loading flow data

Usage

## S3 method for class 'ocRectangleGate'
pop_add(gate, gh, recompute, ...)

## S3 method for class 'ocRectRefGate'
pop_add(gate, gh, recompute, ...)

Arguments

Details

however it is proven that logical indices are too big to be efficiently passed around

Description

get posteriors from a fcFilter object

Usage

## S4 method for signature 'fcFilter,ANY'
posteriors(x, y = "missing")

Arguments

Description

It extends gtMethod class.

Examples

## Not run: 
     gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))
     ppMethod(gt, '3', '4')

## End(Not run)

Description

get preprocessing method from the node

Usage

## S4 method for signature 'gatingTemplate,character'
ppMethod(obj, y, z)

Arguments

Examples

## Not run: 
gt <- gatingTemplate(system.file("extdata/gating_template/tcell.csv",package = "openCyto"))
ppMethod(gt,  "/nonDebris/singlets",  "/nonDebris/singlets/lymph")

## End(Not run)

apply a ppMethod to the GatingSet

Description

apply a ppMethod to the GatingSet

Usage

## S4 method for signature 'ppMethod,GatingSet'
preprocessing(x, y, ...)

Arguments

Description

We elicit data-driven prior parameters from a flowSet object for specified channels. For each sample in the flowSet object, we apply the given prior_method to elicit the priors parameters.

Usage

prior_flowclust(
  flow_set,
  channels,
  prior_method = c("kmeans"),
  K = 2,
  nu0 = 4,
  w0 = c(10, 10),
  shrink = 1e-06,
  ...
)

Arguments

Details

Currently, we have implemented only two methods. In the case that one channel is given, we use the kernel-density estimator (KDE) approach for each sample to obtain K peaks from which we elicit prior parameters. Otherwise, if more than one channel is specified, we apply K-Means to each of the samples in the flowSet and aggregate the clusters to elicit the prior parameters.

In the rare case that a prior covariance matrix is singular, we shrink the eigenvalues of the matrix slightly to ensure that it is positive definite. For instance, if the flow_set has two samples, this case can occur. The amount of shrinkage is controlled in shrink.

Value

list of the necessary prior parameters

Examples

## Not run: 
library(flowCore)
data(GvHD)
prior_flowclust(GvHD[1:3], c("FSC-H", "SSC-H"))

## End(Not run)

Description

get priors from a fcFilter object

Usage

## S4 method for signature 'fcFilter,ANY'
priors(x, y = "missing")

Arguments

Description

It extends gtMethod class.

Slots

refNodes

character specifying the reference nodes

Description

Function registers a new gating or preprocessing method with openCyto so that it may be used in the csv template.

Usage

register_plugins(fun = NA, methodName, dep = NA, ...)

Arguments

Details

The fun argument should be a wrapper function definition for the gating or preprocessing method. Gating method must have formal arguments:

fr a flowFrame

pp_res a pre-processing result

xChannel character (optional)

yChannel character (required)

filterId character

... ellipses for the additional parameters.

Preprocessing method must have formal arguments:

fs a flowSet that stores the flow data (could be subgrouped data if groupBy column is defined in the csv template

gs a GatingSet

gm a gtMethod object that stores the information from gating method

xChannel character (required)

yChannel character (required)

... ellipses for the additional parameters.

The gating function must return a filter (i.e. polygonGate or other instance) from flowCore. The preprocessing can return anything and it will be passed on to the gating function. So it is up to gating function to use and interpret the results of preprocessing. Not all formal parameters need to be used. Additional arguments are passed via the ... and can be processed in the wrapper

Value

logical TRUE if successful and prints a message. FALSE otherwise.

Description

rewrite huber estimator

Usage

robust_m_estimator(x, sd)

Description

show method for boolMethod

Usage

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

Arguments

Description

show method for fcFilter

Usage

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

Arguments

Description

show method for gatingTemplate

Usage

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

Arguments

Description

show method for gtMethod

Usage

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

Arguments