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Plot stimulation gate

Source: R/plot_gate.R
stimgate_plot.Rd

Plot bivariate hex and univariate density plots for batches of samples, along with their gates.

Usage

stimgate_plot(
  ind,
  .data,
  path_project,
  marker,
  ind_lab = NULL,
  marker_lab = NULL,
  exc_min = TRUE,
  limits_expand = NULL,
  limits_equal = FALSE,
  grid = TRUE,
  grid_n_col = 2,
  show_gate = TRUE,
  min_cell = 10
)

Arguments

ind

numeric vector. Specifies indices in .data to plot.

.data

GatingSet. Same GatingSet passed to stimgate_gate.

path_project

character. Path to the project directory used for stimgate_gate.

marker

character vector of length one or two. Specifies markers (channels, really) to be plotted. If only one is passed, then only univariate plots are created.

ind_lab

named character vector. Labels for ind used in plot. Optional.

marker_lab

named character vector. Labels for marker used in plot. Optional.

exc_min

Logical. If TRUE, excludes the minimum expression values when processing the data. Default is TRUE.

limits_expand

list. Expand the limits of the plot axes. Default is NULL.

limits_equal

Logical. If TRUE, forces equal lengths of the limits.

grid

Logical. If TRUE, arranges the resulting plots in a grid format using cowplot::plot_grid. Default is TRUE.

grid_n_col

Integer. Number of columns in grid layout.

show_gate

Logical. If TRUE, overlays gate lines on the plots.|> Default is TRUE.

min_cell

integer. Minimum number of cells to be plotted. Will skip plots with fewer cells. Default is 10.

Value

A grid of plots if grid is TRUE, otherwise a list of ggplot objects.

Examples

# Create example data and run gating
example_data <- get_example_data()
#> see ?HDCytoData and browseVignettes('HDCytoData') for documentation
#> loading from cache
#> Done
#> To reload it, use 'load_gs' function
gs <- flowWorkspace::load_gs(example_data$path_gs)
path_project <- file.path(dirname(example_data$path_gs), "stimgate")

# Run gating
stimgate::stimgate_gate(
  .data = gs,
  path_project = path_project,
  pop_gate = "root",
  batch_list = example_data$batch_list,
  marker = example_data$marker
)
#> ----
#> getting base gates
#> ----
#> 
#> chnl: BC1(La139)Dd
#> getting pre-adjustment gates
#> batch 8 of 8
#> getting clustered and/or controlled gates
#> chnl: BC2(Pr141)Dd
#> getting pre-adjustment gates
#> batch 8 of 8
#> getting clustered and/or controlled gates
#> 
#> 
#> ----
#> getting single+ gates
#> ----
#> 
#> 
#> 
#> 
#> getting cyt combn frequencies
#> batch 8 of 8
#> [1] "/tmp/RtmprrFstN/stimgate_example/stimgate"

# Create plots
plots <- stimgate_plot(
  ind = example_data$batch_list[[1]], # indices in `gs` to plot
  .data = gs, # GatingSet
  path_project = path_project,
  marker = example_data$marker,
  grid = TRUE
)