Evaluation of Kontextual over a range of radii.

This function obtains `Kondtional` values over a range of radii, standard deviations for each value can be obtained using permutation for significance testing. To obtain estimates for standard deviations specify `se = TRUE`.

Usage

kontextCurve(
  cells,
  from,
  to,
  parent,
  image = NULL,
  rs = seq(10, 100, 10),
  inhom = TRUE,
  edge = FALSE,
  se = FALSE,
  nSim = 20,
  cores = 1,
  imageID = "imageID",
  cellType = "cellType",
  ...
)

Arguments

cells: A single image from a SingleCellExperiment object
from: The first cell type to be evaluated in the pairwise relationship.
to: The second cell type to be evaluated in the pairwise relationship.
parent: The parent population of the from cell type (must include from cell type).
image: A vector of images to subset the results to. If NULL we default to all images.
rs: A vector of radii to evaluate kontextual over.
inhom: A logical value indicating whether to perform an inhomogeneous L function.
edge: A logical value indicating whether to perform edge correction.
se: A logical value to indicate if the standard deviation of kontextual should be calculated to construct error bars.
nSim: Number of randomisations to perform using relabelKontextual, which will be used to calculated the SE.
cores: Number of cores for parallel processing.
imageID: The column in colData that stores the image ids.
cellType: The column in colData that stores the cell types.
...: Any arguments passed into Kontextual.

Value

A data frame of original L values and Kontextual values evaluated over a range of radii.

Examples


data("kerenSCE")

kerenImage6 = kerenSCE[, kerenSCE$imageID =="6"]

rsDf <- kontextCurve(
  cells = kerenSCE,
  from = "CD4_Cell",
  to = "Keratin_Tumour",
  parent = c("CD4_Cell", "Macrophages"),
  rs = seq(10, 510, 100),
  cores = 2
)