mxfda (version 0.2.2-1)

univariate: univariate

Description

Internal function called by extract_summary_functions() to calculate a univariate spatial summary function for a single image.

Usage

univariate(
  mximg,
  markvar,
  mark1,
  mark2,
  r_vec,
  func = c(Kest, Lest, Gest),
  edge_correction,
  empirical_CSR = FALSE,
  permutations = 1000
)

Value

A data.frame containing:

r

the radius of values over which the spatial summary function is evaluated

sumfun

the values of the spatial summary function

csr

the values of the spatial summary function under complete spatial randomness

fundiff

sumfun - csr, positive values indicate clustering and negative values repulsion

Arguments

mximg

Dataframe of cell-level multiplex imaging data for a single image. Should have variables x and y to denote x and y spatial locations of each cell.

markvar

The name of the variable that denotes cell type(s) of interest. Character.

mark1

dummy filler, unused

mark2

dummy filler, unused

r_vec

Numeric vector of radii over which to evaluate spatial summary functions. Must begin at 0.

func

Spatial summary function to calculate. Options are c(Kest, Lest, Gest) which denote Ripley's K, Besag's L, and nearest neighbor G function, respectively.

edge_correction

Character string that denotes the edge correction method for spatial summary function. For Kest and Lest choose one of c("border", "isotropic", "Ripley", "translate", "none"). For Gest choose one of c("rs", "km", "han")

empirical_CSR

logical to indicate whether to use the permutations to identify the sample-specific complete spatial randomness (CSR) estimation.

permutations

integer for the number of permtuations to use if empirical_CSR is TRUE and exact CSR not calculable

Author

Julia Wrobel

Alex Soupir

Details

References

Creed, J. H., Wilson, C. M., Soupir, A. C., Colin-Leitzinger, C. M., Kimmel, G. J., Ospina, O. E., Chakiryan, N. H., Markowitz, J., Peres, L. C., Coghill, A., & Fridley, B. L. (2021). spatialTIME and iTIME: R package and Shiny application for visualization and analysis of immunofluorescence data. Bioinformatics (Oxford, England), 37(23), 4584–4586. https://doi.org/10.1093/bioinformatics/btab757