univariate function

univariate

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

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

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

Returns

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

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

Author(s)

Julia Wrobel julia.wrobel@emory.edu

Alex Soupir alex.soupir@moffitt.org