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bivariate

Source: R/extract_bivariate.R
bivariate.Rd

Internal function called by extract_summary_functions to calculate a bivariate spatial summary function for a single image.

Usage

bivariate(
  mximg,
  markvar,
  mark1,
  mark2,
  r_vec,
  func = c(Kcross, Lcross, Gcross, entropy),
  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

Character string that denotes first cell type of interest.

mark2

Character string that denotes second cell type of interest.

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(Kcross, Lcross, Gcross) which denote Ripley's K, Besag's L, and nearest neighbor G function, respectively, or entropy from Vu et al, 2023.

edge_correction

Character string that denotes the edge correction method for spatial summary function. For Kcross and Lcross choose one of c("border", "isotropic", "Ripley", "translate", "none"). For Gcross 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

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

Details

[Stable]

References

Xiao, L., Ruppert, D., Zipunnikov, V., and Crainiceanu, C. (2016). Fast covariance estimation for high-dimensional functional data. Statistics and Computing, 26, 409-421. DOI: 10.1007/s11222-014-9485-x.

Vu, T., Seal, S., Ghosh, T., Ahmadian, M., Wrobel, J., & Ghosh, D. (2023). FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures. PLOS Computational Biology, 19(9), e1011490.

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

Julia Wrobel julia.wrobel@emory.edu

Alex Soupir alex.soupir@moffitt.org