bi_NN_G: Bivariate Nearest Neighbor Based Measures of Spatial...

In spatialTIME: Spatial Analysis of Vectra Immunoflourescent Data

Bivariate Nearest Neighbor Based Measures of Spatial Clustering for IF data

Description

This function computes the nearest neighbor distribution for a particular marker relative to another marker for the observed and permuted point processes.

Usage

bi_NN_G(
  mif,
  mnames,
  r_range = seq(0, 100, 50),
  num_permutations = 50,
  edge_correction = "rs",
  keep_perm_dis = FALSE,
  exhaustive = TRUE,
  workers = 1,
  overwrite = FALSE,
  xloc = NULL,
  yloc = NULL
)

Arguments

mif	An MIF object
mnames	Character vector of marker names to estimate degree of nearest neighbor distribution
r_range	Numeric vector of potential r values this range must include 0. Note that the range selected is very different than count based measures. See details.
num_permutations	Numeric value indicating the number of permutations used. Default is 50.
edge_correction	Character value indicating the type of edge correction to use. Options include "rs" or "hans".
keep_perm_dis	Logical value determining whether or not to keep the full distribution of permuted G values
exhaustive	Logical. If TRUE then markers must be a vector and spatial measures will be computed all pairs of unique markers. If FALSE then markers must be a data.frame with the desired combinations.
workers	Integer value for the number of workers to spawn
overwrite	Logical value determining if you want the results to replace the current output (TRUE) or be to be appended (FALSE).
xloc	a string corresponding to the x coordinates. If null the average of XMin and XMax will be used
yloc	a string corresponding to the y coordinates. If null the average of YMin and YMax will be used

Value

Returns a data frame

anchor	Marker for which the distances are measured from
counted	Marker for which the distances are measured to
Theoretical CSR	Expected value assuming complete spatial randomness
Permuted CSR	Average observed G for the permuted point process
Observed	Observed value for the observed point process
Degree of Clustering Permuted	Degree of spatial clustering where the reference is the permuted estimate of CSR
Degree of Clustering Theoretical	Degree of spatial clustering where the reference is the theoretical estimate of CSR

Examples

#' #Create mif object
library(dplyr)
x <- create_mif(clinical_data = example_clinical %>% 
mutate(deidentified_id = as.character(deidentified_id)),
sample_data = example_summary %>% 
mutate(deidentified_id = as.character(deidentified_id)),
spatial_list = example_spatial,
patient_id = "deidentified_id", 
sample_id = "deidentified_sample")

#Nearest Neighbor distribution for the colocalization of CD3+CD8+ positive 
#cells and CD3+FOXP3+ positive cells where CD3+FOXP3+ is the reference cell 
#type at neighborhood size of 10,20,...,100 (zero must be included in the 
#input).

x <- bi_NN_G(mif = x, mnames = c("CD3..CD8.", "CD3..FOXP3."), 
num_permutations = 1, edge_correction = 'rs', r = seq(0,100,10),
keep_perm_dis = FALSE, workers = 1, exhaustive = TRUE) 

spatialTIME documentation built on April 1, 2023, 12:18 a.m.