NN_G: Nearest Neighbor Based Measures of Spatial Clustering for IF...

In spatialTIME: Spatial Analysis of Vectra Immunoflourescent Data

Nearest Neighbor Based Measures of Spatial Clustering for IF data

Description

For a given cell type, this function computes proportion of cells that have nearest neighbor less than r for the observed and permuted point processes.

Usage

NN_G(
  mif,
  mnames,
  r_range = seq(0, 100, 50),
  num_permutations = 50,
  edge_correction = "rs",
  keep_perm_dis = FALSE,
  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.
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
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

Theoretical CSR	Expected value assuming complete spatial randomnessn
Permuted CSR	Average observed G for the permuted point process
Observed	Observed valuefor 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")

# Define the set of markers to study
markers <- c("CD3..Opal.570..Positive","CD8..Opal.520..Positive",
"FOXP3..Opal.620..Positive","CD3..CD8.","CD3..FOXP3.")

# Nearest Neighbor distribution for all markers with a neighborhood size 
# of  10,20,...,100 (zero must be included in the input).


x <- NN_G(mif = x, mnames = markers[1:2], num_permutations = 1,
edge_correction = 'rs', r = seq(0,100,10),
keep_perm_dis = FALSE, workers = 1)

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