compute_metrics {spatialTIME}

Calculate Count Based Measures and NN Measures of Spatial Clustering for IF data

Description

This function calculates count based Measures (Ripley's K, Besag L, and Marcon's M) of IF data to characterize correlation of spatial point process. For neareast neighbor calculations of 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

compute_metrics(
  mif,
  mnames,
  r_range = seq(0, 100, 50),
  num_permutations = 50,
  edge_correction = c("translation"),
  method = c("K"),
  k_trans = "none",
  keep_perm_dis = FALSE,
  workers = 1,
  overwrite = FALSE,
  xloc = NULL,
  yloc = NULL,
  exhaustive = T
)

Arguments

mif

An MIF object

mnames

Character vector of marker names to estimate degree of spatial clustering.

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 vector indicating the type of edge correction to use. Options for count based include "translation" or "isotropic" and for nearest neighboroOptions include "rs" or "hans".

method

Character vector indicating which count based measure (K, BiK, G, BiG) used to estimate the degree of spatial clustering. Description of the methods can be found in Details section.

k_trans

Character value of the transformation to apply to count based metrics (none, M, or L)

keep_perm_dis

Logical value determining whether or not to keep the full distribution of permuted K or 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

exhaustive

whether or not to compute all combinations of markers

Value

Returns a data.frame

Theoretical CSR

Expected value assuming complete spatial randomnessn

Permuted CSR

Average observed K, L, or M 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 permutated 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
mnames <- c("CD3..Opal.570..Positive","CD8..Opal.520..Positive",
"FOXP3..Opal.620..Positive","CD3..CD8.","CD3..FOXP3.")

# Ripley's K and nearest neighbor G for all markers with a neighborhood size 
# of  10,20,...,100 (zero must be included in the input).