NN_G | R Documentation |
Univariate Nearest Neighbor G(r)
NN_G(
mif,
mnames,
r_range = 0:100,
num_permutations = 50,
edge_correction = "rs",
keep_perm_dis = FALSE,
workers = 1,
overwrite = FALSE,
xloc = NULL,
yloc = NULL
)
mif |
object of class 'mif' created by function 'create_mif()' |
mnames |
character vector of column names within the spatial files, indicating whether a cell row is positive for a phenotype |
r_range |
numeric vector of radii around marker positive cells which to use for G(r) |
num_permutations |
integer number of permutations to use for estimating core specific complete spatial randomness (CSR) |
edge_correction |
character vector of edge correction methods to use: "rs", "km" or "han" |
keep_perm_dis |
boolean for whether to summarise permutations to a single value or maintain each permutations result |
workers |
integer number for the number of CPU cores to use in parallel to calculate all samples/markers |
overwrite |
boolean whether to overwrite previous run of NN G(r) or increment "RUN" and maintain previous measurements |
xloc , yloc |
the x and y location columns in the spatial files that indicate the center of the respective cells |
object of class 'mif' containing a new slot under 'derived' got nearest neighbor distances
library(dplyr)
x <- spatialTIME::create_mif(clinical_data = spatialTIME::example_clinical %>%
dplyr::mutate(deidentified_id = as.character(deidentified_id)),
sample_data = spatialTIME::example_summary %>%
dplyr::mutate(deidentified_id = as.character(deidentified_id)),
spatial_list = spatialTIME::example_spatial,
patient_id = "deidentified_id",
sample_id = "deidentified_sample")
mnames_good <- c("CD3..Opal.570..Positive","CD8..Opal.520..Positive",
"FOXP3..Opal.620..Positive","PDL1..Opal.540..Positive",
"PD1..Opal.650..Positive","CD3..CD8.","CD3..FOXP3.")
x2 = NN_G(mif = x, mnames = mnames_good[1:2],
r_range = 0:100, num_permutations = 10,
edge_correction = "rs", keep_perm_dis = FALSE,
workers = 1, overwrite = TRUE)
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