Dixon's S Segregation Statistic — dixons

This function processes the spatial files in the mif object, requiring a column that distinguishes between different groups i.e. tumor and stroma

dixons_s(
  mif,
  mnames,
  num_permutations = 1000,
  type = c("Z", "C"),
  workers = 1,
  overwrite = FALSE,
  xloc = NULL,
  yloc = NULL
)

Arguments

mif: An MIF object
mnames: vector of markers corresponding to spatial columns to check Dixon's S between
num_permutations: Numeric value indicating the number of permutations used. Default is 1000.
type: a character string for the type that is wanted in the output which can be "Z" for z-statistic results or "C" for Chi-squared statistic results
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 for Z-statistic

From
To
Obs.Count
Exp. Count
S
Z
p-val.Z
p-val.Nobs
Marker
Classifier Labeled Column Counts
Image.Tag

Returns a data frame for C-statistic

Segregation
df
Chi-sq
P.asymp
P.rand
Marker
Classifier Labeled Column Counts
Image.Tag

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")