dixons_s {spatialTIME}

Dixon's S Segregation Statistic

Description

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

Usage

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")