dixons_s: Dixon's S Segregation Statistic

In spatialTIME: Spatial Analysis of Vectra Immunoflourescent Data

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")

spatialTIME documentation built on June 22, 2024, 10:35 a.m.