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
An object of class mif. Depending on type, one or both of these
tables is written to the derived slot.
Dixon_Z (type = "Z"), one row per ordered marker pair per sample:
- <sample_id>
sample identifier
- From, To
the two cell types being compared, as
dixon::dixon()labels them- Anchor, Counted
which requested marker pair the row belongs to. The per-marker count columns are pair-specific, because dual positives are dropped per pair, so without this the counts cannot be interpreted
- Obs.Count, Exp.Count, S, Z, p-val.Z, p-val.Nobs
Dixon's statistics
columns cells of each marker retained for that pair
- Simulations, Run
permutations requested, and which call produced the row
Dixon_C (type = "C"), three rows per marker pair per sample:
- <sample_id>
sample identifier
- Direction
"Overall segregation", or "From
" - Anchor, Counted
as above
- df, Chi-sq, P.asymp, P.rand
chi-squared segregation test
- Simulations, Run
as above
Marker pairs with fewer than 3 cells of either type are returned with NA
statistics rather than dropped, so the table shape does not depend on cell
counts.
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")