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#' Internal function to generate table for otsu misclassification analysis
#'
#' @inheritParams run_otsu_discordance
#' @param threshold thresolding function as defined using `run_otsu_discordance()`
#'
#' @importFrom magrittr %>%
#'
#' @return `mx_dataset` object with `otsu_data` attribute added and misclassification results
#' @noRd
otsu_mx_dataset <- function(mx_data,
table,
threshold){
## check if threshold is in skf to use np_array
use_np_array = FALSE
if(any(grepl("python",class(threshold)))){ use_np_array = TRUE }
## setup variables for other functions
cols = mx_data$marker_cols
slide = mx_data$slide_id
## generate slide level otsus
if(table == "both"){
## run for raw, table = raw
o1 = get_otsu_tab(tdat=mx_data$data,
cols,
slide,
table="raw",
threshold,
use_np_array) %>%
data.table::rbindlist()
## run for normalized, table = normalized
o2 = get_otsu_tab(tdat=mx_data$norm_data,
cols,
slide,
table="normalized",
threshold,
use_np_array) %>%
data.table::rbindlist()
otsu_data = rbind(o1,o2)
}
if(table == "raw"){
## run for raw, table = raw
otsu_data = get_otsu_tab(tdat=mx_data$data,
cols,
slide,
table,
threshold,
use_np_array) %>%
data.table::rbindlist()
}
if(table == "normalized"){
## run for normalized, table = normalized
otsu_data = get_otsu_tab(tdat=mx_data$norm_data,
cols,
slide,
table,
threshold,
use_np_array) %>%
data.table::rbindlist()
}
mx_data$otsu_data = as.data.frame(otsu_data)
mx_data
}
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