Takes a `res` object (as returned by `run_soft_gating()` or `run_tree_gating()`), builds a per-cell-type label matrix using a cutoff function, stores it as `res$label_mat`, and writes a single-label assignment into `colData(res$spe)`.
Usage
apply_cutoff_labels(
res,
cutoff_fn = prob_quantile_cutoff,
label_col = "cutoff_label",
unknown_label = "Unknown"
)Arguments
- res
List containing at least `spe` and `prob_mat`.
- cutoff_fn
Function passed to [probability_label_matrix()] to compute per-column cutoffs (default [prob_quantile_cutoff()]).
- label_col
Name of the column to add to `colData(res$spe)` (default "cutoff_label").
- unknown_label
Label used when zero or multiple cell types pass their cutoff (default "Unknown").
Details
A cell receives a label only if exactly one cell type passes its cutoff; if multiple (or zero) cell types pass, the cell is labeled `unknown_label`.
Examples
data("cytoGateR_example", package = "cytoGateR")
image_ids <- SummarizedExperiment::colData(cytoGateR_example)$image_name
spe <- cytoGateR_example[, image_ids == image_ids[1L]]
prob_cols <- grep(
"^KNN_P_",
names(SummarizedExperiment::colData(spe)),
value = TRUE
)
prob_mat <- as.matrix(SummarizedExperiment::colData(spe)[, prob_cols])
rownames(prob_mat) <- colnames(spe)
colnames(prob_mat) <- sub("^KNN_P_", "", colnames(prob_mat))
result <- apply_cutoff_labels(
list(spe = spe, prob_mat = prob_mat),
cutoff_fn = function(x) 0.5
)
table(SummarizedExperiment::colData(result$spe)$cutoff_label)
#>
#> Bcell BnTcell CD4 CD8 Myeloid Neutrophil
#> 554 1310 775 540 306 41
#> Plasma_cell Stroma Treg Tumor Unknown
#> 60 400 175 85 117