Perform developmental clustering on CyTOF data using a pre-fit classifier
Source:R/developmental_classifier_helpers.R
tof_apply_classifier.RdPerform developmental clustering on CyTOF data using a pre-fit classifier
Usage
tof_apply_classifier(
cancer_tibble = NULL,
classifier_fit = NULL,
distance_function = c("mahalanobis", "cosine", "pearson"),
num_cores = 1,
parallel_vars
)Arguments
- cancer_tibble
A `tibble` or `tof_tibble` containing cells to be classified into their nearest healthy subpopulation (generally cancer cells).
- classifier_fit
A nested `tibble` produced by `tof_build_classifier` in which each row represents a healthy cell subpopulation into which the cells in `cancer_tibble` should be classified using minimum distance.
- distance_function
A string indicating which distance function should be used to perform the classification. Options are "mahalanobis" (the default), "cosine", and "pearson".
- num_cores
An integer indicating the number of CPU cores used to parallelize the classification. Defaults to 1 (a single core).
- parallel_vars
Unquoted column names indicating which columns in `cancer_tibble` to use for breaking up the data in order to parallelize the classification. Defaults to NULL. Supports tidyselect helpers.
Value
A tibble with `nrow(cancer_tibble)` rows and `nrow(classifier_fit) + 1` columns. Each row represents a cell from `cancer_tibble`, and `nrow(classifier_fit)` of the columns represent the distance between the cell and each of the healthy subpopulations' cluster centroids. The final column represents the cluster id of the healthy subpopulation with the minimum distance to the cell represented by that row.