tof_apply_classifier: Perform developmental clustering on CyTOF data using a...

In keyes-timothy/tidytof: Analyze High-dimensional Cytometry Data Using Tidy Data Principles

Perform developmental clustering on CyTOF data using a pre-fit classifier

Description

Perform 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.

Examples

NULL

keyes-timothy/tidytof documentation built on March 31, 2024, 12:01 p.m.