tof_cluster_kmeans: Perform k-means clustering on high-dimensional cytometry...

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

Perform k-means clustering on high-dimensional cytometry data.

Description

This function performs k-means clustering on high-dimensional cytometry data using a user-specified selection of input variables/high-dimensional cytometry measurements. It is mostly a convenient wrapper around kmeans.

Usage

tof_cluster_kmeans(
  tof_tibble,
  cluster_cols = where(tof_is_numeric),
  num_clusters = 20,
  ...
)

Arguments

tof_tibble	A 'tof_tibble'.
cluster_cols	Unquoted column names indicating which columns in 'tof_tibble' to use in computing the k-means clusters. Defaults to all numeric columns in 'tof_tibble'. Supports tidyselect helpers.
num_clusters	An integer indicating the maximum number of clusters that should be returned. Defaults to 20.
...	Optional additional arguments that can be passed to kmeans.

Value

A tibble with one column named '.kmeans_cluster'. This column will contain an integer vector of length 'nrow(tof_tibble)' indicating the id of the k-means cluster to which each cell (i.e. each row) in 'tof_tibble' was assigned.

See Also

Other clustering functions: tof_cluster(), tof_cluster_ddpr(), tof_cluster_flowsom(), tof_cluster_phenograph()

Examples

sim_data <-
    dplyr::tibble(
        cd45 = rnorm(n = 1000),
        cd38 = rnorm(n = 1000),
        cd34 = rnorm(n = 1000),
        cd19 = rnorm(n = 1000)
    )
tof_cluster_kmeans(tof_tibble = sim_data)
tof_cluster_kmeans(tof_tibble = sim_data, cluster_cols = c(cd45, cd19))

keyes-timothy/tidytof documentation built on May 7, 2024, 12:33 p.m.