This function is a wrapper around tidytof's tof_cluster_* function family. It performs clustering on high-dimensional cytometry data using a user-specified method (of 5 choices) and each method's corresponding input parameters.
Usage
tof_cluster(
tof_tibble,
cluster_cols = where(tof_is_numeric),
group_cols = NULL,
...,
augment = TRUE,
method
)Arguments
- tof_tibble
A `tof_tbl` or `tibble`.
- cluster_cols
Unquoted column names indicating which columns in `tof_tibble` to use in computing the clusters. Defaults to all numeric columns in `tof_tibble`. Supports tidyselect helpers.
- group_cols
Optional. Unquoted column names indicating which columns should be used to group cells before clustering. Clustering is then performed on each group independently. Supports tidyselect helpers.
- ...
Additional arguments to pass to the `tof_cluster_*` function family member corresponding to the chosen method.
- augment
A boolean value indicating if the output should column-bind the cluster ids of each cell as a new column in `tof_tibble` (TRUE, the default) or if a single-column tibble including only the cluster ids should be returned (FALSE).
- method
A string indicating which clustering methods should be used. Valid values include "flowsom", "phenograph", "kmeans", "ddpr", and "xshift".
Value
A `tof_tbl` or `tibble` If augment = FALSE, it will have a single column encoding the cluster ids for each cell in `tof_tibble`. If augment = TRUE, it will have ncol(tof_tibble) + 1 columns: each of the (unaltered) columns in `tof_tibble` plus an additional column encoding the cluster ids.
See also
Other clustering functions:
tof_cluster_ddpr(),
tof_cluster_flowsom(),
tof_cluster_kmeans(),
tof_cluster_phenograph()
Examples
sim_data <-
dplyr::tibble(
cd45 = rnorm(n = 500),
cd38 = rnorm(n = 500),
cd34 = rnorm(n = 500),
cd19 = rnorm(n = 500)
)
tof_cluster(tof_tibble = sim_data, method = "kmeans")
#> # A tibble: 500 × 5
#> cd45 cd38 cd34 cd19 .kmeans_cluster
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 1.33 -0.447 1.50 0.436 11
#> 2 -1.20 -0.481 -0.391 -1.54 9
#> 3 -0.541 0.666 -1.68 -0.986 16
#> 4 -1.22 1.32 0.689 -0.791 10
#> 5 0.639 0.519 -1.32 -0.204 18
#> 6 -0.239 0.397 -0.780 0.372 1
#> 7 0.651 0.997 -0.665 0.805 18
#> 8 0.788 1.26 0.584 -0.953 19
#> 9 -0.344 0.388 -0.407 -0.442 13
#> 10 0.120 0.885 -2.26 0.583 17
#> # ℹ 490 more rows
tof_cluster(tof_tibble = sim_data, method = "phenograph")
#> # A tibble: 500 × 5
#> cd45 cd38 cd34 cd19 .phenograph_cluster
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 1.33 -0.447 1.50 0.436 2
#> 2 -1.20 -0.481 -0.391 -1.54 1
#> 3 -0.541 0.666 -1.68 -0.986 1
#> 4 -1.22 1.32 0.689 -0.791 3
#> 5 0.639 0.519 -1.32 -0.204 5
#> 6 -0.239 0.397 -0.780 0.372 5
#> 7 0.651 0.997 -0.665 0.805 4
#> 8 0.788 1.26 0.584 -0.953 8
#> 9 -0.344 0.388 -0.407 -0.442 1
#> 10 0.120 0.885 -2.26 0.583 5
#> # ℹ 490 more rows