This function is a wrapper around tidytof's tof_metacluster_* function family. It performs metaclustering on CyTOF data using a user-specified method (of 5 choices) and each method's corresponding input parameters.
Arguments
- tof_tibble
A `tof_tbl` or `tibble`.
- cluster_col
An unquoted column name indicating which column in `tof_tibble` stores the cluster ids for the cluster to which each cell belongs. Cluster labels can be produced via any method the user chooses - including manual gating, any of the functions in the `tof_cluster_*` function family, or any other method.
- metacluster_cols
Unquoted column names indicating which columns in `tof_tibble` to use in computing the metaclusters. Defaults to all numeric columns in `tof_tibble`. Supports tidyselect helpers.
- central_tendency_function
The function that should be used to calculate the measurement of central tendency for each cluster before metaclustering. This function will be used to compute a summary statistic for each input cluster in `cluster_col` across all columns specified by `metacluster_cols`, and the resulting vector (one for each cluster) will be used as the input for metaclustering. Defaults to
median.- ...
Additional arguments to pass to the `tof_metacluster_*` function family member corresponding to the chosen `method`.
- augment
A boolean value indicating if the output should column-bind the metacluster ids of each cell as a new column in `tof_tibble` (TRUE; the default) or if a single-column tibble including only the metacluster ids should be returned (FALSE).
- method
A string indicating which clustering method should be used. Valid values include "consensus", "hierarchical", "kmeans", "phenograph", and "flowsom".
Value
A `tof_tbl` or `tibble` If augment = FALSE, it will have a single column encoding the metacluster 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 metacluster ids.
See also
Other metaclustering functions:
tof_metacluster_consensus(),
tof_metacluster_flowsom(),
tof_metacluster_hierarchical(),
tof_metacluster_kmeans(),
tof_metacluster_phenograph()
Examples
sim_data <-
dplyr::tibble(
cd45 = rnorm(n = 1000),
cd38 = rnorm(n = 1000),
cd34 = rnorm(n = 1000),
cd19 = rnorm(n = 1000),
cluster_id = sample(letters, size = 1000, replace = TRUE)
)
tof_metacluster(
tof_tibble = sim_data,
cluster_col = cluster_id,
clustering_algorithm = "consensus",
method = "flowsom"
)
#> # A tibble: 1,000 × 6
#> cd45 cd38 cd34 cd19 cluster_id .flowsom_metacluster
#> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 -0.350 -1.50 2.82 -0.573 g 2
#> 2 1.64 -0.422 0.543 2.31 i 3
#> 3 1.06 -0.0543 0.877 -0.444 l 3
#> 4 0.644 -1.12 -1.12 -0.604 k 4
#> 5 1.34 0.606 -0.631 0.0561 p 2
#> 6 -1.20 0.484 -0.166 0.0976 l 3
#> 7 0.781 -0.699 0.619 -0.693 x 2
#> 8 -0.230 -0.0746 0.793 -0.805 f 4
#> 9 -0.504 1.05 -1.39 1.01 h 2
#> 10 0.511 0.492 -2.11 0.543 x 2
#> # ℹ 990 more rows
tof_metacluster(
tof_tibble = sim_data,
cluster_col = cluster_id,
method = "phenograph"
)
#> # A tibble: 1,000 × 6
#> cd45 cd38 cd34 cd19 cluster_id .phenograph_metacluster
#> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 -0.350 -1.50 2.82 -0.573 g 2
#> 2 1.64 -0.422 0.543 2.31 i 1
#> 3 1.06 -0.0543 0.877 -0.444 l 1
#> 4 0.644 -1.12 -1.12 -0.604 k 3
#> 5 1.34 0.606 -0.631 0.0561 p 2
#> 6 -1.20 0.484 -0.166 0.0976 l 1
#> 7 0.781 -0.699 0.619 -0.693 x 4
#> 8 -0.230 -0.0746 0.793 -0.805 f 3
#> 9 -0.504 1.05 -1.39 1.01 h 2
#> 10 0.511 0.492 -2.11 0.543 x 4
#> # ℹ 990 more rows