tof_cluster_flowsom | R Documentation |
This function performs FlowSOM 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 SOM
and MetaClustering
.
tof_cluster_flowsom(
tof_tibble = NULL,
cluster_cols = where(tof_is_numeric),
som_xdim = 10,
som_ydim = 10,
som_distance_function = c("euclidean", "manhattan", "chebyshev", "cosine"),
perform_metaclustering = TRUE,
num_metaclusters = 20,
...
)
tof_tibble |
A 'tof_tbl' or 'tibble'. |
cluster_cols |
Unquoted column names indicating which columns in 'tof_tibble' to use in computing the flowSOM clusters. Defaults to all numeric columns in 'tof_tibble'. Supports tidyselect helpers. |
som_xdim |
The width of the grid used by the self-organizing map. The total number of clusters returned by FlowSOM will be som_xdim * som_ydim, so adjust this value to affect the final number of clusters. Defaults to 10. |
som_ydim |
The height of the grid used by the self-organizing map. The total number of clusters returned by FlowSOM will be som_xdim * som_ydim, so adjust this value to affect the final number of clusters. Defaults to 10. |
som_distance_function |
The distance function used during self-organizing map calculations. Options are "euclidean" (the default), "manhattan", "chebyshev", and "cosine". |
perform_metaclustering |
A boolean value indicating if metaclustering should be performed on the initial clustering result returned by FlowSOM. Defaults to TRUE. |
num_metaclusters |
An integer indicating the maximum number of metaclusters that should be returned after metaclustering. Defaults to 20. |
... |
Optional additional parameters that can be passed to the |
For additional details about the FlowSOM algorithm, see this paper.
A tibble with one column named '.flowsom_cluster' or '.flowsom_metacluster' depending on the value of 'perform_metaclustering'. The column will contain an integer vector of length 'nrow(tof_tibble)' indicating the id of the flowSOM cluster to which each cell (i.e. each row) in 'tof_tibble' was assigned.
Other clustering functions:
tof_cluster()
,
tof_cluster_ddpr()
,
tof_cluster_kmeans()
,
tof_cluster_phenograph()
sim_data <-
dplyr::tibble(
cd45 = rnorm(n = 200),
cd38 = rnorm(n = 200),
cd34 = rnorm(n = 200),
cd19 = rnorm(n = 200)
)
tof_cluster_flowsom(tof_tibble = sim_data, cluster_cols = c(cd45, cd19))
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