Metacluster clustered CyTOF data using consensus clustering

This function performs consensus metaclustering on a `tof_tbl` containing CyTOF data using a user-specified selection of input variables/CyTOF measurements and the number of desired metaclusters. See ConsensusClusterPlus for additional details.

Usage

tof_metacluster_consensus(
  tof_tibble,
  cluster_col,
  metacluster_cols = where(tof_is_numeric),
  central_tendency_function = stats::median,
  num_metaclusters = 10L,
  proportion_clusters = 0.9,
  proportion_features = 1,
  num_reps = 20L,
  clustering_algorithm = c("hierarchical", "pam", "kmeans"),
  distance_function = c("euclidean", "minkowski", "pearson", "spearman", "maximum",
    "binary", "canberra"),
  ...
)

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.
num_metaclusters: An integer indicating the number of clusters that should be returned. Defaults to 10.
proportion_clusters: A numeric value between 0 and 1 indicating the proportion of clusters to subsample (from the total number of clusters in `cluster_col`) during each iteration of the consensus clustering. Defaults to 0.9
proportion_features: A numeric value between 0 and 1 indicating the proportion of features (i.e. the proportion of columns specified by `metacluster_cols`) to subsample during each iteration of the consensus clustering. Defaults to 1 (all features are included).
num_reps: An integer indicating how many subsampled replicates to run during consensus clustering. Defaults to 20.
clustering_algorithm: A string indicating which clustering algorithm ConsensusClusterPlus should use to metacluster the subsampled clusters during each resampling. Options are "hierarchical" (the default), "pam" (partitioning around medoids), and "kmeans".
distance_function: A string indicating which distance function should be used to compute the distances between clusters during consensus clustering. Options are "euclidean" (the default), "manhattan", "minkowski", "pearson", "spearman", "maximum", "binary", and "canberra". See ConsensusClusterPlus.
...: Optional additional arguments to pass to ConsensusClusterPlus.

Value

A tibble with a single column (`.consensus_metacluster`) and the same number of rows as the input `tof_tibble`. Each entry in the column indicates the metacluster label assigned to the same row in `tof_tibble`.

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_consensus(tof_tibble = sim_data, cluster_col = cluster_id)
#> # A tibble: 1,000 × 1
#>    .consensus_metacluster
#>    <chr>                 
#>  1 5                     
#>  2 5                     
#>  3 1                     
#>  4 7                     
#>  5 4                     
#>  6 7                     
#>  7 5                     
#>  8 4                     
#>  9 2                     
#> 10 5                     
#> # ℹ 990 more rows

Usage

Arguments

Value

See also

Examples