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Run BBKNN

bbknn_sc.Rd

This function implements the batch-balanced k-nearest neighbour algorithm from Polański, et al. Briefly, the algorithm generates a KNN index on a per batch basis and identifies the neighbours of cells for each individual index. Subsequently, it leverages UMAP connectivity calculations to reduce spurious connections. For more details, please refer to Polański, et al.

Usage

bbknn_sc(
  object,
  batch_column,
  no_neighbours_to_keep = 5L,
  embd_to_use = "pca",
  no_embd_to_use = NULL,
  bbknn_params = params_sc_bbknn(),
  seed = 42L,
  .verbose = TRUE
)

Arguments

object

SingleCells class.

batch_column

String. The column with the batch information in the obs data of the class.

no_neighbours_to_keep

Integer. Maximum number of neighbours to keep from the BBKNN algorithm. Due to generating neighbours for each batch, there might be a large number of generated neighbours. This will only keep the top no_neighbours_to_keep neighbours. Defaults to 5L.

embd_to_use

String. The embedding to use. Atm, the only option is "pca".

no_embd_to_use

Optional integer. Number of embedding dimensions to use. If NULL all will be used.

bbknn_params

A list, please see params_sc_bbknn(). The list has the following parameters:

  • neighbours_within_batch - Integer. Number of neighbours to consider per batch.

  • set_op_mix_ratio - Numeric. Mixing ratio between union (1.0) and intersection (0.0).

  • local_connectivity - Numeric. UMAP connectivity computation parameter, how many nearest neighbours of each cell are assumed to be fully connected.

  • trim - Optional integer. Trim the neighbours of each cell to these many top connectivities. May help with population independence and improve the tidiness of clustering. If NULL, it defaults to 10 * neighbours_within_batch.

  • knn - List of kNN parameters. See params_knn_defaults() for available parameters and their defaults.

seed

Integer. Random seed.

.verbose

Boolean. Controls the verbosity of the function.

Value

The object with added kNN matrix based on BBKNN and the graph based on the returned connectivities of the algorithm.

References

Polański, et al., Bioinformatics, 2020