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Run BBKNN on the GPU

bbknn_gpu_sc.Rd

GPU counterpart of bixverse::bbknn_sc(), implementing the batch-balanced k-nearest neighbour algorithm from Polański, et al. One nearest neighbour index is built per batch and queried by every cell, so each cell gets neighbours_within_batch neighbours from every batch. The UMAP connectivity calculations that reduce spurious connections then run on the CPU, shared with the CPU implementation.

Usage

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

Arguments

object

SingleCells or SingleCellsSubset class from bixverse.

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. Generating neighbours per batch can produce a lot of them, so this keeps the top no_neighbours_to_keep. 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

List. Output of params_sc_bbknn_gpu().

seed

Integer. Random seed.

.verbose

Boolean or integer. Controls verbosity and returns run times. FALSE -> quiet, TRUE or 1L -> normal verbosity, 2L -> detailed verbosity.

Value

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

Details

Only the per-batch searches move to the device, and they are the part that scales with batch count: BBKNN builds one index per batch and queries each with all cells, so the work grows as n_cells * n_batches. With a handful of batches on a small object the CPU is fine. The GPU starts to matter once you have many samples.

Results match the CPU path exactly with knn_method = "exhaustive", since both are exact and recompute distances against the same embedding. The approximate backends break ties differently and will not.

References

Polański, et al., Bioinformatics, 2020