Generate a new SingleCellNearestNeighbour from data

Usage

generate_sc_knn(
  data,
  neighbours_params = params_sc_neighbours(),
  seed = 42L,
  .validate_index = FALSE,
  .verbose = TRUE
)

Arguments

data

Numerical matrix. Samplex x features. The embedding matrix from which to generate the kNN data.

neighbours_params

List. Output of params_sc_neighbours(). A list with the following items:

full_snn - Boolean. Shall the full shared nearest neighbour graph be generated that generates edges between all cells instead of between only neighbours. (Not used in this function.)
pruning - Numeric. Weights below this threshold will be set to 0 in the generation of the sNN graph. (Not used in this function.)
snn_similarity - String. One of c("rank", "jaccard"). Defines how the weight from the SNN graph is calculated. For details, please see params_sc_neighbours(). (Not used in this function.)
knn - List of kNN parameters. See params_knn_defaults() for available parameters and their defaults.

seed

Integer. Random seed for reproducibility.

.validate_index

Boolean. Shall an exhaustive search against a subset of cells be run to validate the approximate nearest neighbour index.

.verbose

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

Value

The SingleCellNearestNeighbour for downstream usage.

Examples

# kNN over a random embedding; the rows have to carry cell names
set.seed(42L)
embd <- matrix(rnorm(500 * 10), nrow = 500)
rownames(embd) <- sprintf("cell_%03d", 1:500)
knn <- generate_sc_knn(embd, .verbose = FALSE)
dim(get_knn_mat(knn))
#> [1] 500  15