Parameters for single cell kNN searches

Usage

params_sc_knn(
  k = 15L,
  knn_method = c("kmknn", "hnsw", "annoy", "nndescent", "ivf", "exhaustive"),
  ann_dist = c("euclidean", "cosine"),
  n_trees = 50L,
  search_budget = NULL,
  delta = 0.001,
  diversify_prob = 0,
  ef_budget = NULL,
  extract_knn = FALSE,
  m = 16L,
  ef_construction = 200L,
  ef_search = 100L,
  n_list = NULL,
  n_probe = NULL
)

Arguments

k: Integer. Number of neighbours. Defaults to 15L.
knn_method: String. Which method to use for the approximate nearest neighbour search. One of c("kmknn", "hnsw", "annoy", "nndescent", "ivf", "exhaustive"). Defaults to "kmknn".
ann_dist: String. Distance metric to use. One of c("euclidean", "cosine"). Defaults to "euclidean".
n_trees: Integer. Annoy param: number of trees. Defaults to 50L.
search_budget: Integer or NULL. Annoy param: optional search budget per tree. If NULL, defaults to n_trees * k * 20L internally. Defaults to NULL.
delta: Numeric. NNDescent param: early termination criterion. Defaults to 0.001.
diversify_prob: Numeric. NNDescent param: diversification probability applied at the end of index construction. Defaults to 0.0.
ef_budget: Integer or NULL. NNDescent param: optional query budget. Higher values improve recall at the cost of speed. Defaults to NULL.
extract_knn: Boolean. NNDescent param: hand back the graph the descent already built instead of beam searching it. Skips the query pass entirely, so it is much faster, at the cost of some recall. Rows the descent never filled come back padded with duplicate edges. Ignored by every other method. Defaults to FALSE.
m: Integer. HNSW param: number of connections between layers. Defaults to 16L.
ef_construction: Integer. HNSW param: size of the dynamic candidate list during construction. Defaults to 200L.
ef_search: Integer. HNSW param: size of the candidate list at query time. Higher values improve recall at the cost of speed. Defaults to 100L.
n_list: Integer or NULL. IVF param: number of clusters to generate. If NULL, defaults to sqrt(n) internally. Defaults to NULL.
n_probe: Integer or NULL. IVF param: number of clusters to query. If NULL, defaults to sqrt(n_list) internally. Defaults to NULL.

Value

A named list with the following elements:

k - Integer. Number of neighbours. Defaults to 15L.
knn_method - String. Which method to use for the approximate nearest neighbour search. One of c("kmknn", "hnsw", "annoy", "nndescent", "ivf", "exhaustive"). Defaults to "kmknn".
ann_dist - String. Distance metric to use. One of c("euclidean", "cosine"). Defaults to "euclidean".
n_trees - Integer. Annoy param: number of trees. Defaults to 50L.
search_budget - Integer or NULL. Annoy param: optional search budget per tree. If NULL, defaults to n_trees * k * 20L internally. Defaults to NULL.
delta - Numeric. NNDescent param: early termination criterion. Defaults to 0.001.
diversify_prob - Numeric. NNDescent param: diversification probability applied at the end of index construction. Defaults to 0.0.
ef_budget - Integer or NULL. NNDescent param: optional query budget. Higher values improve recall at the cost of speed. Defaults to NULL.
extract_knn - Boolean. NNDescent param: hand back the graph the descent already built instead of beam searching it. Skips the query pass entirely, so it is much faster, at the cost of some recall. Rows the descent never filled come back padded with duplicate edges. Ignored by every other method. Defaults to FALSE.
m - Integer. HNSW param: number of connections between layers. Defaults to 16L.
ef_construction - Integer. HNSW param: size of the dynamic candidate list during construction. Defaults to 200L.
ef_search - Integer. HNSW param: size of the candidate list at query time. Higher values improve recall at the cost of speed. Defaults to 100L.
n_list - Integer or NULL. IVF param: number of clusters to generate. If NULL, defaults to sqrt(n) internally. Defaults to NULL.
n_probe - Integer or NULL. IVF param: number of clusters to query. If NULL, defaults to sqrt(n_list) internally. Defaults to NULL.