Wrapper function for parameters for neighbour identification in single cell

Usage

params_sc_neighbours(
  full_snn = TRUE,
  pruning = 0.0833333333333333,
  snn_similarity = c("jaccard", "rank"),
  knn = list()
)

Arguments

full_snn: Boolean. Shall the full shared nearest neighbour graph be generated that generates edges between all cells instead of between only neighbours. Defaults to TRUE.
pruning: Numeric. Weights below this threshold will be set to 0 in the generation of the sNN graph. Seurat uses for example 1/15 with k = 20. As the default k is set to 15, we set it to 1/12. Track this against k rather than leaving it: the threshold is a share of the neighbourhood, so the same value prunes far harder at a larger k. Over-pruning fails quietly, in that you still get a clustering, but cells left with too few shared neighbours drop out as singleton communities, which then show up downstream as one-cell clusters with inflated run_paga_sc() connectivities. Defaults to 0.08333333333333333.
snn_similarity: String. The Jaccard similarity calculates the Jaccard between the neighbours, whereas the rank method calculates edge weights based on the ranking of shared neighbours. For the rank method, the weight is determined by finding the shared neighbour with the lowest combined rank across both cells, where lower-ranked (closer) shared neighbours result in higher edge weights Both methods produce weights normalised to the range [0, 1]. One of c("jaccard", "rank"). Defaults to "jaccard".
knn: List. Optional overrides for kNN parameters. See params_knn_defaults() for available parameters: k, knn_method, ann_dist, search_budget, n_trees, delta, diversify_prob, ef_budget, extract_knn, m, ef_construction, ef_search, n_list and n_probe. See params_knn_defaults() for the available elements. Defaults to list().

Value

A named list with the following elements:

full_snn - Boolean. Shall the full shared nearest neighbour graph be generated that generates edges between all cells instead of between only neighbours. Defaults to TRUE.
pruning - Numeric. Weights below this threshold will be set to 0 in the generation of the sNN graph. Seurat uses for example 1/15 with k = 20. As the default k is set to 15, we set it to 1/12. Track this against k rather than leaving it: the threshold is a share of the neighbourhood, so the same value prunes far harder at a larger k. Over-pruning fails quietly, in that you still get a clustering, but cells left with too few shared neighbours drop out as singleton communities, which then show up downstream as one-cell clusters with inflated run_paga_sc() connectivities. Defaults to 0.08333333333333333.
snn_similarity - String. The Jaccard similarity calculates the Jaccard between the neighbours, whereas the rank method calculates edge weights based on the ranking of shared neighbours. For the rank method, the weight is determined by finding the shared neighbour with the lowest combined rank across both cells, where lower-ranked (closer) shared neighbours result in higher edge weights Both methods produce weights normalised to the range [0, 1]. One of c("jaccard", "rank"). Defaults to "jaccard".
The elements of params_knn_defaults(), overridden by knn, spliced in at this position.