Wrapper function for Boost parameters

Usage

params_boost(
  boost_rate = 0.25,
  replace = FALSE,
  resolution = 1,
  n_iters = 25L,
  p_thresh = 1e-07,
  voter_thresh = 0.9,
  normalisation = list(),
  hvg = list(),
  pca = list(),
  knn = list(k = 0L, ann_dist = "euclidean")
)

Arguments

boost_rate: Numeric. Boosting rate for the algorithm. Must be between 0 and 1. Defaults to 0.25.
replace: Boolean. Whether to use replacement during boosting. Defaults to FALSE.
resolution: Numeric. Resolution parameter for graph-based clustering. Higher values lead to more clusters. Defaults to 1.0.
n_iters: Integer. Number of iterations to run the algorithm. Defaults to 25L.
p_thresh: Numeric. P-value threshold for significance testing. Defaults to 1e-7.
voter_thresh: Numeric. Voter threshold across iterations. Proportion of iterations a cell must be assigned to a cluster to be considered a member. Must be between 0 and 1. Defaults to 0.9.
normalisation: List. Optional overrides for normalisation parameters. See params_norm_doublet_detection_defaults() for available parameters: log_transform, mean_center, normalise_variance, target_size. Note: Boost uses different defaults (log_transform = FALSE, mean_center = TRUE, normalise_variance = TRUE, target_size = NULL).
hvg: List. Optional overrides for highly variable gene selection parameters. See params_hvg_defaults() for available parameters: min_gene_var_pctl, hvg_method, loess_span, clip_max.
pca: List. Optional overrides for PCA parameters. See params_pca_defaults() for available parameters: no_pcs, random_svd.
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, m, ef_construction, ef_search, n_list and n_probe. Note: this function defaults to k = 0L (automatic neighbour detection).

Value

A named list with all Boost parameters, combining defaults with any user-specified overrides.

A list with the Boost parameters.