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Find the neighbours for single cell.

find_neighbours_sc.Rd

This function will generate the kNNs based on a given embedding. Four different algorithms are implemented with different speed and accuracy to approximate the nearest neighbours. "hnsw" implements a Hierarchical Navigatable Small Worlds vector search that has slower index generation but high precision. "annoy" is based on the Approximate Nearest Neighbours Oh Yeah algorithm and is more rapid in terms of index generation, but querying on large data sets can be slow. "nndescent" is a Rust-based implementation of the PyNNDescent algorithm and is a good all-rounder and performs well on very large data sets. "exhaustive" performs exact nearest neighbour search. Subsequently, the kNN data will be used to generate an sNN igraph for clustering methods.

Usage

find_neighbours_sc(
  object,
  embd_to_use = "pca",
  no_embd_to_use = NULL,
  neighbours_params = params_sc_neighbours(),
  seed = 42L,
  .verbose = TRUE
)

Arguments

object

SingleCells class.

embd_to_use

String. The embedding to use. Whichever you chose, it needs to be part of the object.

no_embd_to_use

Optional integer. Number of embedding dimensions to use. If NULL all will be used.

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.

  • pruning - Numeric. Weights below this threshold will be set to 0 in the generation of the sNN graph.

  • 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().

  • knn - List of kNN parameters. See params_knn_defaults() for available parameters and their defaults.

seed

Integer. For reproducibility.

.verbose

Boolean. Controls verbosity and returns run times.

Value

The object with added KNN matrix.