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Calculate recall at k and distance ratio

calc_knn_metrics.Rd

Helper function to compare the results of two SingleCellNearestNeighbour against each other. The first one can serve as a reference (ground truth) and you can compare against the second one.

Usage

calc_knn_metrics(ref_knn, query_knn)

Arguments

ref_knn

The reference SingleCellNearestNeighbour.

query_knn

The query SingleCellNearestNeighbour.

Value

A list with:

  • matches - The intersecting indices between the reference and query kNN for each sample. In an ideal match up should be equal to k.

  • distance_ratio - The distance ratio. Calculates sum(dist_query) / sum(dist_ref) per sample. Indicates how much worse the reference is.

  • final_recall - The final recall across all samples.

  • final_ratio - The final distance ratio across all samples.

Examples

# recall of an annoy index against the default one
set.seed(42L)
embd <- matrix(rnorm(500 * 10), nrow = 500)
rownames(embd) <- sprintf("cell_%03d", 1:500)
ref <- generate_sc_knn(embd, .verbose = FALSE)
query <- generate_sc_knn(
  embd,
  neighbours_params = params_sc_neighbours(
    knn = list(knn_method = "annoy")
  ),
  .verbose = FALSE
)
calc_knn_metrics(ref, query)$final_recall
#> [1] 1