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Generate an miloR abundance object for differential abundance testing

get_miloR_abundances_sc.Rd

This function implements the miloR differential abundance testing on top of the kNN graph. The general idea of the approach is to use the kNN graph generated from the single cell data, generate representative neighbourhoods and calculate differential abundances within these neighbourhoods. For further details on the method, please refer to Dann, et al. This function will take a SingleCells class, run the neighbourhood detection, count the occurrences of a sample and return a sc_miloR class for subsequent differential abundance testing and further annotations.

Usage

get_miloR_abundances_sc(
  object,
  sample_id_col,
  embd_to_use = "pca",
  no_embd_to_use = NULL,
  miloR_params = params_sc_miloR(),
  seed = 42L,
  .verbose = TRUE
)

Arguments

object

SingleCells class.

sample_id_col

Character. The column in the obs table representing the sample identifier to count.

embd_to_use

Character. The embedding to use for the refinement procedure. Please use the same here as you used to generate the neighbours! Defaults to "pca".

no_embd_to_use

Optional integer. If you only want to use a subset of the embedding.

miloR_params

A list, please see params_sc_miloR(). The list has the following parameters:

  • prop - Numeric. Proportion of cells to sample as neighbourhood indices. Must be in (0,1).

  • k_refine - Integer. Number of neighbours to use for refinement.

  • refinement_strategy - String. Strategy for refining sampled indices. One of c("approximate", "bruteforce", "index").

  • index_type - String. Type of kNN index to use. One of c("annoy", "hnsw").

  • knn - List of kNN parameters. See params_knn_defaults() for available parameters and their defaults. Note: knn_method cannot be "exhaustive" for MiloR as it basically boils down to "bruteforce".

seed

Integer. Seed for reproducibility

.verbose

Boolean. Controls verbosity of the method.

References

Dann, et al., Nat Biotechnol, 2022