Calculate diffusion coordinates

To leverage the quality metrics from Persad, et al., we need the diffusion coordinates to then calculate if a cell is a dense or sparse region of the manifold, its compactness and separation to other meta cells. To do so, generate a diffusion map on the original data based on the approach of SEACells and add the data to the object, see Persad, et al.

Usage

calc_diffusion_coordinates(
  object,
  knn_data,
  n_dcs = 10L,
  k_density = 150L,
  seed = 42L,
  .verbose = TRUE
)

Arguments

object

MetaCells class.

knn_data

SingleCellNearestNeighbour class. Contains the kNN graph from the original cells.

n_dcs

Integer. Number of diffusion coordinates to use. Defaults to 10L.

k_density

Integer. The k-th neighbour to use for the density region estimation. Defaults to 150L.

seed

Integer. Seed for reproducibility

.verbose

Boolean or integer. Controls verbosity and returns run times. FALSE -> quiet, TRUE or 1L -> normal verbosity, 2L -> detailed verbosity.

Value

The class with the diffusion map coordinates, density distance and region attached.

References

Persad, et al. Nat Biotechnol, 2023

Examples

# diffusion map off the source kNN graph, giving each meta cell a region
sc <- demo_single_cells()
mc <- generate_bt_meta_cells_sc(
  sc,
  sc_meta_cell_params = params_sc_bt_metacells(target_no_metacells = 50L),
  .verbose = FALSE
)
mc <- calc_diffusion_coordinates(
  mc,
  knn_data = get_knn_obj(sc),
  .verbose = FALSE
)
table(mc[["density_region"]]$density_region)
#> 
#> high  low  mid 
#>   12   11   27 

unlink(sc@dir_data, recursive = TRUE, force = TRUE)