Generate SuperCells. — rs_supercell • bixverse

This function implements the approach from Bilous, et al. to generate meta cells or called here SuperCells. You can provide an already pre-computed kNN matrix or an embedding to regenerate the kNN matrix with specified parameters in the meta_cell_params. If knn_mat is provided, this one will be used. You need to at least provide knn_mat or embd!

Usage

rs_supercell(
  f_path,
  knn_mat,
  embd,
  cells_to_keep,
  cells_to_use,
  supercell_params,
  target_size,
  seed,
  verbose
)

Arguments

f_path: String. Path to the counts_cells.bin file.
knn_mat: Optional integer matrix. The kNN matrix you wish to use for the generation of the meta cells. This function expects 0-indices!
embd: Optional numerical matrix. The embedding matrix (for example PCA embedding) you wish to use for the generation of the kNN graph that is used subsequently for aggregation of the meta cells.
cells_to_keep: Optional indices of the cells to keep, i.e., the cells used for the generation of the embedding.
cells_to_use: Optional indices of cells to use for meta cell generation. Useful if you wish to generate meta cells in specific cell types. If this is provided, the kNN graph will be regenerated.
supercell_params: A list containing the SuperCell parameters.
target_size: Numeric. Target library size for re-normalisation of the meta cells. Typically 1e4.
seed: Integer. For reproducibility purposes.
verbose: Boolean. Controls verbosity of the function.

Value

A list with the following elements:

assignments - A list containing assignment information with elements: assignments (vector), metacells (list), unassigned (vector), n_metacells, n_cells, n_unassigned
aggregated - A list with indptr, indices, raw_counts, norm_counts, nrow, ncol in sparse format.