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Assign cell types per cell based on ScType

assign_sc_type.Rd

The cluster-level ScType path (score_clusters()) stamps one label on every cell of a cluster, so a minority population sharing a Leiden community with a bigger one gets absorbed without anything flagging it. This runs the scoring per cell instead: the score matrix is smoothed over the sNN graph via label spreading (Zhou et al.), then each cell takes its own argmax. Cells whose best score falls below score_floor come back as NA.

Pass cluster_col to also get the per-cluster composition (purity, entropy, runner-up cell type) and the hybrid assignment, where clusters at or above purity_threshold keep the blanket cluster-level call and mixed clusters fall back to the per-cell calls.

Usage

assign_sc_type(
  object,
  sc_type_res,
  cluster_col = NULL,
  sctype_cell_params = params_sctype_cells(),
  .verbose = TRUE
)

Arguments

object

SingleCells.

sc_type_res

ScTypeResults, see calc_sc_type_scores().

cluster_col

Optional string. Name of the obs column with the cluster assignment. If provided, the composition and hybrid assignment are returned on top of the per-cell calls.

sctype_cell_params

List. Output of params_sctype_cells().

.verbose

Boolean or integer. Controls verbosity.

Value

An ScTypeCellResults results class.

References

Ianevski et al., Nat Comm, 2022. Zhou et al., NIPS, 2004.

Examples

# per cell calls from the ScType scores
sc <- demo_single_cells()
markers <- data.table::data.table(
  cell_type = rep(sprintf("cell_type_%i", 1:3), each = 10),
  gene_id = sprintf("gene_%02d", 1:30)
)
cell_markers <- prepare_cell_markers(obj = sc, marker_df = markers)
scores <- calc_sc_type_scores(
  sc,
  cell_marker_list = cell_markers,
  .verbose = FALSE
)
res <- assign_sc_type(sc, sc_type_res = scores, .verbose = FALSE)
table(res$assignments)
#> 
#> cell_type_1 cell_type_2 cell_type_3 
#>         167         166         167 

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