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Binarise regulon activity into on/off calls

binarise_regulon_activity.Rd

The last SCENIC step. Each regulon gets its own threshold derived from the shape of its AUC distribution across cells, and a cell counts as on when its score sits strictly above that threshold.

The thresholds come back alongside the calls, so you can inspect them, override any that look wrong and re-apply the comparison yourself. SCENIC does the same, it writes them to an editable file between scoring and assignment.

Regulons flagged as not bimodal fell back to mean + 2 * sd. If most of your regulons land there, the AUC distributions are too flat to separate, which usually points at the scoring statistic rather than the thresholding. Check you used auc_type = "recovery", see params_sc_aucell().

Usage

binarise_regulon_activity(
  auc_matrix,
  binarise_params = params_scenic_binarise(),
  .verbose = TRUE
)

Arguments

auc_matrix

Numeric matrix of cells x regulons, or the ScMatrixRes returned by aucell_sc().

binarise_params

List. Output of params_scenic_binarise().

.verbose

Boolean. Controls verbosity of the function.

Value

A list with:

  • binary - Logical matrix of cells x regulons, TRUE where the regulon is on.

  • thresholds - data.table with one row per regulon, holding the threshold, whether it was bimodal and the number of cells called on.

References

Aibar, et al., Nat Methods, 2017

Examples

# on/off calls for a bimodal and a unimodal regulon
set.seed(7L)
auc <- cbind(
  regulon_a = c(rnorm(50, 0.1, 0.02), rnorm(50, 0.4, 0.02)),
  regulon_b = rnorm(100, 0.2, 0.05)
)
rownames(auc) <- sprintf("cell_%i", 1:100)
binarise_regulon_activity(auc, .verbose = FALSE)$thresholds
#>      regulon threshold bimodal n_cells_on
#>       <char>     <num>  <lgcl>      <num>
#> 1: regulon_a 0.2536428    TRUE         50
#> 2: regulon_b 0.3014215   FALSE          2