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Calculate AUC scores (akin to AUCell)

aucell_sc.Rd

Calculates an AUC-type score akin to AUCell across the gene sets, see Aibar et al. Three statistics are on offer, all consuming the same within-cell ranking but weighting it differently. "recovery" (default) is the recovery-curve AUC under a rank cutoff, i.e. the AUCell statistic of Aibar, et al., and is top-heavy: only genes inside the top max_rank of the cell contribute. "wilcox" is the AUC derived from the Mann-Whitney U statistic over the full ranking; its null sits at 0.5 for any gene set size, which makes it a good fit for pathway activity. "ap" is average precision, the most top-heavy of the three, but its null tracks the gene set prevalence, so raw values are not comparable across gene sets of different size unless standardise is on.

Usage

aucell_sc(
  object,
  gs_list,
  aucell_params = params_sc_aucell(),
  streaming = NULL,
  .verbose = TRUE
)

Arguments

object

SingleCells, MetaCells (or potentially other) class.

gs_list

Named list. The elements have the gene identifiers of the respective gene sets.

aucell_params

List with the AUCell parameters, see params_sc_aucell() with the following elements:

  • auc_type - String. Which statistic to calculate. One of c("recovery", "wilcox", "ap"). "recovery" is the SCENIC one.

  • max_rank - Optional numeric. Rank cutoff for "recovery". If NULL, the top 5% of the gene universe is used. Ignored by the other statistics.

  • standardise - Boolean. Shall each gene set's scores be z-scored across the cells.

streaming

Optional Boolean. Shall the data be streamed in. Useful for larger data sets where you wish to avoid loading in the whole data. If NULL, will automatically detect. Ignored when applied to MetaCells.

.verbose

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

Value

AUCell results in form of a matrix that is cells x gene sets or as ScMatrixRes pending the input.

References

Aibar, et al., Nat Methods, 2017

Examples

# recovery curve AUC for two marker programmes
sc <- demo_single_cells()
gs_list <- list(
  type_1 = get_gene_names(sc)[1:10],
  type_2 = get_gene_names(sc)[11:20]
)
res <- aucell_sc(sc, gs_list = gs_list, .verbose = FALSE)
dim(res)
#> [1] 500   2

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