selFeatures

Selects features based on cluster-wise expression or marker detection, or a combination.

Usage

selFeatures(
  sce,
  clusters = NULL,
  nfeatures = 1000,
  propMarkers = 0,
  auc.min = 0.75,
  FDR.max = NULL
)

Arguments

sce

A SummarizedExperiment-class, SingleCellExperiment-class with a 'counts' assay.

clusters

Optional cluster assignments. Should either be a vector of labels for each cell.

nfeatures

The number of features to select.

propMarkers

The proportion of features to select from markers (rather than on the basis of high expression). Ignored if `clusters` isn't given.

auc.min

Minimum AUC to consider for marters (this will be the minimum of the mean of AUC min, max, median and mean).

FDR.max

Deprecated. Use 'auc.min' instead.

Value

A vector of feature (i.e. row) names.

Examples

sce <- mockDoubletSCE()
selFeatures(sce, clusters=sce$cluster, nfeatures=5)
#> [1] "gene13"  "gene149" "gene70"  "gene84"  "gene171"