Return a list of features with the strongest contribution to a set of components

TopFeatures(
  object,
  dim = 1,
  nfeatures = 20,
  projected = FALSE,
  balanced = FALSE,
  ...
)

Arguments

object

DimReduc object

dim

Dimension to use

nfeatures

Number of features to return

projected

Use the projected feature loadings

balanced

Return an equal number of features with both + and - scores.

...

Extra parameters passed to Loadings

Value

Returns a vector of features

Examples

data("pbmc_small") pbmc_small
#> An object of class Seurat #> 230 features across 80 samples within 1 assay #> Active assay: RNA (230 features, 20 variable features) #> 2 dimensional reductions calculated: pca, tsne
TopFeatures(object = pbmc_small[["pca"]], dim = 1)
#> [1] "HLA-DPB1" "HLA-DQA1" "S100A9" "S100A8" #> [5] "GNLY" "RP11-290F20.3" "CD1C" "AKR1C3" #> [9] "IGLL5" "VDAC3" "PARVB" "RUFY1" #> [13] "PGRMC1" "MYL9" "TREML1" "CA2" #> [17] "TUBB1" "PPBP" "PF4" "SDPR"
# After projection: TopFeatures(object = pbmc_small[["pca"]], dim = 1, projected = TRUE)
#> [1] "HLA-DPB1" "HLA-DQA1" "S100A9" "S100A8" #> [5] "GNLY" "RP11-290F20.3" "CD1C" "AKR1C3" #> [9] "IGLL5" "VDAC3" "PARVB" "RUFY1" #> [13] "PGRMC1" "MYL9" "TREML1" "CA2" #> [17] "TUBB1" "PPBP" "PF4" "SDPR"