Find features with highest scores for a given dimensional reduction technique

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

Usage

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
TopFeatures(object = pbmc_small[["pca"]], dim = 1)
# After projection:
TopFeatures(object = pbmc_small[["pca"]], dim = 1,  projected = TRUE)