Approximate or randomized singular value decomposition

Source: R/dimension_reduction.R

Approximate SVD is performed using the implementation from irlba, randomized SVD is performed using the implementation from rsvd.

randomized_svd(object, exprs_values, n_dims, features = NULL,
  skip = NULL, seed = NULL, ...)

approximate_svd(object, exprs_values, n_dims, features = NULL,
  skip = NULL, seed = NULL, ...)

Arguments

object

A SingleCellExperiment object.
exprs_values

String indicating which assay contains the data that should be used to perform SVD.
n_dims

The number of approximate singular values to calculate.
features

A character vector (of feature names), a logical vector or numeric vector (of indices) specifying the features to use for SVD. The default of NULL will use all features.
skip

A numeric vector indicating which singular values to set to zero (and remove).
seed

A numeric seed to initialize the random number generator.
...

Additional arguments passed on to rsvd or irlba.

Value

A matrix of dimension ncol(object) x dims(object) - length(skip).