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Pipeline step: PCA

step_pca_sc.Rd

Wraps calculate_pca_sc() as an ScStep.

Usage

step_pca_sc(
  no_pcs = 30L,
  pca_params = params_sc_pca(),
  sparse_svd = FALSE,
  hvg = NULL,
  seed = 42L,
  .verbose = TRUE
)

Arguments

no_pcs

Integer. Number of PCs to calculate.

pca_params

Named list. Controls the parameters to be used for the PCA calculation which is single cell-specific, see params_sc_pca()

sparse_svd

Boolean. Shall sparse solvers be used that do not do scaling. If set to yes, in the case of random_svd = FALSE, Lanczos iterations are used to solve the sparse SVD. With random_svd = TRUE, the sparse initial matrix is multiplied with the random matrix, yielding a much smaller dense matrix that does not increase the memory pressure massively. Not used for MetaCells.

hvg

Optional integer. If you want to provide your own HVG genes. Otherwise, the function will default to what is found in get_hvg(). Please provide 1-indexed genes here! If you provide these, the internal HVG will be overwritten.

seed

Integer. Controls reproducibility. Only relevant if randomised_svd = TRUE.

.verbose

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

Value

An ScStep.

Examples

# PCA restricted to whatever the HVG step selected
step_hvg_sc(hvg_no = 30L) %>>% step_pca_sc(no_pcs = 10L)
#> <ScPipeline> 2 steps
#>   1. hvg  hvg_no = 30L, hvg_params = <list>, streaming = NULL, .verbose = TRUE
#>   2. pca  no_pcs = 10L, pca_params = <list>, sparse_svd = FALSE, hvg = NULL, seed = 42L, .verbose = TRUE