Run PCA for single cell — calculate_pca

This function will run PCA (option of full SVD and randomised SVD for now) on the detected highly variable genes.

Usage

calculate_pca_sc(
  object,
  no_pcs,
  randomised_svd = TRUE,
  sparse_svd = FALSE,
  hvg = NULL,
  seed = 42L,
  .verbose = TRUE
)

object: SingleCells class.
no_pcs: Integer. Number of PCs to calculate.
randomised_svd: Boolean. Shall randomised SVD be used. Faster, but less precise.
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.
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. Controls verbosity and returns run times.

The function will add the PCA factors, loadings and singular values to the object cache in memory.