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GPU-accelerated PCA for single cell

calculate_pca_gpu_sc.Rd

This function will run sparse, randomised SVD while running several of the large matrix multiplications on GPU for improved speed. This also means you will have to provide the necessary VRAM for your data set. This version only works on the "rna" modality.

Usage

calculate_pca_gpu_sc(
  object,
  no_pcs,
  pca_params = bixverse::params_sc_pca(),
  hvg = NULL,
  seed = 42L,
  .verbose = TRUE
)

Arguments

object

SingleCells class

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().

hvg

Optional integer. If you want to provide your own HVG genes. Otherwise, the function will default to what is found in bixverse::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

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