Calculates sparse PCA for single cell

Helper function that will calculate sparse PCA without scaling the data. This has the advantage that you avoid creating a large dense matrix due to scaling; however, it has the disadvantage that the first PC will be heavily influenced by average expression. If random_svd is set to FALSE, Lanczos iterations will be used to solve the SVD; if random_svd is set to TRUE, the randomised version will be used with multiplication of the initial sparse matrix with a much smaller random dense matrix, avoiding holding a large dense matrix in memory.

Usage

rs_sc_pca_sparse(
  f_path_gene,
  no_pcs,
  random_svd,
  cell_indices,
  gene_indices,
  seed,
  verbose
)

Arguments

f_path_gene

String. Path to the counts_genes.bin file.

no_pcs

Integer. Number of PCs to calculate.

random_svd

Boolean. Shall randomised SVD be used.

cell_indices

Integer. The cell indices to use. (0-indexed!)

gene_indices

Integer. The gene indices to use. (0-indexed!)

seed

Integer. Random seed for the randomised SVD.

verbose

Boolean. Controls verbosity of the function.

Value

A list with with the following items

• scores - The samples projected on the PCA space (solved via sparse SVD).

• loadings - The loadings of the features for the PCA (solved via sparse SVD).

• singular_values - The singular values for the PCA (solved via sparse SVD).