Run the initial cudacellr workflow

Usage

cudacell_workflow(
  counts,
  n_hvg = 2000L,
  n_components = 30L,
  k = 15L,
  device = c("auto", "cuda", "cpu"),
  batch_size = 256L,
  scale_factor = 10000,
  log1p = TRUE,
  min_mean = 0,
  scale. = TRUE
)

Arguments

counts: Feature-by-cell count matrix.
n_hvg: Number of variable features.
n_components: PCA components.
k: Neighbours per cell.
device: Computation device.
batch_size: Maximum query rows per exact kNN distance block.
scale_factor: Target library size used during normalization.
log1p: Whether to apply log1p() after library-size normalization.
min_mean: Minimum feature mean used during highly variable feature selection.
scale.: Whether to scale selected features before PCA.

Value

A list containing normalized counts, variable features, PCA, and kNN. Feature and cell identifiers are retained throughout all stages.

Examples

set.seed(3)
counts <- matrix(rpois(40 * 25, 2), 40, 25)
cudacell_workflow(
  counts, n_hvg = 15, n_components = 5, k = 5, device = "cpu"
)
#> <cudacell_workflow features=40 cells=25 hvg=15 components=5 k=5 pca_device=cpu compute=cpu>