Estimate BASiCS simulation parameters

Estimate simulation parameters for the BASiCS simulation from a real dataset.

BASiCSEstimate(
  counts,
  spike.info = NULL,
  batch = NULL,
  n = 20000,
  thin = 10,
  burn = 5000,
  regression = TRUE,
  params = newBASiCSParams(),
  verbose = TRUE,
  progress = TRUE,
  ...
)

# S3 method for SingleCellExperiment
BASiCSEstimate(
  counts,
  spike.info = NULL,
  batch = NULL,
  n = 20000,
  thin = 10,
  burn = 5000,
  regression = TRUE,
  params = newBASiCSParams(),
  verbose = TRUE,
  progress = TRUE,
  ...
)

# S3 method for matrix
BASiCSEstimate(
  counts,
  spike.info = NULL,
  batch = NULL,
  n = 20000,
  thin = 10,
  burn = 5000,
  regression = TRUE,
  params = newBASiCSParams(),
  verbose = TRUE,
  progress = TRUE,
  ...
)

Arguments

counts	either a counts matrix or a SingleCellExperiment object containing count data to estimate parameters from.
spike.info	data.frame describing spike-ins with two columns: "Name" giving the names of the spike-in features (must match rownames(counts)) and "Input" giving the number of input molecules.
batch	vector giving the batch that each cell belongs to.
n	total number of MCMC iterations. Must be >= max(4, thin) and a multiple of thin.
thin	thining period for the MCMC sampler. Must be >= 2.
burn	burn-in period for the MCMC sampler. Must be in the range 1 <= burn < n and a multiple of thin.
regression	logical. Whether to use regression to identify over-dispersion. See BASiCS_MCMC for details.
params	BASiCSParams object to store estimated values in.
verbose	logical. Whether to print progress messages.
progress	logical. Whether to print additional BASiCS progress messages.
...	Optional parameters passed to BASiCS_MCMC.

Value

BASiCSParams object containing the estimated parameters.

Details

This function is just a wrapper around BASiCS_MCMC that takes the output and converts it to a BASiCSParams object. Either a set of spike-ins or batch information (or both) must be supplied. If only batch information is provided there must be at least two batches. See BASiCS_MCMC for details.

Examples

if (FALSE) {
# Load example data
library(scater)
set.seed(1)
sce <- mockSCE()

spike.info <- data.frame(Name = rownames(sce)[1:10],
                         Input = rnorm(10, 500, 200),
                         stringsAsFactors = FALSE)
params <- BASiCSEstimate(sce[1:100, 1:30], spike.info)
params
}