Estimate simulation parameters for the ZINB-WaVE simulation from a real dataset.

zinbEstimate(counts, design.samples = NULL, design.genes = NULL,
  common.disp = TRUE, iter.init = 2, iter.opt = 25, stop.opt = 1e-04,
  params = newZINBParams(), verbose = TRUE, BPPARAM = SerialParam(), ...)

# S3 method for SingleCellExperiment
zinbEstimate(counts, design.samples = NULL,
  design.genes = NULL, common.disp = TRUE, iter.init = 2, iter.opt = 25,
  stop.opt = 1e-04, params = newZINBParams(), verbose = TRUE,
  BPPARAM = SerialParam(), ...)

# S3 method for matrix
zinbEstimate(counts, design.samples = NULL,
  design.genes = NULL, common.disp = TRUE, iter.init = 2, iter.opt = 25,
  stop.opt = 1e-04, params = newZINBParams(), verbose = TRUE,
  BPPARAM = SerialParam(), ...)

Arguments

counts

either a counts matrix or a SingleCellExperiment object containing count data to estimate parameters from.

design.samples

design matrix of sample-level covariates.

design.genes

design matrix of gene-level covariates.

common.disp

logical. Whether or not a single dispersion for all features is estimated.

iter.init

number of iterations to use for initalization.

iter.opt

number of iterations to use for optimization.

stop.opt

stopping criterion for optimization.

params

ZINBParams object to store estimated values in.

verbose

logical. Whether to print progress messages.

BPPARAM

A BiocParallelParam instance giving the parallel back-end to be used. Default is SerialParam which uses a single core.

...

additional arguments passes to zinbFit.

Value

ZINBParams object containing the estimated parameters.

Details

The function is a wrapper around zinbFit that takes the fitted model and inserts it into a ZINBParams object. See ZINBParams for more details on the parameters and zinbFit for details of the estimation procedure.

Examples

# NOT RUN {
data("sc_example_counts")
params <- zinbEstimate(sc_example_counts)
params
# }