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(), ...)
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 |
... | additional arguments passes to |
ZINBParams object containing the estimated parameters.
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.
# NOT RUN { # Load example data library(scater) data("sc_example_counts") params <- zinbEstimate(sc_example_counts) params # }