Description Usage Arguments Details Value Examples
View source: R/zinb-estimate.R
Estimate simulation parameters for the ZINB-WaVE simulation from a real dataset.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | 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 class '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 class '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 initialization. |
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 |
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.
ZINBParams object containing the estimated parameters.
1 2 3 4 5 6 7 8 | if (requireNamespace("zinbwave", quietly = TRUE)) {
library(scater)
set.seed(1)
sce <- mockSCE(ncells = 20, ngenes = 100)
params <- zinbEstimate(sce)
params
}
|
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