Estimate simulation parameters for the SparseDC simulation from a real dataset.
Usage
sparseDCEstimate(
counts,
conditions,
nclusters,
norm = TRUE,
params = newSparseDCParams()
)
# S3 method for class 'SingleCellExperiment'
sparseDCEstimate(
counts,
conditions,
nclusters,
norm = TRUE,
params = newSparseDCParams()
)
# S3 method for class 'matrix'
sparseDCEstimate(
counts,
conditions,
nclusters,
norm = TRUE,
params = newSparseDCParams()
)Arguments
- counts
either a counts matrix or an SingleCellExperiment object containing count data to estimate parameters from.
- conditions
numeric vector giving the condition each cell belongs to.
- nclusters
number of cluster present in the dataset.
- norm
logical, whether to library size normalise counts before estimation. Set this to FALSE if counts is already normalised.
- params
PhenoParams object to store estimated values in.
Details
The nGenes and nCells parameters are taken from the size of the
input data. The counts are preprocessed using
pre_proc_data and then parameters are estimated using
sparsedc_cluster using lambda values calculated using
lambda1_calculator and
lambda2_calculator.
See SparseDCParams for more details on the parameters.
Examples
if (requireNamespace("SparseDC", quietly = TRUE)) {
# Load example data
library(scuttle)
set.seed(1)
sce <- mockSCE(ncells = 20, ngenes = 100)
conditions <- sample(1:2, ncol(sce), replace = TRUE)
params <- sparseDCEstimate(sce, conditions, nclusters = 3)
params
}
#> A Params object of class SparseDCParams
#> Parameters can be (estimable) or [not estimable], 'Default' or 'NOT DEFAULT'
#> Secondary parameters are usually set during simulation
#>
#> Global:
#> (GENES) (CELLS) [SEED]
#> 100 10 489515
#>
#> 7 additional parameters
#>
#> Markers:
#> (NUMBER) (SHARED) [Same]
#> 1 1 FALSE
#>
#> Clusters:
#> (CONDITION 1) (CONDITION 2)
#> 2, 3 1, 2, 3
#>
#> Means:
#> [Lower] [Upper]
#> 1 2
#>