Estimate SparseDC simulation parameters

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

sparseDCEstimate(
  counts,
  conditions,
  nclusters,
  norm = TRUE,
  params = newSparseDCParams()
)

# S3 method for SingleCellExperiment
sparseDCEstimate(
  counts,
  conditions,
  nclusters,
  norm = TRUE,
  params = newSparseDCParams()
)

# S3 method for 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.

Value

SparseParams object containing the estimated parameters.

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(scater)
    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   121054 
#> 
#> 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 
#>