Estimate scDD simulation parameters

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

scDDEstimate(
  counts,
  params = newSCDDParams(),
  verbose = TRUE,
  BPPARAM = SerialParam(),
  ...
)

# S3 method for matrix
scDDEstimate(
  counts,
  params = newSCDDParams(),
  verbose = TRUE,
  BPPARAM = SerialParam(),
  conditions,
  ...
)

# S3 method for SingleCellExperiment
scDDEstimate(
  counts,
  params = newSCDDParams(),
  verbose = TRUE,
  BPPARAM = SerialParam(),
  condition = "condition",
  ...
)

# S3 method for default
scDDEstimate(
  counts,
  params = newSCDDParams(),
  verbose = TRUE,
  BPPARAM = SerialParam(),
  condition,
  ...
)

Arguments

counts	either a counts matrix or a SingleCellExperiment object containing count data to estimate parameters from.
params	SCDDParams object to store estimated values in.
verbose	logical. Whether to show progress messages.
BPPARAM	A BiocParallelParam instance giving the parallel back-end to be used. Default is SerialParam which uses a single core.
...	further arguments passed to or from other methods.
conditions	Vector giving the condition that each cell belongs to. Conditions can be 1 or 2.
condition	String giving the column that represents biological group of interest.

Value

SCDDParams object containing the estimated parameters.

Details

This function applies preprocess to the counts then uses scDD to estimate the numbers of each gene type to simulate. The output is then converted to a SCDDParams object. See preprocess and scDD for details.

Examples

if (requireNamespace("scDD", quietly = TRUE)) {
    library(scater)
    set.seed(1)
    sce <- mockSCE(ncells = 20, ngenes = 100)

    colData(sce)$condition <- sample(1:2, ncol(sce), replace = TRUE)
    params <- scDDEstimate(sce, condition = "condition")
    params
}
#> Performing Median Normalization
#> Notice: 10 genes have less than 3 nonzero cells per condition.  Skipping these genes.
#> Setting up parallel back-end using FALSE cores
#> Clustering observed expression data for each gene
#> Notice: Number of permutations is set to zero; using 
#>             Kolmogorov-Smirnov to test for differences in distributions
#>             instead of the Bayes Factor permutation test
#> Classifying significant genes into patterns
#> A Params object of class SCDDParams 
#> 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 
#> 
#> 11 additional parameters 
#> 
#> Data: 
#> (SCdat) 
#> SingleCellExperiment with 95 features and 20 cells 
#> 
#> Genes: 
#> (NDE)  (NDP)  (NDM)  (NDP)  (NEE)  (NEP) 
#>     0      0      0      0     95      5 
#> 
#> Fold change: 
#> [SD Range]   [Mode FC] 
#>       1, 3     2, 3, 4 
#> 
#> Variance: 
#> [Inflation] 
#>        1, 1 
#> 
#> Condition: 
#> [Condition] 
#>   condition 
#>