Estimate scDD simulation parameters — scDDEstimate • Splatter

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

Usage

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

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

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

# Default S3 method
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(scuttle)
    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 1 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   489515 
#> 
#> 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 
#>