Estimate Kersplat simulation parameters

Estimate simulation parameters for the Kersplat simulation from a real dataset. See the individual estimation functions for more details on how this is done.

Usage

kersplatEstimate(counts, params = newKersplatParams(), verbose = TRUE)

# S3 method for class 'SingleCellExperiment'
kersplatEstimate(counts, params = newKersplatParams(), verbose = TRUE)

# S3 method for class 'matrix'
kersplatEstimate(counts, params = newKersplatParams(), verbose = TRUE)

Arguments

counts

either a counts matrix or a SingleCellExperiment object containing count data to estimate parameters from.

params

KersplatParams object to store estimated values in.

verbose

logical. Whether to print progress messages.

Value

KersplatParams object containing the estimated parameters.

See also

kersplatEstMean, kersplatEstBCV, kersplatEstLib

Examples

if (requireNamespace("igraph", quietly = TRUE)) {
    # Load example data
    library(scuttle)
    set.seed(1)
    sce <- mockSCE()

    params <- kersplatEstimate(sce)
    params
}
#> Warning: The Kersplat simulation is still experimental and may produce unreliable results. Please try it and report any issues to https://github.com/Oshlack/splatter/issues. The development version may have improved features.
#> Estimating mean parameters...
#> Selected MGE (CvM) fit
#> Estimating expression outlier parameters...
#> Estimating BCV parameters...
#> Raw: 2.6081737071714 A: 8.12109522965305 B: 1.31905398252679 C: -8.46037793603958 Y: 1.36293828975133
#> Estimating library size parameters...
#> Selected MGE (CvM) fit
#> A Params object of class KersplatParams 
#> Parameters can be (estimable) or [not estimable], 'Default' or  'NOT DEFAULT' 
#> Secondary parameters are usually set during simulation
#> 
#> Global: 
#> (GENES)  (CELLS)   [SEED] 
#>    2000      200   787110 
#> 
#> 24 additional parameters 
#> 
#> Mean: 
#>                  (RATE)                  (SHAPE)               (OUT PROB) 
#>     0.00272353361858179        0.475099389599897                        0 
#>          (Out Location)              (Out Scale)                (DENSITY) 
#>                       4                      0.5  Object of class density 
#>                [Method]                 [Values] 
#>                     fit                  Not set 
#> 
#> BCV: 
#>    (COMMON DISP)             [DoF] 
#> 1.11818825375479                60 
#> 
#> Network: 
#>    [Graph]     [nRegs]   [regsSet] 
#>    Not set         100       FALSE 
#> 
#> Paths: 
#> [nPrograms]      [Means] 
#>          10      Not set 
#> 
#> [Design]
#> data.frame (1 x 3) with columns: Path, From, Steps 
#>   Path From Steps
#> 1    1    0   100
#> 
#> Library size: 
#>              (LOCATION)                  (SCALE)                (DENSITY) 
#>        12.7854772402738       0.0327578186101294  Object of class density 
#>                [Method] 
#>                     fit 
#> 
#> Cells: 
#> 
#> [Design]
#> data.frame (1 x 4) with columns: Path, Probability, Alpha, Beta 
#>   Path Probability Alpha Beta
#> 1    1           1     1    0
#> 
#> Doublets: 
#> [Prop] 
#>      0 
#> 
#> Ambient: 
#> [Scale]  [Empty] 
#>    0.05        0 
#>