Estimate simulation parameters for the Splat simulation from a real dataset. See the individual estimation functions for more details on how this is done.
Usage
splatEstimate(counts, params = newSplatParams())
# S3 method for class 'SingleCellExperiment'
splatEstimate(counts, params = newSplatParams())
# S3 method for class 'matrix'
splatEstimate(counts, params = newSplatParams())
Examples
# Load example data
library(scuttle)
set.seed(1)
sce <- mockSCE()
params <- splatEstimate(sce)
#> NOTE: Library sizes have been found to be normally distributed instead of log-normal. You may want to check this is correct.
params
#> A Params object of class SplatParams
#> 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
#>
#> 29 additional parameters
#>
#> Batches:
#> [BATCHES] [BATCH CELLS] [Location] [Scale] [Remove]
#> 1 200 0.1 0.1 FALSE
#>
#> Mean:
#> (RATE) (SHAPE)
#> 0.00296241290380408 0.496975675076379
#>
#> Library size:
#> (LOCATION) (SCALE) (NORM)
#> 357331.235 11607.2332293176 TRUE
#>
#> Exprs outliers:
#> (PROBABILITY) (Location) (Scale)
#> 0 4 0.5
#>
#> Groups:
#> [Groups] [Group Probs]
#> 1 1
#>
#> Diff expr:
#> [Probability] [Down Prob] [Location] [Scale]
#> 0.1 0.5 0.1 0.4
#>
#> BCV:
#> (COMMON DISP) (DOF)
#> 0.752043426792851 11211.8933424086
#>
#> Dropout:
#> [Type] (MIDPOINT) (SHAPE)
#> none 2.71153553674717 -1.37209281901341
#>
#> Paths:
#> [From] [Steps] [Skew] [Non-linear] [Sigma Factor]
#> 0 100 0.5 0.1 0.8
#>