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

splatEstimate(counts, params = newSplatParams())

# S3 method for SingleCellExperiment
splatEstimate(counts,
  params = newSplatParams())

# S3 method for matrix
splatEstimate(counts, params = newSplatParams())

Arguments

counts

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

params

SplatParams object to store estimated values in.

Value

SplatParams object containing the estimated parameters.

See also

splatEstMean, splatEstLib, splatEstOutlier, splatEstBCV, splatEstDropout

Examples

# Load example data library(scater) data("sc_example_counts") params <- splatEstimate(sc_example_counts)
#> 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' #> #> Global: #> (GENES) (CELLS) [Seed] #> 2000 40 171612 #> #> 28 additional parameters #> #> Batches: #> [BATCHES] [BATCH CELLS] [Location] [Scale] #> 1 40 0.1 0.1 #> #> Mean: #> (RATE) (SHAPE) #> 0.0032639148887191 0.419304527025529 #> #> Library size: #> (LOCATION) (SCALE) (NORM) #> 388947.775 231618.775891602 TRUE #> #> Exprs outliers: #> (PROBABILITY) (LOCATION) (SCALE) #> 0.00623376623376623 4.22280669039478 0.422108697251885 #> #> 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) #> 3.21969047393022 14.7262017878812 #> #> Dropout: #> [Type] (MIDPOINT) (SHAPE) #> none 5.00420976858168 -0.652889139821156 #> #> Paths: #> [From] [Length] [Skew] [Non-linear] [Sigma Factor] #> 0 100 0.5 0.1 0.8 #>