Estimate simulation parameters for the Lun2 simulation from a real dataset.
Usage
lun2Estimate(
counts,
plates,
params = newLun2Params(),
min.size = 200,
verbose = TRUE,
BPPARAM = SerialParam()
)
# S3 method for class 'SingleCellExperiment'
lun2Estimate(
counts,
plates,
params = newLun2Params(),
min.size = 200,
verbose = TRUE,
BPPARAM = SerialParam()
)
# S3 method for class 'matrix'
lun2Estimate(
counts,
plates,
params = newLun2Params(),
min.size = 200,
verbose = TRUE,
BPPARAM = SerialParam()
)
Arguments
- counts
either a counts matrix or a SingleCellExperiment object containing count data to estimate parameters from.
- plates
integer vector giving the plate that each cell originated from.
- params
Lun2Params object to store estimated values in.
- min.size
minimum size of clusters when identifying group of cells in the data.
- verbose
logical. Whether to show progress messages.
- BPPARAM
A
BiocParallelParam
instance giving the parallel back-end to be used. Default isSerialParam
which uses a single core.
Details
See Lun2Params
for more details on the parameters.
Examples
# \donttest{
# Load example data
library(scuttle)
set.seed(1)
sce <- mockSCE()
plates <- as.numeric(factor(colData(sce)$Mutation_Status))
params <- lun2Estimate(sce, plates, min.size = 20)
#> Estimating number of groups...
#> Computing normalisation factors...
#> Estimating dispersions...
#> Estimating gene means...
#> Estimating plate effects...
#> Estimating zero-inflated parameters...
#> Warning: NaNs produced
#> Warning: NaNs produced
params
#> A Params object of class Lun2Params
#> 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
#>
#> 11 additional parameters
#>
#> Genes:
#>
#> (PARAMS)
#> data.frame (2000 x 2) with columns: Mean, Disp
#> Mean Disp
#> 1 61.200373 2.391091
#> 2 3.915675 19.460132
#> 3 218.990476 0.998549
#> 4 6.268318 25.938766
#> # ... with 1996 more rows
#>
#> (ZI PARAMS)
#> data.frame (2000 x 3) with columns: Mean, Disp, Prop
#> Mean Disp Prop
#> 1 26.795530 4.629399 3.119853e-06
#> 2 2.376801 19.503446 7.215299e-05
#> 3 218.990476 0.998549 0.000000e+00
#> 4 3.194146 25.610243 4.154911e-04
#> # ... with 1996 more rows
#>
#> Plates:
#> [NUMBER] [Modifier] (VARIANCE)
#> 2 1 19.8391786539778
#>
#> Cells:
#> [PLATES] (LIBRARY SIZES)
#> 1, 2, 1, 1,... 344015, 334664, 367199, 349098,...
#> [Lib Size Mod]
#> 1
#>
#> Diff Expr:
#> [Genes] [Fold change]
#> 0 3
#>
# }