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scDD simulation

Source: R/scDD-simulate.R
scDDSimulate.Rd

Simulate counts using the scDD method.

Usage

scDDSimulate(
  params = newSCDDParams(),
  plots = FALSE,
  plot.file = NULL,
  sparsify = TRUE,
  verbose = TRUE,
  BPPARAM = SerialParam(),
  ...
)

Arguments

params

SCDDParams object containing simulation parameters.

plots

logical. whether to generate scDD fold change and validation plots.

plot.file

File path to save plots as PDF.

sparsify

logical. Whether to automatically convert assays to sparse matrices if there will be a size reduction.

verbose

logical. Whether to print progress messages

BPPARAM

A BiocParallelParam instance giving the parallel back-end to be used. Default is SerialParam which uses a single core.

...

any additional parameter settings to override what is provided in params.

Value

SingleCellExperiment containing simulated counts

Details

This function is just a wrapper around simulateSet that takes a SCDDParams, runs the simulation then converts the output to a SingleCellExperiment object. See simulateSet for more details about how the simulation works.

References

Korthauer KD, Chu L-F, Newton MA, Li Y, Thomson J, Stewart R, et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology (2016).

Paper: 10.1186/s13059-016-1077-y

Code: https://github.com/kdkorthauer/scDD

Examples

# \donttest{
sim <- scDDSimulate()
#> Simulating counts with scDD...
#> Setting up parallel back-end using 1 cores
#> Identifying a set of genes to simulate from...
#> Simulating DE fold changes...
#> Simulating individual genes...
#> Done! Simulated 250 DE, 250 DP, 250 DM, 250 DB, 5000 EE, and 4000 EP genes 
#> Creating final dataset...
#> Sparsifying assays...
#> Automatically converting to sparse matrices, threshold = 0.95
#> Skipping 'counts': estimated sparse size 1.36 * dense matrix
#> Done!
# }