lun2Simulate: Lun2 simulation

Description Usage Arguments Details Value References Examples

View source: R/lun2-simulate.R

Description

Simulate single-cell RNA-seq count data using the method described in Lun and Marioni "Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data".

Usage

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lun2Simulate(params = newLun2Params(), zinb = FALSE, verbose = TRUE,
  ...)

Arguments

params

Lun2Params object containing simulation parameters.

zinb

logical. Whether to use a zero-inflated model.

verbose

logical. Whether to print progress messages

...

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

Details

The Lun2 simulation uses a negative-binomial distribution where the means and dispersions have been sampled from a real dataset (using lun2Estimate). The other core feature of the Lun2 simulation is the addition of plate effects. Differential expression can be added between two groups of plates (an "ingroup" and all other plates). Library size factors are also applied and optionally a zero-inflated negative-binomial can be used.

If the number of genes to simulate differs from the number of provied gene parameters or the number of cells to simulate differs from the number of library sizes the relevant paramters will be sampled with a warning. This allows any number of genes or cells to be simulated regardless of the number in the dataset used in the estimation step but has the downside that some genes or cells may be simulated multiple times.

Value

SingleCellExperiment containing simulated counts.

References

Lun ATL, Marioni JC. Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data. Biostatistics (2017).

Paper: dx.doi.org/10.1093/biostatistics/kxw055

Code: https://github.com/MarioniLab/PlateEffects2016

Examples

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sim <- lun2Simulate()

Oshlack/splatter documentation built on Sept. 3, 2018, 5:38 a.m.