lunSimulate: Lun simulation

In Oshlack/splatter: Simple Simulation of Single-cell RNA Sequencing Data

Lun simulation

Description

Simulate single-cell RNA-seq count data using the method described in Lun, Bach and Marioni "Pooling across cells to normalize single-cell RNA sequencing data with many zero counts".

Usage

lunSimulate(params = newLunParams(), sparsify = TRUE, verbose = TRUE, ...)

Arguments

params	LunParams object containing Lun simulation parameters.
sparsify	logical. Whether to automatically convert assays to sparse matrices if there will be a size reduction.
verbose	logical. Whether to print progress messages.
...	any additional parameter settings to override what is provided in params.

Details

The Lun simulation generates gene mean expression levels from a gamma distribution with shape = mean.shape and rate = mean.rate. Counts are then simulated from a negative binomial distribution with mu = means and size = 1 / bcv.common. In addition each cell is given a size factor (2 ^ rnorm(nCells, mean = 0, sd = 0.5)) and differential expression can be simulated with fixed fold changes.

See LunParams for details of the parameters.

Value

SingleCellExperiment object containing the simulated counts and intermediate values.

References

Lun ATL, Bach K, Marioni JC. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biology (2016).

Paper: dx.doi.org/10.1186/s13059-016-0947-7

Code: https://github.com/MarioniLab/Deconvolution2016

Examples

sim <- lunSimulate()

Oshlack/splatter documentation built on Dec. 10, 2024, 3:48 p.m.