lunSimulate {splatter} | R Documentation

## 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()

[Package splatter version 1.14.1 Index]