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.
References
Lun ATL, Bach K, Marioni JC. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biology (2016).
Examples
sim <- lunSimulate()
#> Getting parameters...
#> Simulating means...
#> Simulating cell means...
#> Simulating counts...
#> Creating final dataset...
#> Sparsifying assays...
#> Automatically converting to sparse matrices, threshold = 0.95
#> Converting 'counts' to sparse matrix: estimated sparse size 0.83 * dense matrix
#> Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix
#> Done!