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".
lunSimulate(params = newLunParams(), verbose = TRUE, ...)
| params | LunParams object containing Lun simulation parameters. |
|---|---|
| verbose | logical. Whether to print progress messages. |
| ... | any additional parameter settings to override what is provided in
|
SingleCellExperiment object containing the simulated counts and intermediate values.
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.
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
sim <- lunSimulate()#>#>#>#>#>#>