Simulate counts using the ZINB-WaVE method.
Usage
zinbSimulate(params = newZINBParams(), sparsify = TRUE, verbose = TRUE, ...)Arguments
- params
ZINBParams object containing 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
This function is just a wrapper around zinbSim that
takes a ZINBParams, runs the simulation then converts the
output to a SingleCellExperiment object.
See zinbSim and the ZINB-WaVE paper for
more details about how the simulation works.
References
Campbell K, Yau C. Uncovering genomic trajectories with heterogeneous genetic and environmental backgrounds across single-cells and populations. bioRxiv (2017).
Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J-P. ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data bioRxiv (2017).
Paper: 10.1101/125112
Examples
if (requireNamespace("zinbwave", quietly = TRUE)) {
sim <- zinbSimulate()
}
#> Simulating counts...
#> Creating final dataset...
#> Sparsifying assays...
#> Automatically converting to sparse matrices, threshold = 0.95
#> Converting 'counts' to sparse matrix: estimated sparse size 0.38 * dense matrix
#> Converting 'TrueCounts' to sparse matrix: estimated sparse size 0.75 * dense matrix
#> Skipping 'Dropouts': estimated sparse size 1.47 * dense matrix