Simulate counts from cluster in two conditions using the SparseDC method.
Usage
sparseDCSimulate(
params = newSparseDCParams(),
sparsify = TRUE,
verbose = TRUE,
...
)Arguments
- params
SparseDCParams 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
sim_data that takes a
SparseDCParams, runs the simulation then converts the
output from log-expression to counts and returns a
SingleCellExperiment object. The original
simulated log-expression values are returned in the LogExprs assay.
See sim_data and the SparseDC 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).
Barron M, Zhang S, Li J. A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data. Nucleic Acids Research (2017).
Paper: 10.1093/nar/gkx1113
Examples
if (requireNamespace("SparseDC", quietly = TRUE)) {
sim <- sparseDCSimulate()
}
#> Simulating counts...
#> Creating final dataset...
#> Sparsifying assays...
#> Automatically converting to sparse matrices, threshold = 0.95
#> Converting 'counts' to sparse matrix: estimated sparse size 0.58 * dense matrix
#> Skipping 'LogExprs': estimated sparse size 1.5 * dense matrix