SymSim_simulation | R Documentation |
Simulate Datasets by SymSim
SymSim_simulation(
parameters,
other_prior = NULL,
return_format,
verbose = FALSE,
seed
)
parameters |
A object generated by |
other_prior |
A list with names of certain parameters. Some methods need
extra parameters to execute the estimation step, so you must input them. In
simulation step, the number of cells, genes, groups, batches, the percent of
DEGs are usually customed, so before simulating a dataset you must point it out.
See |
return_format |
A character. Alternative choices: list, SingleCellExperiment,
Seurat, h5ad. If you select |
verbose |
Logical. Whether to return messages or not. |
seed |
A random seed. |
In SymSim, users can only set nCells
and nGenes
to specify the number of cells and genes in the
simulated dataset. See Examples
for instructions.
Zhang X, Xu C, Yosef N. Simulating multiple faceted variability in single cell RNA sequencing. Nature communications, 2019, 10(1): 1-16. https://doi.org/10.1038/s41467-019-10500-w
Github URL: https://github.com/YosefLab/SymSim
## Not run:
ref_data <- simmethods::data
## estimation with cell group information
group_condition <- paste0("Group", as.numeric(simmethods::group_condition))
estimate_result <- simmethods::SymSim_estimation(
ref_data = ref_data,
other_prior = list(group.condition = group_condition),
verbose = TRUE,
seed = 111
)
# 1) Simulate with default parameters
simulate_result <- simmethods::SymSim_simulation(
parameters = estimate_result[["estimate_result"]],
other_prior = NULL,
return_format = "list",
verbose = TRUE,
seed = 111
)
## counts
counts <- simulate_result[["simulate_result"]][["count_data"]]
dim(counts)
# 2) 2000 cells and 5000 genes
simulate_result <- simmethods::SymSim_simulation(
parameters = estimate_result[["estimate_result"]],
other_prior = list(nCells = 2000,
nGenes = 5000),
return_format = "list",
verbose = TRUE,
seed = 111
)
## counts
counts <- simulate_result[["simulate_result"]][["count_data"]]
dim(counts)
## End(Not run)
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