MFA_simulation | R Documentation |
Simulate Datasets by MFA
MFA_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. |
#' @details
In MFA, users can only set nCells
and nGenes
to specify the number of cells and genes in the
simulated dataset. See Examples
for instructions.
For more customed parameters, see splatter::MFAParams()
and splatter::mfaSimulate()
.
Campbell K R, Yau C. Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers. Wellcome open research, 2017, 2. https://doi.org/10.12688/wellcomeopenres.11087.1
Github URL: https://github.com/kieranrcampbell/mfa
## Not run:
ref_data <- simmethods::data
## estimation
estimate_result <- simmethods::MFA_estimation(
ref_data = ref_data,
verbose = TRUE,
seed = 111
)
# 1) Simulate with default parameters
simulate_result <- simmethods::MFA_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) 5000 cells and 5000 genes
simulate_result <- simmethods::MFA_simulation(
parameters = estimate_result[["estimate_result"]],
other_prior = list(nCells = 5000,
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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