SPARSim_simulation: Simulate Datasets by SPARSim
| SPARSim_simulation | R Documentation |
Simulate Datasets by SPARSim
Description
This function is used to simulate datasets from learned parameters by SPARSim_simulation
function in SPARSim package.
Usage
SPARSim_simulation(
parameters,
other_prior = NULL,
return_format,
verbose = FALSE,
seed
)
Arguments
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 addtion to simulate datasets with default parameters, users want to simulate other kinds of datasets, e.g. a counts matrix with 2 or more cell groups. In SPARSim, you can set extra parameters to simulate datasets.
The customed parameters you can set are below:
de.prob. You can directly set
other_prior = list(de.prob = 0.2)to simulate DEGs that account for 20 percent of all genes.fc.group. You can directly set
other_prior = list(fc.group = 3)to specify the fold change of DEGs.batch.condition. You can input the batch vector that each cell belongs to and set
other_prior = list(batch.condition = xxxx). This parameter also determine the number of batches.
If users want to simulate groups, they should estimate group parameters by inputting group.condition parameter previously. Otherwise, thay can not simulate groups.
For more customed parameters in SPARSim, please check SPARSim::SPARSim_simulation().
References
Baruzzo G, Patuzzi I, Di Camillo B. SPARSim single cell: a count data simulator for scRNA-seq data. Bioinformatics, 2020, 36(5): 1468-1475. https://doi.org/10.1093/bioinformatics/btz752
Gitlab URL: https://gitlab.com/sysbiobig/sparsim
Examples
## Not run:
ref_data <- simmethods::data
## Estimation
group_condition <- as.numeric(simmethods::group_condition)
estimate_result <- simmethods::SPARSim_estimation(
ref_data = ref_data,
other_prior = list(group.condition = group_condition),
verbose = TRUE,
seed = 111
)
## 1) Simulation (20% proportion of DEGs, fold change 3)
simulate_result <- simmethods::SPARSim_simulation(
parameters = estimate_result[["estimate_result"]],
other_prior = list(de.prob = 0.2,
fc.group = 3),
return_format = "list",
verbose = TRUE,
seed = 111
)
## counts
counts <- simulate_result[["simulate_result"]][["count_data"]]
dim(counts)
## cell information
col_data <- simulate_result[["simulate_result"]][["col_meta"]]
table(col_data$group)
## gene information
row_data <- simulate_result[["simulate_result"]][["row_meta"]]
table(row_data$de_gene)[2]/4000
## 2) In SPARSim, users can simulate batches when batch.condition parameter is available
simulate_result <- simmethods::SPARSim_simulation(
parameters = estimate_result[["estimate_result"]],
other_prior = list(de.prob = 0.2,
fc.group = 3,
batch.condition = sample(1:3, 160, replace = TRUE)),
return_format = "list",
verbose = TRUE,
seed = 111
)
## counts
counts <- simulate_result[["simulate_result"]][["count_data"]]
dim(counts)
## cell information
col_data <- simulate_result[["simulate_result"]][["col_meta"]]
table(col_data$group)
table(col_data$batch)
## 3) Users can also utilize spike-in genes to simulate datasets. In this case, users
## must input dilution.factor and volume (nanoliter) parameters. Note that the
## reference matrix must contain spike-in gene counts.
ref_data <- simmethods::data
group_condition <- as.numeric(simmethods::group_condition)
estimate_result <- simmethods::SPARSim_estimation(
ref_data = ref_data,
other_prior = list(group.condition = group_condition,
dilution.factor = 50000,
volume = 0.01),
verbose = TRUE,
seed = 111
)
## check spike-in parameters
spikein_params <- estimate_result[["estimate_result"]][["SPARSim_spikein_parameter"]]
## simulate
simulate_result <- simmethods::SPARSim_simulation(
parameters = estimate_result[["estimate_result"]],
other_prior = list(de.prob = 0.2,
fc.group = 3),
return_format = "list",
verbose = TRUE,
seed = 111
)
## End(Not run)
