scDesign_simulation: Simulate Datasets by scDesign

In duohongrui/simmethods: A collection of 40+ simulation methods for singlce-cell RNA seqencing data

Simulate Datasets by scDesign

Description

This function is used to simulate datasets design_data function in scDesign package.

Usage

scDesign_simulation(
  ref_data,
  other_prior = NULL,
  return_format,
  verbose = FALSE,
  seed
)

Arguments

ref_data	A matrix for one dataset or a list of datasets with their own names. This is usually unused except for some methods, e.g. SCRIP, scDesign, zingeR.
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 Details below for more information.
return_format	A character. Alternatives choices: list, SingleCellExperiment, Seurat, h5ad. If you select h5ad, you will get a path where the .h5ad file saves to.
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 scDesign, you can set extra parameters to simulate datasets.

The customed parameters you can set are below:

1. nCells. In scDesign, you can set nCells directly other_prior = list(nCells = 1000) to simulate 1000 cells.

2. nGroups. You can directly set other_prior = list(nGroups = 3) to simulate 3 groups. But the cells will be assigned to these three groups equally if you do not set prob.group below.

3. prob.group. You can directly set other_prior = list(prob.group = c(0.2, 0.3, 0.5)) to assign three proportions of cell groups. Note that the number of groups always equals to the length of the vector.

4. de.prob. You can directly set other_prior = list(de.prob = 0.2) to simulate DEGs that account for 20 percent of all genes.

5. fc.group. You can directly set other_prior = list(fc.group = 2) to specify the fold change of DEGs. But note that, you would better set fc.group because scDesign dose not return the fold changes of DEGs in the result.

For more customed parameters in scDesign, please check scDesign::design_data().

References

Li W V, Li J J. A statistical simulator scDesign for rational scRNA-seq experimental design. Bioinformatics, 2019, 35(14): i41-i50. https://doi.org/10.1093/bioinformatics/btz321

Github URL: https://github.com/Vivianstats/scDesign

Examples

## Not run: 
ref_data <- simmethods::data

## Simulate datasets with default parameters
simulate_result <- scDesign_simulation(ref_data = ref_data,
                                       return_format = "list",
                                       verbose = TRUE,
                                       seed = 111)
counts <- simulate_result[["simulate_result"]][["count_data"]]
dim(counts)


## Simulate two groups with 20% proportion of DEGs and 2 fold change. Note that
## scDesign does not provide fold changes for genes so users would better set
## fc.group parameter in simulation function.
simulate_result <- scDesign_simulation(ref_data = ref_data,
                                       other_prior = list(nCells = 1000,
                                                          nGroups = 2,
                                                          de.prob = 0.2,
                                                          fc.group = 2),
                                       return_format = "list",
                                       verbose = TRUE,
                                       seed = 111)
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)/4000
table(row_data$up_down)


## Simulate three groups with 20% proportion of DEGs and 4 fold change. 20%, 40%
## and 40% of cells belong to Group1, Group2 and Group3, respectively.
simulate_result <- scDesign_simulation(ref_data = ref_data,
                                       other_prior = list(nCells = 1000,
                                                          nGroups = 3,
                                                          prob.group = c(0.2, 0.4, 0.4),
                                                          de.prob = 0.2,
                                                          fc.group = 4),
                                       return_format = "list",
                                       verbose = TRUE,
                                       seed = 111)
counts <- simulate_result[["simulate_result"]][["count_data"]]
dim(counts)
## cell information
col_data <- simulate_result[["simulate_result"]][["col_meta"]]
table(col_data$group)

## End(Not run)

duohongrui/simmethods documentation built on June 17, 2024, 10:49 a.m.