zingeR_simulation: Simulate Datasets by zingeR

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

Simulate Datasets by zingeR

Description

This function is used to simulate datasets from learned parameters by NBsimSingleCell function in zingeR package.

Usage

zingeR_simulation(
  ref_data = NULL,
  parameters,
  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.
parameters	A object generated by zingeR::getDatasetZTNB()
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. Alternative 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 zingeR, you can set extra parameters to simulate datasets.

The customed parameters you can set are below:

1. nCells. In zingeR, You can directly set other_prior = list(nCells = 5000) to simulate 5000 cells. nCells must be larger than the reference data.

2. nGenes. You can directly set other_prior = list(nGenes = 5000) to simulate 5000 genes. nGenes must be larger than the reference data.

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

4. fc.group. You can directly set other_prior = list(fc.group = 2) to specify the fold change of DEGs.

For more customed parameters in zingeR, please check zingeR::NBsimSingleCell().

References

Github URL: https://github.com/statOmics/zingeR

Examples

## Not run: 
ref_data <- simmethods::data
group_condition <- as.numeric(simmethods::group_condition)

estimate_result <- simmethods::zingeR_estimation(
  ref_data = ref_data,
  other_prior = list(group.condition = group_condition),
  verbose = TRUE,
  seed = 111
)

## Default parameters
simulate_result <- simmethods::zingeR_simulation(
  ref_data = ref_data,
  other_prior = list(group.condition = group_condition),
  parameters = estimate_result[["estimate_result"]],
  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"]]
## gene information
row_data <- simulate_result[["simulate_result"]][["row_meta"]]
head(row_data)


## In zingeR, users can only set the number of cells and genes which is higher
## than the reference data. In addtion, the proportion of DEGs and fold change are
## able to be customed. Not that zingeR dose not return cell group information.
## Default parameters
simulate_result <- simmethods::zingeR_simulation(
  ref_data = ref_data,
  other_prior = list(group.condition = group_condition,
                     nCells = 1000,
                     nGenes = 5000,
                     de.prob = 0.2,
                     fc.group = 4),
  parameters = estimate_result[["estimate_result"]],
  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"]]
## gene information
row_data <- simulate_result[["simulate_result"]][["row_meta"]]
head(row_data)

## End(Not run)

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