Lun_simulation: Simulate Datasets by Lun

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

Simulate Datasets by Lun

Description

This function is used to simulate datasets from learned parameters by lunSimulate function in Splatter package.

Usage

Lun_simulation(
  parameters,
  other_prior = NULL,
  return_format,
  verbose = FALSE,
  seed
)

Arguments

parameters	A object generated by splatter::lunEstimate()
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 Lun, you can set extra parameters to simulate datasets.

The customed parameters you can set are below:

1. nCells. In Lun, you can not set nCells directly and should set groupCells instead. For example, if you want to simulate 1000 cells, you can type other_prior = list(groupCells = 1000). If you type other_prior = list(groupCells = c(500, 500)), the simulated data will have two groups

2. nGenes. You can directly set other_prior = list(nGenes = 5000) to simulate 5000 genes.

3. nGroups. You can not directly set other_prior = list(nGroups = 3) to simulate 3 groups. Instead, you should set other_prior = list(prob.group = c(0.2, 0.3, 0.5)) where the sum of group probabilities must equal to 1.

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. 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.

6. fc.up.group. You can directly set other_prior = list(fc.up.group = 2) to specify the foldchange of up-regulated DEGs.

7. fc.down.group. You can directly set other_prior = list(fc.down.group = 0.5) to specify the foldchange of down-regulated DEGs.

For more customed parameters in Lun, please check splatter::LunParams().

References

Zappia L, Phipson B, Oshlack A. Splatter: simulation of single-cell RNA sequencing data. Genome biology, 2017, 18(1): 1-15. https://doi.org/10.1186/s13059-017-1305-0

Bioconductor URL: https://bioconductor.org/packages/release/bioc/html/splatter.html

Github URL: https://github.com/Oshlack/splatter

Examples

## Not run: 
# Load data
ref_data <- simmethods::data
# Estimate parameters
estimate_result <- simmethods::Lun_estimation(ref_data = ref_data,
                                              verbose = TRUE,
                                              seed = 10)

# (1) Simulate 500 cells (Since we can not set nCells directly, so we can set
# groupCells (a numeric vector)) and 2000 genes
simulate_result <- simmethods::Lun_simulation(parameters = estimate_result[["estimate_result"]],
                                              other_prior = list(groupCells = 500,
                                                                 nGenes = 2000),
                                              return_format = "list",
                                              verbose = TRUE,
                                              seed = 111)
count_data <- simulate_result[["simulate_result"]][["count_data"]]
dim(count_data)


# (2) Simulate one group
simulate_result <- simmethods::Lun_simulation(parameters = estimate_result[["estimate_result"]],
                                              other_prior = NULL,
                                              return_format = "list",
                                              verbose = TRUE,
                                              seed = 111)
count_data <- simulate_result[["simulate_result"]][["count_data"]]
dim(count_data)


# (3) Simulate two groups (de.prob = 0.2)
simulate_result <- simmethods::Lun_simulation(parameters = estimate_result[["estimate_result"]],
                                              other_prior = list(prob.group = c(0.4, 0.6),
                                                                 de.prob = 0.2),
                                              return_format = "list",
                                              verbose = TRUE,
                                              seed = 111)
count_data <- simulate_result[["simulate_result"]][["count_data"]]
dim(count_data)
## cell information
col_data <- simulate_result[["simulate_result"]][["col_meta"]]
table(col_data$group)
## gene information
row_data <- simulate_result[["simulate_result"]][["row_meta"]]
### The result of Lun contains the factors of different groups and uses can
### calculate the fold change by division. For example, the DEFactors of A gene
### in Group1 and Group2 are respectively 2 and 1, and the fold change of A gene
### is 2/1=2 or 1/2=0.5.
fc_group1_to_group2 <- row_data$DEFacGroup2/row_data$DEFacGroup1
table(fc_group1_to_group2 != 1)[2]/4000 ## de.prob = 0.2
### number of all DEGs
table(row_data$de_gene)[2]/4000 ## de.prob = 0.2


# (4) Simulate two groups (de.prob = 0.2, fc.up.group = 2, fc.down.group = 0.5)
simulate_result <- simmethods::Lun_simulation(parameters = estimate_result[["estimate_result"]],
                                              other_prior = list(prob.group = c(0.4, 0.6),
                                                                 de.prob = 0.2,
                                                                 fc.up.group = 2,
                                                                 fc.down.group = 0.5),
                                              return_format = "list",
                                              verbose = TRUE,
                                              seed = 111)
count_data <- simulate_result[["simulate_result"]][["count_data"]]
dim(count_data)
## cell information
col_data <- simulate_result[["simulate_result"]][["col_meta"]]
table(col_data$group)
## gene information
row_data <- simulate_result[["simulate_result"]][["row_meta"]]
### The result of Lun contains the factors of different groups and uses can
### calculate the fold change by division. For example, the DEFactors of A gene
### in Group1 and Group2 are respectively 2 and 1, and the fold change of A gene
### is 2/1=2 or 1/2=0.5.
fc_group1_to_group2 <- row_data$DEFacGroup2/row_data$DEFacGroup1
table(fc_group1_to_group2 != 1)[2]/4000 ## de.prob = 0.2
### number of all DEGs
table(row_data$de_gene)[2]/4000 ## de.prob = 0.2
### fc.up.group
max(row_data$DEFacGroup1)
### fc.down.group
min(row_data$DEFacGroup1)

## End(Not run)

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