splatSimulate {splatter} | R Documentation

## Splat simulation

### Description

Simulate count data from a fictional single-cell RNA-seq experiment using the Splat method.

### Usage

    splatSimulate(
      params = newSplatParams(),
      method = c("single", "groups", "paths"),
      verbose = TRUE,
      ...
    )

    splatSimulateSingle(params = newSplatParams(), verbose = TRUE, ...)

    splatSimulateGroups(params = newSplatParams(), verbose = TRUE, ...)

    splatSimulatePaths(params = newSplatParams(), verbose = TRUE, ...)

### Arguments

`params` | SplatParams object containing parameters for the simulation. See `SplatParams` for details.
`method` | which simulation method to use. Options are "single" which produces a single population, "groups" which produces distinct groups (eg. cell types) or "paths" which selects cells from continuous trajectories (eg. differentiation processes).
`verbose` | logical. Whether to print progress messages.
`...` | any additional parameter settings to override what is provided in `params`.

### Details

Parameters can be set in a variety of ways. If no parameters are provided the default parameters are used. Any parameters in `params` can be overridden by supplying additional arguments through a call to `setParams`. This design allows the user flexibility in how they supply parameters and allows small adjustments without creating a new `SplatParams` object. See examples for a demonstration of how this can be used.

The simulation involves the following steps:

1. Set up simulation object

2. Simulate library sizes

3. Simulate gene means

4. Simulate groups/paths

5. Simulate BCV adjusted cell means

6. Simulate true counts

7. Simulate dropout

8. Create final dataset

The final output is a `SingleCellExperiment` object that contains the simulated counts but also the values for various intermediate steps. These are stored in the `colData` (for cell specific information), `rowData` (for gene specific information) or `assays` (for gene by cell matrices) slots. This additional information includes:

`colData`
  Cell

    Unique cell identifier.

  Group

    The group or path the cell belongs to.

  ExpLibSize

    The expected library size for that cell.

  Step (paths only)

    how far along the path each cell is.

`rowData`
  Gene

    Unique gene identifier.

  BaseGeneMean

    The base expression level for that gene.

  OutlierFactor

    Expression outlier factor for that gene. Values of 1 indicate the gene is not an expression outlier.

  GeneMean

    Expression level after applying outlier factors.

  BatchFac[Batch]

    The batch effects factor for each gene for a particular batch.

  DEFac[Group]

    The differential expression factor for each gene in a particular group. Values of 1 indicate the gene is not differentially expressed.

  SigmaFac[Path]

    Factor applied to genes that have non-linear changes in expression along a path.

`assays`
  BatchCellMeans

    The mean expression of genes in each cell after adding batch effects.

  BaseCellMeans

    The mean expression of genes in each cell after any differential expression and adjusted for expected library size.

  BCV

    The Biological Coefficient of Variation for each gene in each cell.

  CellMeans

    The mean expression level of genes in each cell adjusted for BCV.

  TrueCounts

    The simulated counts before dropout.

  Dropout

    Logical matrix showing which values have been dropped in which cells.

Values that have been added by Splatter are named using `UpperCamelCase` in order to differentiate them from the values added by analysis packages which typically use `underscore_naming`.

### Value

SingleCellExperiment object containing the simulated counts and intermediate values.

### References

Zappia L, Phipson B, Oshlack A. Splatter: simulation of single-cell RNA sequencing data. Genome Biology (2017).

Paper: 10.1186/s13059-017-1305-0

Code: https://github.com/Oshlack/splatter

### See Also

`splatSimLibSizes`, `splatSimGeneMeans`, `splatSimBatchEffects`, `splatSimBatchCellMeans`, `splatSimDE`, `splatSimCellMeans`, `splatSimBCVMeans`, `splatSimTrueCounts`, `splatSimDropout`

### Examples

    # Simulation with default parameters
    sim <- splatSimulate()
    ## Not run:
    # Simulation with different number of genes
    sim <- splatSimulate(nGenes = 1000)
    # Simulation with custom parameters
    params <- newSplatParams(nGenes = 100, mean.rate = 0.5)
    sim <- splatSimulate(params)
    # Simulation with adjusted custom parameters
    sim <- splatSimulate(params, mean.rate = 0.6, out.prob = 0.2)
    # Simulate groups
    sim <- splatSimulate(method = "groups")
    # Simulate paths
    sim <- splatSimulate(method = "paths")

    ## End(Not run)

[Package splatter version 1.11.4 Index]