BASiCS_Sim {BASiCS} | R Documentation

## Generates synthetic data according to the model implemented in BASiCS

### Description

`BASiCS_Sim` creates a simulated dataset from the model implemented in BASiCS.

### Usage

    BASiCS_Sim(Mu, Mu_spikes, Delta, Phi, S, Theta)

### Arguments

`Mu` | Gene-specific mean expression parameters μ_i for all biological genes (vector of length `q.bio`, all elements must be positive numbers)
`Mu_spikes` | μ_i for all technical genes defined as true input molecules (vector of length `q-q.bio`, all elements must be positive numbers)
`Delta` | Gene-specific biological over-dispersion parameters δ_i, biological genes only (vector of length `q.bio`, all elements must be positive numbers)
`Phi` | Cell-specific mRNA content normalising parameters φ_j (vector of length `n`, all elements must be positive numbers and the sum of its elements must be equal to `n`)
`S` | Cell-specific technical normalising parameters s_j (vector of length `n`, all elements must be positive numbers)
`Theta` | Technical variability parameter θ (must be positive)

### Value

An object of class `SingleCellExperiment`, including synthetic data generated by the model implemented in BASiCS.

### Author(s)

Catalina A. Vallejos cnvallej@uc.cl, Nils Eling

### References

Vallejos, Marioni and Richardson (2015). PLoS Computational Biology.

### Examples

    # Simulated parameter values for 10 genes
    # (7 biogical and 3 spike-in) measured in 5 cells
    Mu <- c(8.36, 10.65, 4.88, 6.29, 21.72, 12.93, 30.19)
    Mu_spike <-  c(1010.72, 7.90, 31.59)
    Delta <- c(1.29, 0.88, 1.51, 1.49, 0.54, 0.40, 0.85)
    Phi <- c(1.00, 1.06, 1.09, 1.05, 0.80)
    S <- c(0.38, 0.40, 0.38, 0.39, 0.34)
    Theta <- 0.39

    Data <- BASiCS_Sim(Mu, Mu_spike, Delta, Phi, S, Theta)
    head(assay(Data))
    dim(assay(Data))
    metadata(Data)$SpikeInput
    isSpike(Data)

[Package BASiCS version 1.0.1 Index]