bg__MakeSimData {M3Drop} | R Documentation

## Make Simulated Data

### Description

Makes simulated data based on a negative binomial distribution inflated with zeros based on the Michaelis-Menten equation.

### Usage

    	bg__MakeSimData(dispersion_fun=bg__default_mean2disp, n_cells=300, dispersion_factor=1, base_means=10^rnorm(25000, 1, 1), K=10.3)
    	bg__MakeSimDE(dispersion_fun=bg__default_mean2disp, fold_change=10, frac_change=0.1, n_cells=300, sub_pop=0.5, dispersion_factor=1, base_means=10^rnorm(25000,1,1), K=10.3)
    	bg__MakeSimDVar(dispersion_fun=bg__default_mean2disp, fold_change=10, frac_change=0.1, n_cells=300, sub_pop=0.5, dispersion_factor=1, base_means=10^rnorm(25000,1,1), K=10.3)
    	bg__MakeSimHVar(dispersion_fun=bg__default_mean2disp, fold_change=10, frac_change=0.1, n_cells=300, dispersion_factor=1, base_means=10^rnorm(25000,1,1), K=10.3)

### Arguments

`dispersion_fun` | a function which takes mean experssion and returns the dispersion parameter of the negative binomial distribution.
`n_cells` | total number of cells (columns) in the simulated dataset.
`sub_pop` | proportion of cells with changed expression.
`frac_change` | proportion of genes with changed expression.
`fold_change` | fold change in dispersion or mean expression.
`dispersion_factor` | a factor that multiplies the calculated mean-specific dispersion for all genes.
`base_means` | a vector of background mean expression values.
`K` | K of the Michaelis-Menten function

### Details

Generates simulated single-cell gene expression data using a zero-inflated negative binomial distribution. A user-supplied function relates the dispersion parameter (1/size of the R parameterization of the negative binomial distribution). Zeros are added based on a Michaelis-Menten function.

Default values of base_means, K, and dispersion_fun were fit to the Buettner et al. 2015 data [1].

`bg__MakeSimData` generates simulated single-cell data for a single homogeneous population.

`bg__MakeSimDE` generates simulated single-cell data for two different populations where a proportion of genes have a `fold_change` difference in the mean for population "2".

`bg__MakeSimDVar` generates simulated single-cell data for two different populations where a proportion of genes have a `fold_change` difference in the dispersion for population "2".

`bg__MakeSimHVar` generates simulated single-cell data for a single homogeneous population where a proportion of genes have a `fold_change` increase in dispersion over the expectation given the mean expression of the gene.

### Value

`bg__MakeSimData` : a gene expression matrix where rows are genes, columns are cells. `bg__MakeSimDE`, `bg__MakeSimDVar`, `bg__MakeSimHVar` : a list of three named items: data : the gene expression matrix where rows are genes, columns are cells cell_labels : a vector of 1 or 2 indicating which cells are the unchanged ("1") or changed ("2") population. TP : a vector of row IDs of those genes that change (true positives).

### References

[1] Buettner et al. (2015) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature Biotechnology 33 : 155-160.

### Examples

    #  means = c(1,2,5,10,20,50,100,200,500,1000,2000,5000)
    #  population1 <- bg__MakeSimData(n_cells=10, base_means=means)
    #  population2 <- bg__MakeSimData(n_cells=10, base_means=means*2, dispersion_factor=0.5)
    #  sim_DE <- bg__MakeSimDE(n_cells=100, base_means=means)
    #  sim_DVar <- bg__MakeSimDVar(n_cells=100, sub_pop=0.25, base_means=means)
    #  sim_HVar <- bg__MakeSimHVar(base_means=means, fold_change=3)

[Package M3Drop version 1.20.0 Index]