Description Usage Arguments Details Value References Examples

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

1 2 3 4 | ```
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)
``` |

`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 |

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.

`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).

[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.

1 2 3 4 5 6 | ```
# 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)
``` |

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