View source: R/scmet_simulate.R

scmet_simulate | R Documentation |

General function for simulating datasets with diverse proprties. This for instance include, adding covariates X that explain differences in mean methylation levels. Or also defining the trend for the mean - overdispersion relationship.

scmet_simulate( N_feat = 100, N_cells = 50, N_cpgs = 15, L = 4, X = NULL, w_mu = c(-0.5, -1.5), s_mu = 1, w_gamma = NULL, s_gamma = 0.3, rbf_c = 1, cells_range = c(0.4, 0.8), cpgs_range = c(0.4, 0.8) )

`N_feat` |
Total number of features (genomics regions). |

`N_cells` |
Maximum number of cells. |

`N_cpgs` |
Maximum number of CpGs per cell and feature. |

`L` |
Total number of radial basis functions (RBFs) to fit the mean-overdispersion trend. For L = 1, this reduces to a model that does not correct for the mean-overdispersion relationship. |

`X` |
Covariates which might explain variability in mean (methylation). If X = NULL, a 2-dim matrix will be generated, first column containing intercept term (all values = 1), and second colunn random generated covariates. |

`w_mu` |
Regression coefficients for covariates X. Should match number of columns of X. |

`s_mu` |
Standard deviation for mean parameter |

`w_gamma` |
Regression coefficients of the basis functions. Should match the value of L. If NULL, random coefficients will be generated. |

`s_gamma` |
Standard deviation of dispersion parameter |

`rbf_c` |
Scale parameter for empirically computing the variance of the RBFs. |

`cells_range` |
Range (betwen 0 and 1) to randomly (sub)sample the number of cells per feature. |

`cpgs_range` |
Range (betwen 0 and 1) to randomly (sub)sample the number of CpGs per cell and feature. |

A simulated dataset and additional information for reproducibility purposes.

sim <- scmet_simulate(N_feat = 150, N_cells = 50, N_cpgs = 15, L = 4)

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